Predictive real-time data platform to boost e-commerce sales

E-commerce business needs to collect data from various sources, analyse them in real-time and gain insights to understand the visitors behaviour, patterns which will allow the company to serve the customers in contextual and better ways to boost the conversion rate. A real time data platform is need of the hour which can combine stream analytics with Graph and AI to enable predictive analysis for better personalisation for the users which significantly improves the sales by 2X or even more.

Real-time and predictive data-platform for boosting e-commerce sales by visitor analysis is need of the hour

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Some of the general facts (statistical) which relates to e-commerce sales are following

  • Based on survey reports, 45% of online shoppers are more likely to shop on a site that offers personalized content/recommendations
  • According to a report by Gartner, personalization helps in increasing the profits by 15%
  • Majority of the data (more than 60%) is not even captured and analyzed for visitor or customer analytics
  • Less than 20% data is captured in true real time, which diminishes the potential of relevant and contextual personalized engagement with the visitors and hence lead scoring as well

To boost sales, e-commerce is looking to answer some of the following questions in general

  • How to develop personalized real time engagement and messages, content
  • How to engage with the visitors and customers in 1-on-1 basis for higher CR
  • How to identify and leverage purchasing patterns
  • What the entire consumer cycle looks like
  • Ways to improve promotional initiatives
  • How to make the customer and the customer experience the focus of marketing strategies – better lead score and reasons for the score
  • How to identify your customers’ switches between each channel and connect their movements and interactions

The businesses typically seek to predict following for predictive analysis

  • Personalized content in 1-0n-1 manner for better next steps or conversion
  • Exactly which customers are likely to return, their LTVs
  • After what length of time they are likely to make the purchase
  • What other products these customers may also buy at that time
  • How often they will make repeat purchases of those refills

What are some common challenges e-commerce businesses face?

  • Understanding who is “Visitor” and “Potential Buyer”
  • Relationships between different entities and the context
  • Nurturing the existing prospects
  • Personalization
  • Calculating the Lifetime Value
  • Understanding the buyers’ behavior
  • Cart Abandonment
  • Customer Churn

So, how can e-commerce businesses tackle the above challenges?

Predictive Analytics encloses a variety of techniques from data mining, predictive modelling and machine learning to analyze current and historical data and make predictions about future events.

With Predictive analytics, e-commerce businesses can do following

  • Improve Lead scoring
  • Increase Customer retention rates
  • Provide personalized campaigns for each customer
  • Accurately predict and increase CLV
  • Utilize Behavioral Analytics to analyze buyers’ behavior
  • Reduce cart abandonment rates
  • Use Pattern recognition to take actions that prevent Customer Churn

Following are brief list of use case that can be enabled on BangDB

A. Real time visitor scoring for personalization and lead generation for higher conversion

  1. Predictive real time visitor behavior analysis and scoring for personalized offering / targeting for much improved conversion rate. The potential increase in CR or expected biz impact could be 2X or more if implemented and scaled well
  2. Faster, contextual and more relevant lead generation for higher conversion
  3. Personalized content, offerings, pricings, for visitors in 1 on 1 basis, leads to much deeper engagement and higher conversion
  4. Projecting much relevant and usable LTV for every single user/visitor that could lead to better decision making for personalization or targeting or offering
  5. Inventory prediction for different product/versions/offering for better operation optimization

B. Improve engagement

  1. Personalized interaction and engagement with the customers
  2. Shopper’s Next Best Action
  3. Recommendations about relevant products based on shopping and viewing behavior
  4. Tailored website experience

C. Better target promotions

Collate data from other sources (demographics, market size, response rates, geography etc.) and past campaigns to assess the potential success of next campaign. Throw right campaign to right users

D. Optimized pricing

Predictive pricing analytics looks a historical product pricing, customer interest, competitor pricing, inventory and margin targets to deliver optimal prices in real-time that deliver maximum profits. In Amazon’s marketplace, for example, sellers who use algorithmic pricing benefit from better visibility, sales and customer feedback.

E. Predictive inventory management

Being overstocked and out of stock has forever been a problem for retailers but predictive analytics allows for smarter inventory management. Sophisticated solutions can take into account existing promotions, markdowns and allocation between multiple stores to deliver accurate forecasts about demand and allow retailers to allocate the right products to the right place and allocate funds to the most desirable products with the greatest potential for profit.

F. Prompt interactive shopping

Interactive shopping aims for customer loyalty. Integration of an online logistics platform helps maintain end-to-end visibility of purchases and orders, and business intelligence software helps process customer transaction data. It also enables retailers to offer multiple delivery options and can prompt customers for additional purchases based on their buying patterns. Consistent customer service, coupled with technology, can greatly increase customer reliability.

Data mining software enables businesses to be more proactive and make knowledge-driven decisions by harnessing automated and prospective data. It helps retailers understand the needs and interests of their customers in real time. Further, it identifies customer keywords, which can be analyzed to identify potential areas of investment and cost-cutting.

Challenges and Gaps in the market


  1. Need to capture all kinds of data, across multiple channels, not just limited set of data
  2. Need to capture all data truly in seamless and real time manner
  3. Store different entities and their relationships in graph structure and allow rich queries
  4. Need to auto refresh and retrain scoring model for relevant and higher efficacy
  5. Need to scale for high speed, high volume of data across multiple levels/ channels
  6. Need to have full control over the deployment and data
  7. Need to have the ability to add and extend the solution in easy and speedy manner in different context or domains as required

Gaps with the existing systems in the market

  1. Majority of systems (GA, Omniture etc.) are able to ingest limited set of data. It’s virtually impossible to ingest other related data into the system for better scoring model. Also, with these systems, it’s difficult to extend the ingestion mechanism for custom set of data, coming from totally different data sources than just the clickstream. Therefore, there is a need for a system which can ingests heterogenous custom data along with typical CS data for better results and higher efficiency
  2. Most of the systems ingests data with latency not acceptable from the solution perspective. For ex; GA allows limited set of data ingestion in real time, majority of data come with high latency. Omniture also has latency which is not acceptable to certain scenarios for the use cases.  Therefore, there is a need for true real time data ingestion and processing system/platform
  3. All the systems come with pre-loaded model(s) which are trained outside the actual processing system. This is hugely limiting from the AutoML perspective where the models could be trained and improved as it ingests more and more data. Also, finding the efficacy of the model is limiting which may result to poor and non-relevant prediction. Therefore, there is a need to have AI system natively integrated with the analytic scoring platform
  4. As we wish to deploy the system for various locales, different verticals, websites or companies, it is imperative that the system scales well. The speed and volume of data coupled with model preparation, training, deployment etc. make it very difficult for such system to scale well. It takes many weeks and months just to prepare and integrate the system with the new set of data sources. Software deployments, library configurations, infrastructure provisioning, training and testing of models, versioning of models and other large files, all of these creates huge block in terms of scaling the system. Therefore, there is a need to have a platform which hides all these complexities and provides a simple mechanism to scale linearly for higher volume of data, or more num of websites, locales or simply for larger number of use cases as things move forward.
  5. Most of the system acts as black box allowing lesser control on deployment and access to data in larger sense. This results to brittle solutioning and faster development of use cases. Better access to
  6. Most of the systems in the market won’t have “stream”, “graph”, “ML” and “NoSQL” at the same place. Integration takes lots of time, resources and sometime not feasible at all
  7. Also, it provides huge restrictions in terms of dealing with ML since the models and their meta data are often abstracted. More often than not, we might need to upload pre-baked models or model creation logic or file to leverage existing code. Therefore, we need a system which allows us to have greater control of various processes along with ability to reuse and extent already existing knowledge and artifacts

BangDB’s offering

BangDB platform is designed and developed to address the above gaps and challenges.

  1. Captures all kinds of data for visitors
  2. Click stream, pixel data, tags, etc.
  3. Website specific data
  4. Any other data that may be useful/required
  5. Existing data
  6. Retailers’ data, external data
  7. Any other infrastructure or system data as required

Captures all data in real time

Captures all data in real time, as opposed to GS which captures only small fraction of data in real time. This limits the scoring efficacy as real time data is basis for proper analysis. Omniture captures most of the data, but they are available for analysis in few minutes rather in few milli seconds. Proper personalization or any action is best taken as soon as possible, not after few minutes or hours

Accelerated time to market

BangDB comes with a platform along with a ready solution which implement the use cases as needed and have majority of the plumbing in place. Further, it has the built in KPIs, models, actions, visualizations etc. which are ready from day 1. We need to just configure the system, add more data points, fix the API hooks etc., set the model training / retraining processes which is in contrast with many other systems where they may take several weeks or even months to just get started

Scales well across multiple dimensions, in simple manner

Several IPs for high performance and cost-effective method to deal with high volume of fast-moving data. The platform has built in IO layer for improved, flexible and faster data processing. Convergence allows system to scale linearly as required in uninterrupted manner

  • Integrated streaming system to ingest all kinds of data as required for analysis in real time. Build your own apps/solutions or extend the existing ones as needed with just using the UI and not doing coding etc.
  • Integrated machine learning system for faster, simpler and automated model training, testing, versioning and deployment

The platform comes with AI natively integrated, which allows us to get the models be trained and retrained on frequent basis as more and more data arrives. It starts producing output from the model with a week’s time and as it moves forward it keeps improving the model and its efficacy. It also measures its efficacy and tunes/retunes as needed for higher performance.

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Why Hybrid NoSQL Architecture is Indispensable for IoT

Before we talk about hybrid architecture, let’s go over what IoT actually describes in case the term feels kind of fuzzy for you like it does for most people. 

The Internet of Things refers to the way everything is becoming connected. From your smartphone to your home to your car and beyond, all technology is moving toward a place of constant connection and interaction. 

To keep up in business, you have to think about more than just the user experience on your website or app. How will a user interact with your business on their commute to work? What about when they’re on the treadmill, or when they are cooking dinner or washing dishes?

The IoT exists in a perpetual state of evolution, meaning new use cases and scenarios pop up every single day. In order for your business to stay on top of the latest technological developments, and to be part of this endless cycle of connection, your apps, platforms, and operations must all work seamlessly together, and that is where a hybrid NoSQL Architecture comes in.


What is a “Hybrid NoSQL Architecture”?

When it comes to database management, you generally have two options:

  • Relational Database (SQL)
  • Non-Relational Database (NoSQL)

Relational databases were the first to emerge and have been used across the past several decades to store and retrieve information, and to fuel various types of businesses. This type of database uses tables to maintain structured data in rows. Since it was designed before the IoT, the SQL database has struggled in many ways to maintain relevancy and is challenged in areas such as affordability, scalability, and flexibility.

Non-Relational databases were created to solve many of the shortcomings of relational databases. NoSQL databases are far more scalable and flexible, in addition to being much faster to the point of returning results from search queries in near real-time. NoSQL databases operate using less structured and unstructured data stores and are cloud-based to ensure they maintain 24/7 availability.

Hybrid architecture is a combination of different database models. Specifically, a hybrid architecture empowers you with the ability to work with SQL and NoSQL together within a single system. 

But why would you use a hybrid approach when NoSQL is better than SQL in virtually every possible way? Why do you need both?

Infrastructure Considerations

The most obvious reason for the necessity of a hybrid model is that many businesses have built their entire operation around relational database systems. 

In other words, it would be very difficult, extremely time-consuming, and cost-prohibitive to completely switch from one model to another. Yet, modern businesses must evolve with technology if they want to stay relevant.

Enter the hybrid architecture. 

Hybrid NoSQL architectures are capable of managing many SQL applications, which makes them somewhat backward compatible. The system allows businesses to implement the features of NoSQL without sacrificing their relational database infrastructure.

By using a hybrid approach, your business can enjoy the best of both worlds. You can continue your operations uninterrupted while giving yourself a boost with the power of real-time analytics, data, and performance afforded by adding NoSQL.

What Does This Look Like in Practice?

Hybrid databases take advantage of a multi-faceted approach to data storage and retrieval. By storing and returning data with physical disks, and by leveraging in-memory data for active performance enhancement, hybrid database systems can support multiple operations with improved speed and efficiency.

On-Disk Database

The core benefit of leveraging on-disk systems is that physical disks have enormous storage space that can hold loads of data beyond the in-memory capacity. The one pitfall is that retrieving data from a physical disk is a much slower process than pulling it from in-memory.

In-Memory Database

Unlike physical disks, memory-based storage can rapidly recall data for retrieval. Unfortunately, the storage capacity for in-memory is much less than what a physical disk can hold. For this reason, a hybrid system that leverages both can create a powerful in-between solution.

Looking for an innovative NoSQL solution?

Other Benefits of Hybrid Architecture

In addition to speed and storage capacity enhancements, hybrid architecture also offers businesses the following advantages:

  • Affordability: Physical disk storage costs much less than in-memory storage, which means you can increase your storage capacity anytime without eating into your bottom line.
  • Flexibility: A hybrid architecture gives you the ability to perform Hybrid Transactional and Analytical (HTAP) processing. This means you can simultaneously execute transactional and analytical processes without bogging down your database.
  • Multiple Data Stores: The biggest limitation of relational databases is found in the way they store and retrieve structured data using rows. With a hybrid database, you can manage your data in rows, columns, and other formats.
  • Resource Freedom: Since hybrid databases can be launched in the cloud, it means you can free up local resources. While you can still launch your database services locally, you don’t have to, and that gives you a lot of freedom when it comes to your in-house resources.

Why Is This Important for the IoT?

There are times when it doesn’t make sense to use a hybrid setup. Some businesses should stick with relational models while others should go all-in with non-relational models.

When your business is limited in size, and you don’t have plans to add apps or real-time features, and when constant database upkeep isn’t that important, then you might be able to save yourself time and money by using a SQL database. This is also true if you only deal with structured data and if your operations only involve minimal online interactions.

If your business is on a different trajectory – say you are on an exponential growth path, whether that is inventory, user management, or some other aspect, and if you will constantly be engaging with users and need the power of real-time analytics constantly, then a NoSQL-only database could be the right solution for you. 

A hybrid architecture comes in when you need the best of BOTH worlds. When you have offline operations or you have structured data or when you’ve built your entire business around a SQL database, but now you are ready to expand into the IoT to offer newer, faster apps, advertisements, inventory management, and personalization without losing everything you’ve built, then a hybrid database makes the most sense.


Making the Best Choice for Your Business

The best option always depends on your current and future business goals. How have you built yourself up so far? Where are you going in three years? Five years? Ten years?

If you think you will need a combination of SQL and NoSQL solutions, then a hybrid architecture will be the right choice for you. This is especially true if you’ve already been using SQL and you want to give yourself a solid foundation as the world of IoT continues to evolve.

However, if you only deal in structured, low-volume data, then you will save time and money by sticking with your trusty old relational system.

Finally, if you’re all-in on technology, real-time data, scalability, flexibility, and your plan is for exponential growth, then a cloud-based NoSQL database is the absolute best choice for you. 

To get the most comprehensive NoSQL solution in existence, start here with BangDB completely free, and give yourself the biggest IoT advantage right now.

Further Reading:

Use Cases and Scenarios Suitable for a NoSQL Database Technology

Wherever you are starting from, you have to consider which type of database technology makes the most sense for the needs of your business. 

The NoSQL database is a powerhouse of a management solution best suited for businesses working with a lot of unstructured data in huge volume and in real-time.

If you’re a small business with a low volume of structured data, and if you don’t need the ability to manipulate data in real-time, then you’ll find relational databases (or even just an excel spreadsheet) can be a better option, although not always.

To explore other types of databases, check out this article where we cover both spreadsheets and relational database solutions. Now, let’s dive into some of the different scenarios that might call for the nearly limitless power of a NoSQL database.

Use Case Number One: Scaling Up

Some businesses always remain small. Others grow to the moon and beyond. Imagine what Amazon was like when it started. They had a limited inventory because their operation was tiny. Over time, Amazon grew much larger.

Today, Amazon has warehouses all around the world. Their inventory is massive, and they operate with so many moving pieces and at such scale, that trying to accommodate wide-scale inventory adjustments using a relational system would be virtually impossible. Thankfully, Amazon is a smart business. The company is built on a cloud computing solution that scales with them as they grow.  

With NoSQL, businesses can easily manage inventory systems at scale regardless of how large and complex they become. 

But what if you don’t have inventory? Even if you only have a large user base, a challenge could arise as your business grows. If you operate a platform that manages many users on a daily basis, then over time, you will find you need a solution to easily call up specific user data quickly.

Managing hundreds of thousands or even millions of users with a relational database would take light-years. Relational systems have to read every entry line by line to find and return the requested data, so by the time the system finds the targeted dataset, users are usually long gone.

NoSQL on the other hand takes an entirely different approach by working with unstructured data so that queries run fast, and data can be retrieved quickly no matter how much data is contained within the system.

NOSQL Database

Use Case Number Two: Real-Time Data

Building from the scenario above, imagine if Amazon couldn’t provide order data to customers instantaneously. In a world where people demand instant results, using a relational database leads to an absolute catastrophe.

The good news is, non-relational systems operate with such speed and efficiency, that user data can be searched, retrieved, and returned to the end-user almost as fast as they can request it.

If you expect to provide information to consumers regularly, and if you plan to have a lot of people using your platform or service, then you absolutely must use some form of NoSQL to manage your data because the alternative leads to the downfall of your business.

Since there are different types of NoSQL databases, we’ve broken down the storage options to give you an idea of how each type works and when you might want to use one over another. Some are faster than others, so you’ll want to explore your options before choosing a service provider.

Returning user data instantly is one use case. Another scenario occurs when you think about the overarching consumer experience.

Looking for an innovative NoSQL solution?

Relational databases aren’t particularly useful for creating personalized experiences because they are too slow. When you want to offer personalized advertisements or other engaging and interactive platform elements, then you’re looking for a power found only within NoSQL.

Use Case Number Three: Affordability

Probably the most sensible reason why businesses turn to NoSQL is affordability. Just because you’re growing an enormous company doesn’t mean you have to settle for enormous costs. Unfortunately, that’s exactly what happens when you work with relational databases. 

Relational Databases Are Expensive

Some companies worry that upgrading their database will come with endless expenses. The truth is, outdated relational systems cost far more to manage than what you can save by migrating to NoSQL. This happens because relational databases weren’t designed for the cloud. They were built for a different time and to handle different needs.

It’s kind of like how you wouldn’t expect a computer from the early 90’s to play a modern PC game. Not only would the older computer not handle the game out of the box, but you would have to rebuild the old computer from the ground up to make it possible.

NoSQL Was Built for the Cloud

Non-Relational databases were created for the internet of things (IoT). Their design works within cloud computing systems which makes them extremely flexible, scalable, dependable, and therefore, affordable. 

When you upgrade to NoSQL, you are making the move to a modern solution that isn’t just designed to handle the challenges of today but also is adaptable for the needs of tomorrow. 

Instead of rebuilding from the ground up, your team can quickly institute solutions while minimizing costs, so even if you invest cash during the migration phase, you end up saving a lot more over time.

That said, if you were to use BangDB’s open source NoSQL technology, then you could start completely free, and you would save a lot more.

NoSQL Database

Is NoSQL Right for You?

What do you value in business and within your operations? If you need to move fast, provide users with an extremely reliable, engaging, or personalized experience, or if your business has large-scale operations or is likely to grow quickly, then NoSQL is probably the right choice for you. 

If you are a small business working with a limited amount of data that is mostly structured, and if you don’t need 24/7 availability or quick recall of information and datasets, then relational technology is still a viable option. 

However, even small businesses can benefit from NoSQL solutions when they want to bring the power of cloud computing into their services. For instance, if you wanted to offer an app for your customers, part of your business could remain on a relational system while your app is developed using a non-relational solution to maximize speed, minimize costs, and deliver the best possible user experience.

After considering your options, if you find that NoSQL is the right choice for you, then BangDB has a powerful, affordable solution with a range of storage options that extend beyond other providers. Click here to explore our NoSQL technology for your business completely free.

Further Reading:

The Difference Between SQL and NoSQL. Why Should I Use Both?

Learning the difference between SQL and NoSQL databases can guide you in choosing the best tools for your project. Each type of database has its benefits but using both SQL and NoSQL can have even greater benefits.

Learn the 4 big differences between SQL and NoSQL as well as instances where you might consider using both to power your technology for the best customer experience and software use.

4 Main Differences Between SQL and NoSQL

At their very core, SQL and NoSQL databases are different. That’s because SQL databases are relational databases, while NoSQL is non-relational. But the difference in architecture translates into 4 main differences between these types of databases.

1. Schemas and Query Languages

SQL databases are characterized by their structured query functions. These databases have a predefined schema that makes data manipulation simple. The ability to complete complex query functions is one reason why SQL is still popular despite its challenges in expanding capacity vertically. 

However, SQL is also somewhat restrictive in that you must decide your schemas and data structure at the onset of a project. You cannot work with your data until you’ve defined it. And all data must then conform to this framework. Working in SQL databases means doing a great deal of pre-work and realizing that changing your data structure could mean disruptions to your application or entire technology system.

In contrast, NoSQL databases are unstructured and you can store your data in a variety of ways, such as column, document, graph or key-value store. Flexibility in storing data leaves room for creating documents and defining their structure later or allowing each document to have its own structure. Syntaxes can vary from one database to another and you can add fields to your NoSQL database as you go.

2. Database Scalability

Database Scaling

SQL databases are challenging to scale. They only scale vertically, which means you have to increase the capacity or load on a server. You’ll need to add more SSD, CPU or RAM to scale your application.

In contrast, NoSQL databases scale horizontally through a process called sharding. That means that you can add new servers to your NoSQL database. That’s one reason why developers choose NoSQL over SQL is because they can scale as needed and deal with frequently changing data sets.

Some industry experts compare the scaling of SQL to adding more floors to a building. You have to build upward to get more space. In contrast, expanding NoSQL databases is more like adding new buildings to a neighborhood to acquire more space.

3. Data Structure

SQL databases use a table structure. In contrast, NoSQL databases can be document, graph, column or key-value based.

The added flexibility NoSQL offers is yet another reason why developers have started to prefer working with NoSQL over SQL. 

4. Consistency

SQL databases are well known for their consistency. ACID compliance was once only available in SQL databases. Today, some NoSQL databases like BangDB are ACID-compliant to offer a transactional database. 

While historically NoSQL provides less consistency, this is now more about choosing the right database for the job. Understanding what you need your database to do should be the first step in evaluating the best database for you. If you start there, you should have no trouble finding a NoSQL database that will be consistent to meet your needs.

Looking for an innovative NoSQL solution?

Pros and Cons of SQL Databases

SQL databases were the only database option for many years and served developer and data scientist needs well. But with the dawn of NoSQL, we’ve also started to recognize its weaknesses. Here are the pros and cons of SQL databases.


  • Flexible query capabilities to support diverse workloads
  • Reduced data storage footprint that maximizes database performance
  • Familiar language and infrastructure developers know including ACID-compliance and properties that developers are familiar with


  • Challenging to scale as needs change and grow
  • Opens up your application to a single point of failure since the database is not distributed across various servers
  • Data models are rigid and require pre-defined schema before starting a project

Pros and Cons of NoSQL Databases

While NoSQL is the new technology on the scene and meets the needs of big data, it still has its limitations. Learn about the pros and cons of NoSQL databases before deciding the best technology for your application.


  • Scalable horizontally and provides excellent availability
  • Data models are flexible, allowing you to capture all data your company produces and allows you to adjust data models as needed
  • Allows for unstructured data so that you don’t miss out on any data that your company produces so you can analyze and understand everything
  • Is high performing to offer your application speed and performance


  • ACID compliance is not available in all NoSQL databases
  • Distributing your data can be helpful, but it can also present some challenges and require expertise you may or may not have in-house 

When to Use SQL

Although NoSQL databases have risen to popularity over the last decade, there are still many use cases for SQL databases. Here’s a look at some instances where you might still consider a SQL database.

  • To build custom dashboards
  • When you need to use joins to execute complex queries
  • When you prefer to work with SQL code or your team is only familiar with SQL
  • You need to analyze behavioral data or custom session data
  • You need ACID compliance

When to Use NoSQL

NoSQL databases are great for transmitting large volumes of data. Here’s a look at when you should use NoSQL.

  • You’re dealing with a large volume of data that is unstructured, semi-structured or a mix of structured, unstructured and semi-structured
  • When you don’t need ACID compliance (or select your NoSQL database carefully to find a transactional option)
  • A traditional relational model does not meet your needs
  • Your data requires a flexible schema
  • You need to log data from different sources
  • The application does not require constraints or logic
  • You need a way to store temporary data, such as a wish list or shopping cart

When You Can Benefit from Both SQL and NoSQL

In some instances, you can use SQL and NoSQL together to gain the benefits of each. Additionally, some NoSQL databases allow for SQL-like queries to allow your development team to work in the language that is familiar to you. 

BangDB uses a Command Line Interface (CLI) to help developers interact with the database in an easy, efficient manner. You can complete nearly any task using the CLI and it accepts SQL-like language. For graph-related queries, BangDB also supports Cypher syntax. 

Adding NoSQL to an existing database can add capacity to a SQL database-based application or allow you to store additional data you aren’t currently logging. You can increase your server storage by adding a NoSQL database without having to remove your SQL database. 

BangDB helps bridge the gap between SQL and NoSQL databases to offer the benefits of each so you can get the most out of your application. Download BangDB now to see the flexibility and modern infrastructure it provides.

Further Reading:

The Evolution of Databases from SQL to NoSQL

The amount of data the world is producing grows day after day. And as it grows, technology experts are looking for ways to store and access this data to improve the customer experience and analyze activity. 

This growing data volume required changes in database options. Relational databases are expensive and challenging to scale since developers can only scale them horizontally. Yet, SQL has always been the go-to data structure up until about a decade ago.

Relational databases date back to 1970 when Edgar F. Codd introduced the concept of storing data in rows and columns with a specific key that showed the relationship between the data. The majority of relational databases used structured query language (SQL) but with time, they have become too rigid and restrictive to handle complex and unstructured data.

We’ll take a look at the evolution of databases and what’s fueling the need to move from SQL to NoSQL in the big data era.

Databases from SQL to NOSQL

Unstructured Data Boom

Until recently, all data fit perfectly into a relational SQL database because the data was all structured. But then came the unstructured data boom, which led to SQL databases being insufficient to meet the needs of many companies. 

The unstructured data boom began when access to the internet became commonplace. And with greater access to the internet, social media platforms began to spring up as users shared simple updates to keep their friends and network informed about their daily activities. 

According to research from 2021, 7 in every 10 Americans use social media. That’s up from just 5 percent in 2005 when Pew Research began tracking social media usage. With that ever-increasing demand for social media updates has come the need for storing and delivering unstructured data at incredibly rapid rates.

Not only is the need for storing unstructured data rising, but the data also includes various types, such as images, video, audio and text. These large files put enormous strains on limited storage capacities within SQL databases.

Before NoSQL joined the marketplace, IT professionals relied solely on relational database management systems (RDBMS) to handle storing all data. From website data to business application data, SQL databases were able to handle storage needs.

Why SQL Was Suited for Storing Structured Data

Relational databases were well-suited for storing all structured data because they are ACID compliant. ACID stands for:

  • Atomic: transactions are “all or nothing,” which means that if one part of the transaction fails, the entire transaction will fail. If the transaction does fail, the database state is unchanged. Relational databases guaranteed atomicity in every situation, which allowed developers to use the database for crucial transactions, such as banking.
  • Consistency: whether a transaction is successful or not, the database remains consistent. Before, during and after a transaction, developers could rely on the database to be consistent.
  • Isolation: data modification transactions are independent of one another.
  • Durability: once the system notifies a user that their transaction was successful, the action cannot and will not be undone. That transaction will persist within the database.

The consistent experience of working with SQL was one reason why developers enjoyed it. You could count on the following characteristics being present with any relational database.

  • Table format data storage
  • Data represents relationships
  • You can join tables using relational links
  • Normalization reduces duplicate data in the database
  • The databases are flexible and efficient

Discovering SQL Shortcomings

Although developers relied on SQL databases to store their data and power applications, they also recognized their shortcomings, which grew as the need for big data and unstructured data grew.

While SQL is great for storing structured data, it cannot store unstructured data. And because data needs are constantly changing, SQL databases are also challenging because developers must know and understand their data and create its structure before beginning a project. And if that data changes, it can require application downtime or large expenses to adapt the SQL database accordingly. 

Social media data has no structural boundary. But that’s just one example of schema-less data. With rising needs for creating, reading, updating and deleting (CRUD) all types of data, relational databases are becoming more challenging to use and more expensive to operate. Maintaining relationships between data has become a big job and in some cases, impossible. 

That’s what led technology specialists to look for a new solution. Great minds in technology, such as Google and Facebook, have worked to develop new databases that don’t require schema and data relationships to store and retrieve unstructured data.

Looking for an innovative NoSQL solution?

The Dawn of NoSQL

For nearly 30 years, SQL databases and other types of relational databases were the only option and met the needs of most developers. In 1998 Carlo Strozzi introduced the concept of NoSQL. But it took more than a decade for the concept to catch on.

We didn’t see much about NoSQL until 2009 when Eric Evans and Johan Oskarsson described non-relational databases using the term NoSQL. While NoSQL is often thought to mean it does not use SQL at all, it actually means not only SQL because these systems can engage in SQL-like queries.

Developers created NoSQL options to respond to the growing need to store and process web data, which is generally unstructured. The system allows for a distributed database system allowing developers to rely on multiple computers and servers.

The ad-hoc approach to data storage is incredibly fast and appropriate for storing various kinds of data and in large volumes. Slowly, these databases are becoming the database of choice for large, unstructured data sets because they are far more flexible, fast and economical.

Enormous companies like Twitter, Facebook and Google that process incredible volumes of data have turned to NoSQL to power their experiences. 

Big data is not a new term. It became official in 2005 but was something that many companies were grappling with before this. NoSQL has been the answer to dealing with big data and helping applications with CRUD operations.


NoSQL Database Flexibility

One reason why developers appreciate NoSQL databases so much is because they allow for various types of data storage. There are four formats NoSQL can store data in.

  1. Key-value
  2. Document
  3. Column
  4. Graph

Over the last decade, some databases are now multi-model, which means they can store data in more than one of these formats or even all four. And some databases merge SQL and NoSQL to provide the benefits of each type of database through using SQL-like language with a NoSQL database.

The future of NoSQL databases is strong. Given the ever-growing need for managing additional data, developers continue to rely more heavily on NoSQL and the industry doesn’t foresee that trend changing. 

For a multi-model NoSQL database that includes artificial intelligence and SQL-like queries through a command-line interface (CLI), download BangDB. The advanced NoSQL database is incredibly flexible and offers some of the most modern technology available for a database. 

Further Reading:

The Dark Side of Big Data Analytics with NoSQL

Non-relational database systems are growing in popularity. They’re scalable, accessible, virtually always available, and they solve many of the problems and limitations of relational database models in real-time

That’s why companies around the globe are turning to these modern technological wonders to power their businesses with big data insights.

Although amazing breakthroughs happen every day, there is a dark side to managing big data analytics with NoSQL, and it’s something we wanted to shed light on so you can make informed decisions for your own business.

Challenges of Big Data Analytics with NoSQL

  • It may not be the right solution for your data
  • It is not “one-size-fits-all”
  • Smaller field of experts for your in-house team
  • New technology can lack support in the early years

The ability to work with unstructured data in real-time applications is a major bonus for most companies. Nonetheless, it would be unwise to overlook the potential drawbacks as you decide which kind of database makes the most sense for you.

Let’s go a little deeper with some of the more common challenges companies face when they embark on their big data journey.

Looking for an innovative NoSQL solution?

Data Modeling is Never Done

The world of data modeling is rapidly expanding. That’s great, but it comes with a challenge. Data modeling in NoSQL is an ongoing process. 

It’s not one of those things where you set it and forget it. You need to constantly toy with your data modeling to figure out what setup works best for what you want to accomplish right now.

That’s because data modeling in NoSQL doesn’t work the same way as in relational systems. Instead of leveraging structured schemas, non-relational databases are flexible so that they are fast, scalable, and design-friendly. 

The drawback is that non-relational data modeling may not be as efficient when working with structured data which it wasn’t designed for.

NoSQL is Not One-Size-Fits-All

When it comes to big, fast-as-lightning data, you typically have four general data models to choose from:

  • Key-value store
  • Document-based store
  • Column-based store
  • Graph-based store

Each of these data models operates differently from the others. The challenge here is that you have to figure out which data model makes the most sense for the types of data you work with, and for what you want to accomplish with your analytics. 

Furthermore, each model has its own benefits and drawbacks to consider.

For instance, the key-value store matches unique key pairs to store data. This setup could be useful for storing online retail information such as product details, pricing, categories and more. Companies like Oracle and Redis use key-value pairs within their systems. Key-value store is a valid structure, but performance issues can crop up when keys are either too long or too short and this could be an issue for some.

In a document-store, records are stored in a single document, which makes the model semi-structured. The information within this model must be encoded in JSON or XML, and it is never stored in a table (as you’d find in a relational database). The benefit is that complex structures can be contained in a single record. At the same time, this can be a drawback since it opens up programmers to the potential of accidentally adding incorrect data into a table. 

Column-based store collects data in columns, hence the name, “column-based.” Again, this is similar to relational databases, except that relational databases store data in rows rather than columns. The core difference is that by storing data in columns, it is contained as a single, ongoing entry which speeds up the retrieval process. Unfortunately, the data entry process can be much slower with column stores when compared with relational systems, especially when dealing with large volumes of data.

The last of the main data models, graph-based store, represents data in graphs rather than tables. The benefit of this model is that it is highly flexible and the analytics captured can easily be extended with attributes. This model also benefits from rapid search returns, and faster indexing. On the downside, graph stores are much less efficient when it comes to high volumes of transactions. Likewise, they are also inefficient when working with queries across entire databases. This can be a major drawback for companies who deal in data warehousing.

Growing Base of Expertise

Let’s say you’ve reviewed your value store options, and you’ve decided that the drawbacks are worth the payoff. Not only is NoSQL more flexible and scalable, but the ability to call data up in real-time is just too good not to use. 

Now that you’ve decided to go this direction, you have to figure out who will handle your database administration. 

Yes, NoSQL tends to be friendlier from a design standpoint, but because the model is much newer than relational models, there are far fewer experts available.

Although NoSQL is rapidly making headway in removing the necessity of skilled maintenance personnel, there is still room to grow. For now, there remains a need for trained experts who can install and administer the system to ensure it operates smoothly 24/7.

Until more professionals migrate into big data systems, this could be a factor that you need to consider before making the leap.

