Month: November 2021

The Dark Side of Big Data Analytics with NoSQL

Posted on by Sachin

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 a “one-size-fits-all”
  • A 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 into some of the more common challenges companies face when they embark on their big data journey.

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 a 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 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. A 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. 

The 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, the 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 that 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 in 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 leaping.

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, your NoSQL provider must be capable of delivering premium support promptly 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 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 to 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

Posted on by Sachin

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.

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 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 record assets 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 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 and 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 sensibly handling big data.

Column Store

Column-based stores can also be a good NoSQL storage option. It records 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 NoSQL storage makes the most sense. Graph stores represent data in graphs instead of tables which makes them highly flexible and easily extendable. 

The graph NoSQL storage 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 storage 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
  • Predictive analysis (i.e. predicting when a car’s oil will need to be 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 offer all of the storage types listed above in a single solution, even for free. 

Further Reading:

Did You Know? Popular Applications that Use NoSQL

Posted on by Sachin

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 popular applications.

Uber

Uber uses NoSQL databases

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

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.

Netflix

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.

Forbes

Forbes is also one of popular applications that relies on NoSQL technology. 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

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 at 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 with twice the conversion rate it had been experiencing thanks to a better, more efficient use of sales resources.

Facebook Messenger

Facebook - Popular Social media Application / Platform

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 anyone 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

Linkedin - Popular Application for connecting with people

LinkedIn is one of those popular applications that 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

Posted on by Sachin

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

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.

2. Stream Processing Provides Real-time Continuous Data Intelligence

BangDB NoSQL Database 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 a 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 reading and writing operations, allowing it to handle data in an incredibly efficient manner. 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 a variety of indexes and query support and implements cipher query language for the graph.”

5. BangDB NoSQL Datbase 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 cipher 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 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 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 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: