Why AI needs Graph and Streaming database for higher efficiency


AI has become a more necessary entity for any kind of data processing today than ever when it comes to data analysis. More and more systems are trying to improve the AI model efficiency as it directly translates to business outcomes. If the model performs well then, the outcome is good else the outcome is counter-productive in many cases. Therefore, it’s extremely important to get the AI model right. What makes the biggest difference when it comes to the model’s efficiency? it’s the ability to leverage the context as well along with the set of features for training purposes. AI Graph structure is one of the most powerful mechanisms to represent the context within the data. Therefore, AI needs Graph and Streaming Database for higher efficiency.

Example use cases

Every decision-making is largely driven by AI at the core. This can be seen in the way e-commerce companies are targeting users. The way fintech is improving the customer experience is through AI models. The way vehicles are becoming more autonomous. The way cyber security is hardening the firewall to thwart the security threats, and many more use cases in various sectors. But having AI models trained onset of features is not sufficient, we must also capture the context in the most natural ways to improve the efficiency of the prediction. For example, it’s hard to tell the difference between two random similar acts without taking the context into the account. Therefore, it’s imperative to represent the context along with data and leverage these together during the model training process.

Let’s consider the example where an e-commerce company or a fintech company wants to recommend a set of products to users. If we just consider two random people with similar basic profiles, we might end up recommending an inhomogeneous manner and risk losing both the potential opportunities. However, if we consider their context then suddenly, we serve them a lot more relevant recommendations. This concept is widely understood and accepted. But the central question is, how do we capture and use contexts for the AI model?

Definition of context

To answer this, we must understand the meaning of context. And the context in a simple sense could be defined as the time, environment, and background in which certain events occur. These time, environment, and background can also be loosely defined as the different participating entities and their various inter-relationships. While the identity of nodes may be invariant, there could many numerous dynamic relationships that could be defined and the properties of the nodes could change continuously. The combinations of entities and relationships took together to bring the context into the processing as well.

Once we have understood that the context can be defined as entities and their relationships, the next question would be, how do we efficiently store these contexts in the database and query them in a high-performance manner? How do we enrich the data as it flows into the system such that when it comes to model training, we can leverage not only the contexts but also some of the extra computed values as part of the features for models? For example, can we find the natural clusters within the data? can we find the similarity scores among different entities? can we find some recurring patterns? If yes, then these could become part of the feature set for model training.

Steps to ingest, process, store in Graph, and Train AI Models

First, we must ingest the data in such a manner that it could be taken into the system without any impedance. One of the most difficult and heavy tasks for data processing is ETL and it’s widely recognized that sometimes we spent the majority of our time in ETL to try to get it right otherwise heavy penalty awaits if even a minor thing goes wrong at the beginning. BangDB avoids this process to great extent by implementing continuous data ingestion mechanism along with processing that could be done to extract and transform what we need at any given time. BangDB allows users to continuously ingest the data and also transform them while it’s being ingested at any point in time to keep enriching it for various further computations. Running statistics, joins, refers, complex event processing, filters, computed attributes, etc. are some of the tools available that can be used to enrich, add, and expand the scope in a real-time manner while processing every single event at a time. It can also continuously update the underlying Graph Store as data arrives. Since we have plenty of methods to add/transform the data within the event processing framework, the graph receives a lot more enriched data along with raw events which makes the structure way more valuable.

Next, we pass the data from the stream layer to the graph store where all different entities and their relationships could be dynamically stored. We can simply tell the stream layer to pass the data to the graph store. BangDB Graph store is very powerful and efficient where the triples (subject, object, and predicate) can be stored explicitly or implicitly. While the stream layer explicitly pushes the data, we can use IE (information extraction) to do NER (name entity recognition) and relationship definitions among the entities. BangDB graph is feature-rich and quite an efficient store for triples which allows Cypher and SQL (like) queries to be executed for data retrieval.

Now how does Graph help in building AI models? First, it allows us to build the feature sets quite efficiently. While we fetch the entities, we also fetch these entities based on the relationships. Further, we can exploit the way data is stored within the Graph store by extracting the various natural clusters and groups. Next, we can use the inbuilt Graph processing methods for computing the similarity scores between different entities and use these scores while building the models. These clusters, groups, and similarities could be computed in many different dimensions. For example, groups based on location, age, purchasing habits, common products, spending behavior, etc. Similarity scores are based on past patterns, anomalies, personal data, life journeys, etc.

    Questions we want to answer at run time

    Some of the interesting questions that we can answer using Cypher within BangDB are the following. While these examples are just to give you a sense of the power of Graph processing within BangDB and help you in extending these to define many more such questions/ commands as relevant for your business

    1. Process the entities cluster Analysis within Graph to compute and return similarity scores. This is the template for similarity based on feature set X
    2. Process Association rule mining using natural Graph properties for recommendations
    3. Do customer segmentation based on cluster analysis and return similar users
    4. Use collaborative filtering for a set of features that have fixed and limited set of values to identify similar users
    5. Do Classification of different groups/clusters
    6. Popularity based / Trend based similarity scores and clusters
    7. Seasonally based ontologies and triple set

    BangDB Graph Features

    BangDB is a converged database platform that natively implements and provides stream processing, AI, Graph, and multi-model data persistence and query. It provides the following high-level features for Graph processing.

    • Node, entity, triple creation
    • Running query and selecting data (Cypher and SQL*)
    • Statistics (Running and continuous)
    • Graph Functional properties
    • Graph algorithms
    • Set operations
    • Data Science (entire AI with Graph)

    To see more details on Graph, please check out the Graph introduction

    You can check out this paper which has pretty good details on Representing Learning on Graph

    REAN model to achieve higher conversions through hyper personalisation and recommendations

    BangDB implements REAN Model to enable conversion through personalization. It ingests and processes various aspects of users or customers and their other activities to optimize the conversion rate. With the REAN model, e-commerce companies can ingest and analyze the clickstream and all pixel data in real-time to understand the customer and visitors in much more advanced ways. This enables the organizations to personalize the content and recommend the products and solutions in a 1-on-1 manner which leads to much higher conversions and revenue


    The goal of the document is to lay out the basic building unit indices and KPIs using which we can target customers a lot better. But this is not the end, in fact, it just begins the journey to a “1-on-1 personalization and recommendation” for the organization where the underlying goal is to provide a much-improved customer experience and offer higher values. Once we get what’s there in this document, then we need to use Stream Processing and Even ingestions for ETL, data enrichment, and CEP (complex event processing). Further, we will need to put the Graph structure in place to operate in a highly “context-aware-environment” for personalization and recommendation. Let’s first look into the basic part of the bigger recommendation system here, and then in the next blog we will go into stream processing and Graph

    REAN Model is defined as follows.

      • Develop Reach
      • Engage
    • Activate and
    • Nurture model

    REAN Model Does Two Things Very Well

    • Firstly, it gives you a very clear indication of the measurement challenges you might have when breaking your strategy down into its component parts.
    • Secondly, it can be used to help you define a measurement strategy. You could develop KPIs around each activity (Reach, Engage, Activate and Nurture) and then combine the metrics as matrices of each other.

    From a high level, this is how the REAN model is different components are defined.


    • Traffic sources
      • Search Engines
      • Ads, Campaign
      • Email, Newsletter
      • Internal links
      • Partner sites
      • YouTube, Video, Video banners
      • Blog
      • PR
    • SEM
    • SEO
    • Brand Awareness – traffic coming from logo, company names, product names,
    • Seeding – from opinion leaders, reviews, articles


    • Shopping carts
    • Self-service process – ex; SaaS product sign up etc
    • Any creatives
    • User segmentation based on behavior
      • Session length and depth      
      • Separate users based on their likes and dislikes [ based on session length and depth]
    • Click depth – average click depth and corresponding user’s segment
    • Duration – length of time spent on a website
    • Offline engagements – relevant for offline stores, etc.


    • Also, can be interpreted as a conversion
    • Purchases
    • Downloads of software or documents
    • Activation is typically what reach, engagement, and nurture KPIs are measured against.
      • B2B, B2C, Media, Customer service, Branding


    • CRM
    • Follow-ups – emails, community
    • Most importantly
      • Personalization & customization
      • When users, customers are back, how we interact with them
      • Cookie, user management, etc
      • Recommendations
    • Recency – Measure of time elapsed between your visitor’s last visit and his/her current one


    • Click depth index [# of the session with more than n page views / #total sessions]
    • Recency index [ # of the session with more than n page views in last m weeks / #total sessions]
    • Duration index [ #num of sessions more than n min / #total sessions]
    • Brand index [ # of sessions originated directly (no ref URL) / total sessions]
    • Feedback index
    • Interaction index [ # of sessions where visitor completed any or tracked activity / #total sessions]
    • Loyalty index = ∑(Ci+Ri+Di+Bi+Fi+Ii) per visitors and select top k
    • Subscription index [ #num of visitors with content subscribers / #total visitors]
    • Content page view index [ count of content page views/total page views]
    • Internal banner index [ banner clicks / total clicks]
    • Content consumption index [ #page views per content/ #page views]
    • System perf idx [ #views from per system / #page views]




    MetricVisitor AcquisitionConversion to opportunity
    Conversion to sale

    Customer Retention & Growth

    Tracking metricsUnique visitors
    New visitors
    Opportunity volumeSales volumeE-mail list quality Transaction
    churn rate
    Performance drivers
    Bounce rate
    rate New visit
    Macro-conversion rate to
    opportunity to micro
    conversion rate
    Conversion rate to sale
    conversion rate
    Active customers % (site & email
    Repeat conversion rate
    Customer-centric KPIsCost per click,
    sale Brand
    Cost per opportunity or
    Cost per saleLifetime value Customer loyalty
    Business value
    Audience sharesTotal orderTotal salesRetained sales growth and
    StrategyOnline and
    and reach
    Lead generation strategyOnline sales
    Retention, customer growth
    campaign, ads,
    Personalization &
    TargetingTargeting Churn rate etc


    There are the following types of Web Analytics Metrics

    • Count
    • Ratios
    • KPIs – either count or ratio
    • Dimension – segments
    • Aggregates
    • Etc.

    Business questions that we need to answer through KPIs

    1. What is the best source of traffic in terms of volumes and sales?
    2. Where are the visitors coming from? [ top k places]
    3. Which channel is most productive? [ top k channels]
    4. Which channels are overlapping?
    5. Which landing page converts best [ top k landing pages]
    6. Do registered users buy more?
    7. Most searched pages [ top pages]
    8. How many downloads?
    9. What’s the value of download for different items [ top downloads by value]?
    10. Avg response time for lead response?
    11. Internal search stats
    12. How engaged are our visitors?
    13. What are the Top paths to our website?
    14. How are visitors finding the site?
    15. What is the cost per conversion (per campaign?)
    16. Users by location
    17. How many people don’t get through a shopping cart?
    18. What are the search keywords?

    Page bounce rate – left from a landing page

    • Hourly
    • 15% deviation – Alert

    Page time index – time spent on the page / total time spent on the site

    • Hourly
    • 15% deviation – Alert


    • By paid traffic [ Reach] – campaign, ads, banners, etc.
    • Unpaid traffic [ Engage]
    • By location [ Engage]
    • By search phrase or keyword [ Engage]
    • By site pages or behaviors [ Engage]
    • By system vars [ device, browser, etc.] [ Engage ]
    • By conversion
    • By loyalty – repeat visitors, registered, recency, etc.

    Attributes for basic segmentation

    • Visits
    • % Add to cart
    • Conversion rate [ #confirmed conversion / # total visits]
    • Engagement conversion rate [#confirmed conversion / # total engaged visitors]
    • Marketing cost
    • Cost per visit (CPV)    
    • Visitor volume ratio [ num of visitors from a source / total visitor]
    • Video engagement rate [ count of num of times video played / num of visitors to the page]
    • Cost per add to cart
    • Customers
    • Average cart value
    • Shopping cart abandonment rate
    • Page time index
    • Visitor engagement index
    • Content page view index [ count of content page views/total page views]
    • Internal banner index [ banner clicks / total clicks]
    • Content consumption index [ #page views per content/ #page views]
    • System perf idx [ #views from per system / #page views]
    • Cost per acquisition [ cost of referring source / num of conversions]
    • Sales

    Attributes for Behavior segmentation KPIs

    • Page views per session
    • Avg session time

    Nurture rate

    • Repeat visitor index [ # of repeat visitors / # of visits]
    • Email perf index

    BangDB is designed to ingest and process high-speed data to extract the intelligence and apply them to ongoing operations for better operational value. BangDB comes with Stream processing, AI, and Graph processing with unstructured data analysis in real-time. Take BangDB free of cost and start building applications

    Relevant Readings

    How to mitigate security risk using BangDB

    Security risk is everywhere and it has been growing rapidly while we try to mitigate security risk at the same time. The fraudsters are always a step ahead of the curve and come up with new ideas for attacks while we are busy handling the older ones. To mitigate security risk, it requires all of us to innovate faster and prepare in a much more advance and modern manner. Most of the time we keep solving the older problems due to the enormity of challenges here and forget about preparing for the potential upcoming attacks. The sheer definition of the problem is not available most of the time, the tools in the market are sparse and siloed, and the concepts are available but implementations are limited. The core of the solution lies in the ability to scan every single event in context and use modern methods to not only do forensic but be predictive to avoid the repercussions

    The cybersecurity threats have changed in three crucial ways in the recent past:

    • MOTIVE: In the past viruses were introduced by curious programmers. Today cyberattacks are a result of a well-executed plan by trained militaries in support of cyber warfare.
    • SPEED: The potential rate at which the attack spreads has also increased and can affect computers all over the globe in seconds.
    • IMPACT: the potential impact has increased manifold due to the wide penetration of the internet


    Continuous and relentless: Threats may come from any place, any system, and the most unlikely of places. Therefore, we must capture and analyze all data (and not just samples). Hence stream processing in a continuous manner is critical where all events/data are analyzed with as low latency as possible. Most of the tools in the market are batch processing tools, they miss the pattern at the boundaries of the batches, therefore not suitable for such use cases

    Non-atomic in nature: Threats may not necessarily be atomic in nature; it may arrive in small packets over a period from many different sources. Therefore, by just looking at a single packet or event we can’t perceive the thread. We must analyze these arriving data packets in the state-managed system with a continuously moving window that can see the pattern over a period. Also, we need to link data points to capture the essence and context

    Unpredictability: Few threats may have known or constant signatures, which we could identify using some computations in the deterministic and absolute manner. However, several threats are extremely hard to be captured in this way as they are designed to miss the regular known or anticipated structure. Therefore, we must use AI to predict some of these scenarios continuously on stream data

    High-speed processing: The speed of data is so high in some cases that existing tools in the market would sample and then process. We know that this is too open and risky. We must capture and process all data. This means we must have a system that has very high performance in reading and writing. The high throughput data store is desired in such cases

    Linear scale: Data volume would be high as we need to process and store all data. A large scalable system would be needed to achieve this. We need a linear scale to ensure the data ingestion and processing work uninterrupted while system scales

    What BangDB does do? – It enables a Predictive instead of a Forensic approach with high speed in a scalable manner to mitigate security risk

    BangDB ingests and processes the security telemetry information at extremely high speed to make it easily accessible for advanced computation and analytics. It further leverages the following to achieve a predictive vs forensic approach

    • Advanced statistical and data science models for high-speed anomaly detection
    • Real-time ingestion and stream processing to enable continuous threat analysis
    • Machine learning models integrated with stream for predictive threat detection
    • Graph with stream for interlinking of data points for richer pattern detection
    • Handle all kinds of data in a single place, text, images, videos in a joined manner

    What are the typical steps that BangDB takes to tackle this?

    STEP1: Advanced Threat Detection

    Need to leverage BANGDB to combine and contextualize incidents from multiple big data disparate sources for continuous near real-time streaming detection, capturing incidents that are often missed in batch-based technologies. 

    STEP2: Link data

    Enrich data with a Graph model to capture the “context” rather than just isolated events which do not provide enough information. Further, integrate Graph with Stream processing such that the linking of data and context capturing in automated and continuous

    STEP3: Complex event processing

    Find anomalies, and patterns using complex event processing (CEP). This allows users to define a certain pattern that is so complex in nature that can’t be run on typical RDBMS or other databases. The pattern identified here are absolute in nature and with 100% confidence

    STEP4: Predict and don’t depend on forensics as much as possible

    Artificial intelligence enables the identification of never seen threats, malware, and infiltration techniques. Using AI, build a comprehensive security score leveraging behavioral modeling and stochastic anomaly detection. Kill chain incidents are prioritized based on potential impact, key users, and critical assets.

    STEP5: Take automated action

    When an anomaly or pattern is detected, take action in an automated manner. This means timely action which could potentially result in saving time and resources and in many cases avoiding the situation itself

    STEP6: Track threat propagation

    Leverage BangDB’s ability to ingest and analyze immense amounts of data to track threats and their propagation across time and space through a near real-time relational-graph view of the entire network

    STEP7: Visual threat hunting

    Sophisticated threat hunting tools within the Security Intelligence platform to allow the SOC staff to effectively hunt, validate and remediate potential threat incidents surfaced by the product. Analysts can self-assemble new threat hunting workflows using building block modules for ingestion, enrichment, and analytics on a security playbook interface.