Support Challenges

The final area worth mentioning expands on the previous area. In addition to a lack of experts in the field, many NoSQL databases are built upon open-sourced projects where quality support for the development of the database isn’t always available.

Lacking in-house expertise, it is essential that your NoSQL provider is capable of delivering premium support in a timely manner to keep your analytics flowing. 

Depending on the provider you choose, you may not have the support you need when you need it most, and this is possibly the single biggest reason why some companies continue using outdated, tedious models even when faster and flashier options have arrived.

All of that said, if you choose a strong database provider, such as BangDB, many of these problems can be mitigated. 

Not only does BangDB offer additional value stores beyond the limited options listed in this article, but it provides both free and enterprise versions of its database, a deep support library, and backend support for enterprise clients to ensure your transition into big data analytics is as seamless as possible.

Do the NoSQL Drawbacks Outweigh the Benefits When it Comes to Big Data?

Truthfully, no. As with any emerging technology, there will be challenges to overcome. But when it comes to building scalable systems that provide real-time analytics that you can actually use without spending countless hours on upkeep, NoSQL is the victor.

The key to how well NoSQL performs for your company depends largely on choosing the best database provider for your business, and ensuring that whoever you choose offers helpful resources, support, and ongoing services to keep you running like a well-oiled machine.

At BangDB, we pride ourselves on delivering all of that, and we even have an option to get started for free. If that sounds fair for you, then go here to download BangDB now and start building your big data analytics system today.

Further Reading:

How Data is Stored in a NoSQL Database: Concepts of NoSQL DB Architecture

The world of data storage and retrieval can be incredibly simple, or very challenging. 

When you leverage the right solution for the amounts and types of data you’re dealing with, then you’ll make life much easier for yourself.

First, you need to know what types of storage options are available to you. In general, you can store and retrieve data using any of the following:

  1. Spreadsheets (i.e. Microsoft Excel)
  2. Relational Database (i.e. SQL)
  3. Non-Relational Database (i.e. NoSQL)

Spreadsheets are best for small datasets. For instance, when you’ve got less than 10,000 records to manage, you can easily store them in rows within a single spreadsheet. Later, you can quickly search that spreadsheet to find the exact data you need.

Relational databases are a step up from spreadsheets. When you’ve got 10k+ records, or when you’ve got multiple spreadsheets with 10k+ records, then it makes sense to use a relational system that is designed to quickly store and retrieve data across multiple systems (spreadsheets).

As you continue to scale up toward big data, and as your datasets become more complex, you eventually reach a point where relational databases become inefficient. To get real-time analytics, and to deal with more sophisticated records, you need a faster, more powerful alternative, and that’s where the NoSQL database comes in.

Looking for an innovative NoSQL solution?

Now that you have an idea of the uses for different types of systems, let’s explore how data is actually stored within NoSQL.

NoSQL Data Store Options

Non-relational databases are powerful storage systems that emerged in the early 2000s. They’re designed to manage large volumes of unstructured data, return real-time web app analytics, and to process big data across the internet of things (IoT). 

Within the NoSQL system, there are multiple ways to store and retrieve records that surpass the limitations of relational systems. The primary ways data can be stored in NoSQL include:

  • Key-Value Store
  • Document Store
  • Column Store
  • Graph Store
  • Time Series Store

Some NoSQL databases work with more than one type of record store. Most non-relational systems use one to three of the above. A select few, such as BangDB,  offer queries across all five.

Key-Value Store

The key-value store is a database system that stores records as sets of unique identifiers with an associated (paired) value. This data-pairing is referred to as a “key value pair.” The “key” is the unique identifier. The “value” is the data being identified, or its location. 

A major benefit of key-value stores is that they are fast for data retrieval. Where relational systems store data across rows and columns, and need to query across the database to return a record, the key-value store is more flexible and only has to search for the key, then return the associated value.

Due to the speed of returns, and their flexibility, key-value stores are particularly useful in certain cases such as:

  • Storing, recalling, and updating product information, pricing, categories and other ecommerce related functions.
  • Storing user details, preferences, and session information for rapid recall and rewrites.
  • Generating real-time data to provide relevant advertising as users move through different areas of a platform or website.

The ability to minimize reads and writes, and to quickly locate datasets based on unique identifiers makes the key value store a blazing fast option that outperforms relational databases in almost every way for businesses that deal in retail, advertising, ecommerce, and other web applications.

Document Store

Another storage option, the document-store, stores data in a semi-structured document. The data can then be ordered with markers.

Information in this data type needs to be encoded in XML, JSON, BSON or as a YAML file, and is never stored in a table (which is why it is unsuitable for relational storage). Instead, complex datasets are contained in a single record. 

Retrieval occurs when a key is used to locate the document, and then that document is searched for the information required. 

A benefit of the document store is that different types of documents can be contained within a single store, and updates to those documents do not need to be related back to the database. 

Also, because there are no fields within this store, and therefore, no empty cells for missing records, the document store is incredibly efficient at returning data fast.

Document stores are highly useful when:

  • When working with JSON, BSON, XML, YAML files
  • You need to make changes to your data schema often
  • When you work with unstructured or semi-structured data
  • When you need something simple for development

Document stores are flexible, easily scalable, and developers can work within them, even without prior knowledge of the system. These benefits make them a worthy tool for web applications, and for handling big data in a sensible manner.

Column Store

Column-based stores record data in columns, rather than in rows. By storing data in columns, it is contained as a single, ongoing entry. This minimizes the number of disks accessed, and avoids pulling in unnecessary memory, which speeds up record retrieval since a query does not need to pass over irrelevant rows to return information. Instead, only the information within the column is queried.

Column stores are most frequently used by companies that deal with large data warehousing setups. The data is structured as a table with columns and rows, and is then stored logically in a column-wise format so irrelevant data does not have to be bypassed before the target data is accessed and returned.

Column store databases are best for:

  • Applications with many reads and few writes
  • When your data has a lot of repetitive records for each value
  • Data warehousing operations
  • Increasing retrieval speed and decreasing memory usage

Column stores can save you time and computing power, especially when you have a lot of information that repeats, such as rows of names, addresses, phone numbers, and any other records that might be stored under individual data points.

Graph Store

For some businesses, relationships and connections between data take priority, so a graph store makes the most sense. Graph stores represent data in graphs instead of tables which makes them highly flexible and easily extendable. 

The graph store database returns search results fast and speeds up indexing by representing data as networks of nodes and edges. In a graph store, data is stored in nodes, and then connected with relationships in edges which are then grouped by labels. 

Graph stores are most useful for things like:

  • Data visualization and graph-style analytics
  • Fraud prevention and enterprise operations
  • Geospatial routing
  • Payment systems
  • Social networking systems

They’re best at querying related datasets, although they may not be as efficient when working with big data which can slow their process down.

Time Series Store

A final NoSQL data store option is the time series store which is primarily used for managing datasets that change over time. Time series store captures fixed and dynamic value sets and returns timely analytics.

Imagine a car lot with multiple cars. The time series store might have a fixed value data point for each car, and then tracks the dynamic values within each car such as oil levels, tire pressure, etc. alongside a timestamp to allow the end user to see how these metrics have changed over time.

Time series stores are valuable for:

  • Continuously capturing a stream of metrics
  • Analyzing datasets over periods of time
  • Predictive analysis (i.e. predicting when a car’s oil will need changed)
  • Monitoring the status of various systems with easily accessible analytics

Time series store is best for businesses where multiple systems require ongoing measurements within individual data points.

Which NoSQL Storage Option is Right for You?

As any good developer will tell you, there is no one-size-fits-all solution. Each business is different and has unique storage needs. 

When you deal with small volumes of structured data, then individual spreadsheets or relational databases can be a good fit; however, as your need for big data and real-time information increases, you will need to upgrade to a non-relational database. 

From there, you will have to decide which storage solutions make the most sense for your business based on what you want to accomplish and the types of data you work with. 

If you work with a variety of data types, and you need multiple storage options, then consider BangDB – one of the only NoSQL database providers that offers all of the storage types listed above in a single solution, even for free. 

Further Reading:

Did You Know? Popular Applications that Use NoSQL

NoSQL databases are no longer something that developers will use in the future. We’ve officially reached the future where these databases are common ways to power large, popular applications.

We’ll demonstrate some popular applications you might not realize are using NoSQL databases and why NoSQL is perfect for these applications.


Uber grew at incredible rates when it was first introduced to the marketplace. The app requires instant data availability to pair drivers with empty cars with nearby potential passengers waiting on the curb. 

The application had to be incredibly scalable because the company couldn’t afford to migrate its data every time it needed a larger server. Using NoSQL also helped Uber build an application with failure systems where data is stored in various nodes so that the company can work on the application without taking it offline. 

When Uber reimagined its application, it used Riak, a distributed NoSQL database with a flexible key-value store model. The database offered all the tools and resources the rideshare app needed to power incredible results.


Cisco is a technology powerhouse, but it was facing a serious challenge in its customer experience and support team. The largest challenge Cisco customers face is a lack of compatibility or improper configuration. 

To help this, Cisco wanted to present configuration and compatibility information based on the topics and keywords the customers were typing into the knowledge base. Cisco relied on BangDB for its NoSQL database needs because the database is multi-model and is one of the leaders in the marketplace based on performance.

Using AI and machine learning, Cisco found relationships between what customers were entering into the search field to provide relevant information for them.


To create a better customer experience, Netflix migrated much of its systems to NoSQL. The high availability of a NoSQL database was very attractive and ultimately that availability won out over consistency. 

But with such a massive operation, Netflix needs more than just one NoSQL database. It uses three in combination: SimpleDB, HBase and Cassandra. 

Rearchitecting the company’s systems was challenging since the Netflix team never wanted the service to be unavailable. But the transition has been worth it. Real-time queries provide customers with information about the shows and movies they want to watch when they want to watch them. 

Cassandra helps protect the system from a single point of failure. And now Netflix can scale its operation infinitely to serve the ever-growing list of subscribers that the company serves.

Looking for an innovative NoSQL solution?


Forbes has always been on the cutting edge of technology. In 1996 it was the first business publication to launch a website. And since then, the publication has been doing all that it can to serve its subscribers with high-quality content.

Forbes is 100 years old. The company could have stayed with its old ways of doing things where everything was in print. But instead, it has focused on setting trends for the industry and serving as a blueprint for others to follow.

That’s true even in its technology. To serve its 140 million online customers, Forbes migrated its service to MongoDB Atlas. Now its release cycles are significantly faster and its cost of ownership is 25 percent less.

Moving to a cloud infrastructure allowed the publication to respond to challenging times during the COVID-19 pandemic and increase its subscriptions at a time when people had more availability to read publications.


Accenture had a customer that was an automobile manufacturer looking to increase its lead generation and lead scoring abilities. It needed real-time website data to inform a customer’s propensity to purchase a car. 

Engaging with these customers in the moment was essential to attracting the visitor and making them a prospect. Accenture chose BangDB as the NoSQL database to provide learning models that analyzed the visitors’ behavior to predict their lead scores. The insights BangDB’s AI and streaming brought allowed Accenture to build a dashboard that tracked the customer in real-time. 

The lead scoring application provided the automobile manufacturer twice the conversion rate it had been experiencing thanks to a better, more efficient use of sales resources.

Facebook Messenger

Facebook created Cassandra, a NoSQL database. The purpose of this database was to help in indexing messages users send to one another and allowing users to search those messages using keywords. 

Facebook designed a way to use each person’s user ID as a primary key. All message data was part of another column. This allows Facebook Messenger to display all messages sent between users in one conversation thread. 

Cassandra is a wide-column store database that allows Facebook to scale its messenger operations with no single point of failure. The system is distributed across hundreds of nodes stored in different data centers so that if any one node fails, the system will still run. 


Google Mail

Google Bigtable helps the massive online company power its transactions. Within Google Mail, that means indexing large data sets and allowing users to find their messages based on keywords.

Bigtable is a wide-column store. Google designed the database so that it would have greater control over its technology instead of using another service. And now it makes that database available to others. 

Many other Google services also use Bigtable, including Google Maps, Google Earth and Google Finance.



LinkedIn uses a graph NoSQL database to power relationships within the system. NoSQL helps the massive networking platform manage rolling data in the system to keep the data available for users to call upon even as it is used and changed.

LinkedIn launched its fault-tolerant NoSQL database named Espresso in 2015. The technology powers LinkedIn applications, such as the member profile, InMail, homepage and more. 

Espresso is a document-oriented database. It is unique because it guarantees operability among all LinkedIn applications.


Applications Ideal for NoSQL

NoSQL databases have many use cases. But they are especially ideal in certain circumstances and types of applications. These applications include the following.

  • Internet of things (IoT) applications
  • Real-time or nearly real-time data processing
  • Mobile apps
  • Discussion threads
  • Social media
  • Knowledge bases
  • eCommerce
  • Applications you need to develop quickly
  • Applications that require various forms of data
  • Systems that process large amounts of data
  • Applications you need to grow and scale rapidly

To learn more about BangDB and the ways that our customers have used our technology, check out our case studies. You’ll learn scenarios where developers implemented BangDB to solve problems, delight customers and streamline operations.Ready to get started with a NoSQL database? Download BangDB for free now to learn more and see if it might be right for you.

Further Reading:

Why Developers of Applications Choose BangDB NoSQL Database

With dozens of NoSQL databases available, developers have many options to choose from to power their applications. So what makes a developer select BangDB?

We’ll explain the main differentiators that make this NoSQL database attractive to our customers and demonstrate ways that some customers have used the database to transform and modernize their applications.

Here’s a look at the top 9 reasons why developers choose BangDB for their NoSQL database needs.

1. It Is a Multi-Model Database

Modern applications require the use of many different kinds of data. Developers appreciate the fact that they can ingest, process and query these various types of data with BangDB. This means that developers can use the following data types within the system.

  • Document
  • Graph
  • Time series
  • Text
  • Large files 

Multi-model ensures that the database will grow and change with your organization to meet today’s needs and your future needs.

Looking for an innovative NoSQL solution?

2. Stream Processing Provides Real-time Continuous Data Intelligence

BangDB is rare in that it natively supports stream processing so that you can continuously ingest and process data to power real-time predictive analytics. Many modern applications now require stream processing, such as IoT applications. 

Stream processing can take the following actions on data.

  • Aggregations
  • Analytics
  • Transformations
  • Enrichment
  • Ingestion

Batch processing is becoming a thing of the past for modern applications. Using BangDB allows developers to respond to new events as they happen while grouping and collecting data the moment that data is generated.

Consumers now expect these instant reactions from stream processing. For example, when a user’s credit card is stolen and used at a suspicious location, stream processing can make real-time fraud detection possible.

And in the world of personalization, stream processing allows companies to customize their marketing and customer experience to match the interests of their users.

Stream processing has many use cases that are helping developers create excellent applications for users now that will grow in the future.

One user on Capterra had this to say about using BangDB:

“Installed it after recommendation from a friend. Experienced a slight learning curve about data streams and their UI. However, soon it has become a friendly tool for my analysis work. The pricing is also quite sweet.”

Natively Integrated Artificial Intelligence

3. Natively Integrated Artificial Intelligence (AI)

Many NoSQL databases layer on artificial intelligence to power machine learning. But with BangDB, AI is natively integrated. This enables you to train, test, deploy, predict and measure using machine learning. 

Using native AI can aid in getting your application to market faster because it doesn’t require additional coding. 

4. It’s One of the Highest Performing Databases on the Market

BangDB offers the highest throughput for read and write operations, allowing it to handle data in an incredibly efficient manner. In fact, BangDB can process data at about two times the rate of its leading competitors.  

One user had this to say about BangDB’s performance in a Sourceforge review:

“Its performance is very high and works well for high load at scale. It provides variety of indexes and query support and implements cypher query language for graph.”

5. BangDB is ACID-compliant

Developers select BangDB when they are looking for a transactional database to power their application. BangDB is one of few NoSQL databases that is ACID-compliant. All you have to do is start the service with transaction mode on.

6. Developers Can Use The Command Line Interface to Interact with the Database

BangDB uses a command line interface (CLI) to allow users to interact with the database and query the data. For queries, developers can use SQL-like language. Or if you’re completing a graph query, you can use cypher syntax. 

Despite being a NoSQL database, developers can still use SQL-like language while having complete access to machine learning and streaming. Using a command line makes interacting with the database incredibly simple and will reduce the learning curve required for learning how to use and interact with a NoSQL database.

7. Unlimited Free Use Version

BangDB offers an entirely free NoSQL database. We don’t put any limitations on how you use the database, making it valuable and useful for a variety of different use cases. Unlike limited free trials where you can test out the database for a set time period before paying for it, BangDB has no limit to how long you can use the database before having to pay for it.

download BangDB

8. More Than 120,000 Developers Have Downloaded BangDB

Despite being a newer database to join the market, BangDB already has more than 120,000 downloads. And with a strong network of partners helping customers get the most out of the database, it’s no huge surprise that enterprises are appreciating the benefits that the NoSQL database provides.

9. Beneficial for Different Types of Businesses

BangDB works across many industries and types of businesses. Whether you’re seeking a database to power an eCommerce application or want to take advantage of the internet of things (IoT), BangDB is an excellent choice for your needs.

We’ve helped companies build a wide variety of applications, from lead generation and scoring applications to real-time analytics for marketing insights and delivery, BangDB helps developers create outstanding technology and applications.

What to Look for in a NoSQL Database

As you prepare for selecting a NoSQL database for your application, consider these key features and functions you might want your database to have. While you might not think you have a need for all of these now, as your application changes and you develop new use cases for it, you might find that having these capabilities is helpful.