    In the end, no amount of effort and tools can make us completely insulated from these security threats, there is no complete immunity that we can develop for such things. However, we can at best be prepared for such attacks and try to avoid them and mitigate security risk as much as possible. And in case of some attacks, we can try and minimize the damage. An additional set of tools would never hurt, they can probably add more value and make the situation better, hence it is recommended to try BangDB to make the castle bit more impregnable

    Download BangDB for free and get started, BangDB is one of the “fastest databases” in the market. It performs 2X+ when compared to some of the most popular databases like MongoDB, Couchbase, or Redis.

    Please see more related articles on the following;


    Architecture of modern stream processing platform for realtime data analytics

    Humans to Machine – Shift of data source

    There is a rapid shift happening at the data level as we speak. More and more data is being created by devices, which are fast-moving and contain very high value but perishable insights. This shift is demanding a new architecture of stream processing platform for real-time data analytics. Data has been growing exponentially. We have more data streaming through the wire that we can keep them on disk from both value and volume perspectives. These data are being created by everything we deal with on daily basis. When humans were the dominant creator of data, we naturally used to have less amount of data to deal with and at the same time value used to persist for a longer period. This in fact holds true now as well if humans are the sole creator of the data.

    However, humans are no longer the dominant creator of the data. Machines, sensors, devices, etc. have taken over a long time back. The data, predominantly, are being created by machines with humongous speed, so much so that in the last two years we had 90% of the data created since the dawn of civilization. These data tend to have limited shelf life as far as value is concerned. The value of data decreases rapidly with time. If the data is not processed as soon as possible then it may not be very useful for ongoing businesses and operations. Naturally, we need to have a different thought process and approach to deal with these data

    Why stream analytics is the key to future analytics

    Since we are having more of these data streaming in from all different sources, that if combined and analyzed then a huge value could be created for the users or businesses. At the same time given the perishable nature of the data, it’s imperative that these data must be analyzed and used as soon as they are created

    The value of data is maximum when it’s created, the streaming data is perishable in nature, needs to extract the insights immediately

    More and more use cases are being generated that need to be tackled to push the boundaries and achieve newer goals. These use cases demand the collection of data from different data sources, joining across different layers, correlation, and processing across different domains, all in real-time. The future of analysis is less about understanding “what happened” and more about “what’s happening or what may happen”

    Few examples Use cases in this context


    Let’s analyze some of the use cases. Consider an e-commerce platform that is integrated with a real-time stream processing platform. Using this integrated streaming analysis of data, it could combine & process different data in real-time to figure out the intent or behavior of the user to present a personalized offer or content. This could increase the conversion rate significantly or reduce to eroding customer engagements. It could also have better campaign management to yield better results for the same spend

    Data Center

    Think of a small or mid-size data center (DC) that typically has many kinds of different devices and machines each generating volume of data every moment. They typically use many different static tools for different kinds of data in different silos. These tools not only restrict the DC from having a single view of the entire data center but also works as a BI tool. Because of this, the issue identification in a predictive or real-time manner doesn’t happen as a result firefighting becomes the norm of the day. With a converged integrated stream processing platform, DC could have a single view of the entire DC along with real-time monitoring of events, and data to ensure issues are caught before they may create bigger problems. A security breach could be seen or predicted much earlier before the damage is done. Better resource planning and provisioning could be done by analyzing the bandwidth usage and forecasting in near real-time


    The entire IoT is based on the premise that everything can generate data and interact with other things in real-time to achieve larger goals. This requires a real-time streaming analytic framework to be in place to ingest all sorts of unstructured data from different disparate sources, monitor them in real-time, and take actions as required after identifying either known patterns or anomalies

    AI and Predictive Analysis

    AI and predictive analytic means that the data is being collected and processed in real-time otherwise the impact of AI could only be in understanding what happened. And with the growth of data and types, it will be prudent to not rely solely on what has been learned so far in the hindsight. Demand will be in reacting to new things as it’s seen or felt. Also, we have learned from our experiences that a model trained on older data often struggles to deal with newer data with acceptable accuracy. Therefore, here also the real-time stream processing platform becomes the required part rather than a good to have a piece

    Limitations with existing tools or platforms

    There are two broad categories in which we can slot the options available in the market. One is an appliance model and another one is a series of open-source tools that need to be assembled to create a platform. While the former costs several millions of dollars upfront the latter requires dozens of consultants for several months to stitch create a platform. Time to market, cost, ease of use, and lack of unified options are a few major drawbacks. However, there are bigger issues to be addressed by either of these options when it comes to stream processing and here, we require a new approach to solve the problems. We can’t apply older tools to newer, future-looking problems, otherwise, it will remain a patchwork and would not scale to the needs of the hour

    Challenges with Stream Processing

    Here are the basic high-level challenges when it comes to dealing with the stream of data and processing them in real-time.