  • Flexible schema
  • Consistent data across all nodes
  • Available to respond to requests quickly
  • Partition tolerance that allows the system to keep operating even during a network or node failure
  • Regular backups
  • ACID-compliant
  • Encryption at rest

Download BangDB for Free

Want to see why developers choose BangDB? Download it now to experience the BangDB difference and how it can power your application for improved scalability, availability and performance.

Further Reading:

Customer message analysis – predictive & streaming


To implement the use case “customer message analysis in predictive and streaming manner”, you can use following resources

  • code, files, data
  • Video of the demo / usecase
  • BangDB binaries


Users and customers are sending their messages or reviews from their devices. There are several such messages streaming from different users into the system. We must first be able to ingest these messages in real time manner. Further we should be able to processes every single message and  take corrective action as needed.

The processes would include following;

  1. set the streams and sliding window and ingest the data in these streams in continuous manner
  2. find out the sentiment of the message [ positive, negative ] using IE (information extraction) (NOTE: we can find out many different sentiments/ emotions as we want, the demo deals with only two). We need to train a model here for this
  3. filter messages with negative sentiment and put them in separate stream for further action / processing
  4. find out a definitive pattern and send such events matching the patter to another stream for further review / action. The patter is as follows;
    1. Any particular product that gets minimum 3 consecutive negative sentiment messages
    2. from different users in span of 1000 sec, find this pattern in continuous sliding manner
  5. store few triples in graph store like (user, POSTS_REVIEW, prod) and (prod, HAS_REVIEWS, revid), revid id review id and prod is product
  6. set running stats for different attributes in the event such as unique count for users or min/max/avg/sdtdev/sum/kurt for amount spend etc.
  7. set up reverse index for messages such that it can used for text search by the user
  8. set up secondary indexes for several attributes that could be helpful in query and also internal stream joins/ filter etc.

Relevant application areas

  • ecommerce
  • Payment and banking
  • Ridesharing and cabs on hire, ex Uber, Ola
  • Home delivery entities (food, etc)


There are several challenges here, some of them could be

  1. Volume and Velocity. The number of messages could be very high, as these could be several users sending messages per second across the geographical areas. Hence data ingestion in real time is critical
  2. The messages could be in English or in other vernacular language’s, hence we need to extract sentiment from unstructured data, and keep improving or updating the models in real time
  3. Extracting patterns from the streaming set of events in continuous manner, this requires CEP on the streaming data which is very hard to implement on SQL or regular NoSQL databases
  4. Storing certain triples (sub, obj, predicate) in a graph which is continuously updated as events arrive, helpful in linking data and/or events
  5. Different database queries along with text search which requires many secondary and reverse indexes
  6. Infrastructure deployment and maintenance if too many silos are used. Further automation is difficult to achieve in typical deployment models

Benefits of BangDB

  1. Use light weight high performance BangDB agents or other messaging framework to stream data into the BangDB. BangDB is high performance database with ingestion speed over 5K+ events per second per server leading to half a billion events processing per commodity server in a day
  2. Integrated stream processing within BangDB allows users to simply start the process with a simple json schema definition. There is no extra silos setup for streaming infrastructure
  3. Integrated AI within BangDB allows users to simply train, deploy and predict on incoming data without having to set up separate infra and then exporting data/ importing model etc. The entire process can be automated within BangDB
  4. BangDB is a multi-model database and it also allows Graph to be integrated with streams such that the graph is updated on streaming data with triples
  5. BangDB supports many kinds of indexes including reverse indexes, hence running rich queries along with searches on BangDB is quite simple
  6. Integrated with Grafana for visualisation of time-series data

Overview of the solution

  1. We have a stream schema ecomm_schema. Here in these streams we will be ingesting data from various sources
  2. Ingestion of data happens as and when data is created. Therefore agent monitors a set of files here and as we write data into these files agent will parse the data and send to bangdb server. We could directly write data using cli or using program which uses bangdb client etc…
  3. We have 3 different data sources here;
    • product data – this is rathe non-streaming data, but still we could ingest these using agent
    • order data – as and when order is placed
    • customer or user reviews/ messages. This should be high volume streaming data
  4. Sample data is provided here, however you may add more data to run it at larger scale etc.

Steps to run the demo on your own

  • Set up BangDB on your machine

                       Note: If you have got the BangDB already, you may skip the step

                       a. Get the BangDB, please check out or

                       b. Follow the read me file available in the folder and install the db

Check out or clone this repo to get the files for the use case, and go to customer_reviews dir

Note: It will be good to have several shell terminals for server, agent, cli and mon folder

> cd customer_reviews

copy the binaries (server, agent, cli) to the folders here in this folder (note: it’s not required but simple to run the demo)

  • copy bangdb-server-2.0 binary to the server/ folder
  • copy bangdb-agent-2.0 binary to the agent/ folder
  • copy bangdb-cli-2.0 binary to the cli/ folder
  • Before running the demo from scratch, you should simply clean the database, ensure agent.conf file is reset, and the files which are being monitored by the agent are also reset. To do this run the file in the base folder Also, please ensure the file and dir attributes are pointing to the right file and folder respectively in the agent/agent.conf file

  • Run the server, agent and cli

Run the server

> cd server

> ./bangdb-server-2.0 -c hybrid -w 18080

> cd ..

Note: we are running bangdb in hybrid listening mode (tcp and http) both, http port is 18080. This will come handy in ingesting data using agents, interactions using cli etc. and at the same time visualisation using Grafana

Run the agent

> cd  agent

> ./bangdb-agent-2.0

> cd ..

Run the cli

> cd cli

> ./bangdb-cli-2.0

you will see something like this on the prompt

server [ : 10101 ] is master with repl = OFF

 __     _    _   _   ____   ___    ___
|   \  / \  | \ | | | ___\ |   \  |   \
|   / /   \ |  \| | | | __ |    | |   /
|   \/ ___ \| | \ | | |__|||    | |   \
|___/_/   \_|_| |_| |_____||___/  |___/

command line tool for db+stream+ai+graph

please type 'help' to get more info, 'quit' or 'exit' to return


  • Register the schema (set of streams)

Let’s first register the stream schema into which we will be receiving the data

bangdb> register schema ecomm_schema.txt

now let’s ingest few data into the stream product.

NOTE: to help user ingest some events, there is simple script “” which takes following arguments;

bash sendline <fromfile> <tofile> <numrec> <stopsec>

send <fromfile> to <tofile> with <numrec> num of events <stopsec> per sec

In real scenario, application or program will write these events into some log file and agent will keep sending data to the server. For demo purpose, we will simulate application / program by using

  • Send product data to the server

> cd mon/

> bash ../data/ecomm_product.txt prod.txt 1 1

Note: you can send 1000’s of event per second by using

> bash ../data/ecomm_product.txt prod.txt 1000 1

  • Send order data to the server

bash ../data/ecomm_order.txt order.txt 1 1

Now come back to the cli shell terminal and train a model for sentiment analysis.

BangDB will keep using this sentiment model for adding “sentiment” attribute for every event as it arrives

  • Train sentiment model

When you train a model from cli, here is what you will see, you may follow as it’s shown here or simply follow the workflow as cli will keep asking questions.

NOTE: Sentiment model requires a knowledge base for the context. It’s always a good idea to train a KB for the context/ area in which we work. Therefore for better accuracy and performance we should ideally train a model. However, for the demo purpose, we have a sample kb (which is trained on minimal data), which can be used but not sufficient. If you want proper KB for sentiment analysis for customer reviews / comments / messages then please send me mail (, I will forward the link to you. For production, we must use proper trained KB file

bangdb> train model user_sentiment
what's the name of the schema for which you wish to train the model?: ecomm
do you wish to read earlier saved ml schema for editing/adding? [ yes |  no ]: 

	BangDB supports following algorithm, pls select from these
	Classification (1) | Regression (2) | Lin-regression/Classification (3) | Kmeans (4) | Custom (5)
	| IE - ontology (6) | IE - NER (7) | IE - Sentiment (8) | IE - KB (9) | TS - Forecast (10) 
	| DL - resnet (11) | DL - lenet (12) | DL - face detection (13) | DL - shape detection (14) | SL - object detection (15)

what's the algo would you like to use (or Enter for default (1)): 8
what's the input (training data) source? [ local file (1) | file on BRS (2) | stream (3) ] (press enter for default (1)): 1
enter the training file name for upload (along with full path): ../data/review_train.txt

	we need to do the mapping so it can be used on streams later
	This means we need to provide attr name and its position in the training file

need to add mapping for [ 2 ] attributes as we have so many dimensions
enable attr name: sentiment
enable attr position: 0
enable attr name: msg
enable attr position: 1

we also need to provide the labels for which the model will be trained
    enter the label name: positive
do you wish to add more labels? [ yes |  no ]: yes
    enter the label name: negative
do you wish to add more labels? [ yes |  no ]: 
    enter the name of the KB model file (full path)(for ex; /mydir/total_word_feature_extractor.dat): total_word_feature_extractor.dat
    Do you wish to upload the file? [ yes |  no ]: yes
training request : 
   "training_details" : {
      "train_action" : 0,
      "training_source_type" : 1,
      "training_source" : "review_train.txt",
      "file_size_mb" : 1
   "model_name" : "user_sentiment",
   "algo_type" : "IE_SENT",
   "labels" : [
   "schema-name" : "ecomm",
   "total_feature_ex" : "total_word_feature_extractor.dat",
   "attr_list" : [
	 "position" : 0,
	 "name" : "sentiment"
	 "name" : "msg",
	 "position" : 1
do you wish to start training now? [ yes |  no ]: yes
model [ user_sentiment ] scheduled successfully for training
you may check the train status by using 'show train status' command
do you wish to save the schema (locally) for later reference? [ yes |  no ]: 

Now you can see the status of the model training

bangdb> show models
|key                 |model name    |   algo|train status|schema name|train start time        |train end time          |
|ecomm:user_sentiment|user_sentiment|IE_SENT|passed      |ecomm      |Sat Oct 16 00:07:11 2021|Sat Oct 16 00:07:12 2021|

Now let’s ingest the customer reviews and see the output

> cd mon

> bash ../data/user_msg.txt reviews.txt 1 1

come back to cli terminal and select few events from the stream “reviews” in the “ecomm”schema

bangdb> select * from
|key             |val                                                                                                                             |
|1634329532924119|{"uid":"sal","prod":"ipad","msg":"finally the order arrived but i am returning it due to delay","tag":"return","revid":"rev13","|
|                |_pk":1634329532924119,"sentiment":"negative","_v":1}                                                                            |
|1634329531921928|{"uid":"raman","prod":"guitar","msg":"finally order is placed, delivery date is still ok do it's fine","tag":"order","revid":"re|
|                |v12","_pk":1634329531921928,"sentiment":"positive","_v":1}                                                                      |
|1634329530919064|{"uid":"sal","prod":"iphone","msg":"just ordered for p3 and i got a call that the delivery is delayed","tag":"order","revid":"re|
|                |v11","_pk":1634329530919064,"sentiment":"positive","_v":1}                                                                      |
|1634329529916681|{"uid":"raman","prod":"guitar","msg":"the product is in cart, i want to order but it's not going","tag":"cart","revid":"rev10","|
|                |_pk":1634329529916681,"sentiment":"negative","_v":1}                                                                            |
|1634329528914003|{"uid":"mike","prod":"football","msg":"how amazing to get the packet before time, great work xyz","tag":"order","revid":"rev9","|
|                |_pk":1634329528914003,"sentiment":"positive","_v":1}                                                                            |
|1634329527911595|{"uid":"sal","prod":"ipad","msg":"not sure why the product is not yet delivered, it said it will be done 3 days ago","tag":"orde|
|                |r","revid":"rev8","_pk":1634329527911595,"sentiment":"negative","_v":1}                                                         |
|1634329526909432|{"uid":"rose","prod":"guitar","msg":"not sure if this site works or not, frustating","tag":"order","revid":"rev7","_pk":16343295|
|                |26909432,"sentiment":"negative","_v":1}                                                                                         |
|1634329525906102|{"uid":"hema","prod":"p3","msg":"the tabla got set very smoothly, thanks for the quality service","tag":"order","revid":"rev6","|
|                |_pk":1634329525906102,"sentiment":"positive","_v":1}                                                                            |
|1634329524902468|{"uid":"hema","prod":"tabla","msg":"i received the product, it looks awesome","tag":"order","revid":"rev5","_pk":163432952490246|
|                |8,"sentiment":"positive","_v":1}                                                                                                |
|1634329523899985|{"uid":"rose","prod":"guitar","msg":"order placed, money debited but status is still pending","tag":"order","revid":"rev4","_pk"|
|                |:1634329523899985,"sentiment":"negative","_v":1}                                                                                |
total rows retrieved = 10 (10)
more data to come, continue .... [y/n]: 

As you see, the attribute “sentiment” is added with the value predicted by the model user_sentiment

Now let’s check out the events in the filter stream. We see that all negative events are also available in the stream negtative_reviews

bangdb> select * from ecomm.negative_reviews
|key             |val                                                                                                                             |
|1634329532924119|{"uid":"sal","prod":"ipad","msg":"finally the order arrived but i am returning it due to delay","tag":"return","revid":"rev13","|
|                |_pk":1634329532924119,"_v":1}                                                                                                   |
|1634329529916681|{"uid":"raman","prod":"guitar","msg":"the product is in cart, i want to order but it's not going","tag":"cart","revid":"rev10","|
|                |_pk":1634329529916681,"_v":1}                                                                                                   |
|1634329527911595|{"uid":"sal","prod":"ipad","msg":"not sure why the product is not yet delivered, it said it will be done 3 days ago","tag":"orde|
|                |r","revid":"rev8","_pk":1634329527911595,"_v":1}                                                                                |
|1634329526909432|{"uid":"rose","prod":"guitar","msg":"not sure if this site works or not, frustating","tag":"order","revid":"rev7","_pk":16343295|
|                |26909432,"_v":1}                                                                                                                |
|1634329523899985|{"uid":"rose","prod":"guitar","msg":"order placed, money debited but status is still pending","tag":"order","revid":"rev4","_pk"|
|                |:1634329523899985,"_v":1}                                                                                                       |
|1634329522897451|{"uid":"sal","prod":"ipad","msg":"even after contacting customer care, we have no update yet","tag":"order","revid":"rev3","_pk"|
|                |:1634329522897451,"_v":1}                                                                                                       |
|1634329521895545|{"uid":"sal","prod":"ipad","msg":"the order 2 was placed 4 days ago, still there is no response, i am still waiting for any conf|
|                |irmation","tag":"order","revid":"rev2","_pk":1634329521895545,"_v":1}                                                           |
|1634329520891590|{"uid":"sachin","prod":"cello","msg":"even after calling 20 times, the customer care is not responding at all","tag":"order","re|
|                |vid":"rev1","_pk":1634329520891590,"_v":1}                                                                                      |
total rows retrieved = 8 (8)

As you see, the events got automatically collected in this stream, we can further set notification as well which will allow server to take actions / sends notifications in automated manner

But if you see, we don’t have any event in the negative_reviews_pattern stream. This is because we haven’t sent the events which could have formed the pattern. To remind, pattern is defined as “at least 3 consequtive negative events for the same product but from different users within 1000 sec”. We sould like to extract these patterns in continuous manner and store these events in the negative_reviews_pattern stream

Let’s now add two events which are negative (as you note, the last event is predicted as negative so another three more negative event should trigger a pattern)

bangdb> insert into values null {"uid":"alan","prod":"ipad","msg":"finally the order arrived but i am returning it due to delay","tag":"return","revid":"rev14"}

bangdb> insert into values null {"uid":"john","prod":"ipad","msg":"frustating that product is not delievered yet","tag":"return","revid":"rev15"}

bangdb> insert into values null {"uid":"johny","prod":"ipad","msg":"frustating and disappointing that product is not delievered yet","tag":"return","revid":"rev16"}

Now select from the pattern stream

bangdb> select * from ecomm.negative_reviews_pattern
|key             |val                                                                                                                             |
|                |329669688454,"_v":1}                                                                                                            |
total rows retrieved = 1 (1)

As you see it has two uids (since we select both as per schema definition – see the ecomm_schema.txt). The first one where the pattern started and the one where it got completed.

You can play with this and see how it works. If another negative event comes for the product which forms the pattern then it will get collected. else if broken then next time when the pattern is seen, server will send that event to the stream etc.

Now, let’s see the triples as stored by the server in graph structure, we will run Cypher queries

bangdb> USE GRAPH ecomm_graph
USE GRAPH ecomm_graph successful

bangdb> S=>(@u uid:*)-[POSTS_REVIEWS]->(@p prod:guitar)
|sub      |pred         |        obj|
|uid:rose |POSTS_REVIEWS|prod:guitar|
|uid:rose |POSTS_REVIEWS|prod:guitar|

bangdb>  S1=>(@u uid:hema)-[POSTS_REVIEWS]->(@p prod:*)-[HAS_REVIEWS]->(@r revid:*)
|sub       |pred       |       obj|
|prod:p3   |HAS_REVIEWS|revid:rev6|

And so on. Please see for more info on stream, graph, ml etc… you may get help from cli, for ex; help on graph, type “help graph”, for ml type “help ml” etc…

Further, you can run for higher volume with high speed and high volume implementation of the use case. You can train more models, add more triples etc. as required.