    • Deal with the high volume of unstructured data in an extremely low latency manner
    • Avoiding multiple copies of the data across different layers and over a network as well
    • Optimal flow of the data through the system
    • Partitioning the application across resources
    • Stream processing data in real-time
    • Data storage in a unique manner and most suitable manner
    • Processing data and taking actions before it’s persisted
    • Remain predictive rather than only forensic or BI tool
    • Ease of use – we should not code for months before seeing the results
    • Time to market – off the shelf such that the app can go to market in a short time
    • Deployment model – hybrid. From within device to LAN to Cloud – all interconnected

    Most of the options in the market suffer from these bottlenecks. Let’s take a few examples.


    Spark follows the map-reduce model philosophically although in a much more efficient manner. However, it still deals with batches. Spark deals with micro-batches of a given size with a given batch interval. It has several problems when it comes to aligning with stream processing, in fact, its approach is the antithesis of stream processing

    • A Micro or macro batch is not important as long it’s a batch. Consider a macro and micro-batch both with an equal number of events because of different speeds of the data. The concept of the batch doesn’t align with stream processing where processing every single event is important
    • Processing starts when a batch is full. This fails the premise of processing events as it comes
    • Stream processing happens within a moving or sliding window. Windowing with batches is not possible
    • When the batch processing time is more than the batch interval then the backlog only grows, this coupled with persisting data sets only aggravates the situation

    Kafka + Spark + Cassandra

    This model typically uses 5 or more distributed verticals, each containing many different nodes. This increases the network hops, and data copy, to a great level which eventually increases the latency. Scaling such a system is not trivial as we have different dynamic requirements at a different levels. Further, the cost of adding new processing logic is significantly higher than a simple BI tool where things could be handled using a dashboard. Finally, it requires a large team and resources which increases the cost. This can hardly be deployed for a scenario where sub-second latency is desirable


    AWS kinesis at best is equivalent to Kafka, a distributed, partitioned messaging layer. Users still must assemble, process, store, vision, etc. layers themselves

    Why BangDB

    We need a platform that is designed and implemented inhomogeneous manner, towards a single goal of process stream data in real-time, which avoids all the above pitfalls and remain immune to future requirements, and scales well for higher load and volume of data

    BangDB has tried to address most of the above-mentioned problems by designing and building the entire stack from the ground up. Here is a brief introduction of the BangDB platform in the light of the known issues identified

    Deal with a high volume of unstructured data

    BangDB has built a high-performance NoSQL database that scales well for a large amount of data. Also, it follows the convergence model to scale linearly. Scaling “single thing” vs “many things” addresses the problem to a large extent. BangDB also follows the FSM model and implements SEDA to achieve a cushion against the sudden surge in data

    BangDB follows a true convergence model for higher performance, ease of management, and massive linear scale

    Avoiding multiple copies of the data across different layers

    BangDB removes all silos. The silos not only add the latency but also forces data to be copied across different verticals

    Optimal flow of the data through the system

    BangDB processes the data before it reaches the disk. This is opposite to most of the systems in the market. Further BangDB also avoids post-processing as much as possible, almost negligible. All these happen when data reaches a node, therefore there are no network packet hops for the data

    Partitioning the application across resources

    Convergence allows BangDB to partition application and data and all other resources in a single-dimensional manner. This enables the partitioning of space rather than the partitioning of a different set of spaces. Therefore, it naturally enforces optimal use of added capacity and resources which is otherwise is difficult to predict and provision

    Processing streaming data in real-time

    BangDB process every single event rather than micro or macro batches. Hence, the data is updated in real-time, a pattern is identified in real-time, and insight is also served to the application in real-time. Most of the streaming real-time use cases emphasize the need to process data in a sliding window, BangDB provides a configurable sliding window within which most of the processing happens

    True stream processing with continuous sliding window. Most of the operations happen within the sliding window

    BangDB follows the reverse of Map Reduce to achieve very high performance for reads. This is done by avoiding all sorts of post-processing of data and keeping the data in a format needed by the user

    Data storage

    BangDB stores both raw data and the extracted insights or aggregated data within the system. It’s a persistent platform hence it could store as much data as required. However, most of the time it is critical to process data in real-time and then push it to an offline system for deeper analysis. Therefore, BangDB connects with Hadoop and other long term storage frameworks as well

    BangDB has an IO layer that uses SSD as an extension of RAM rather than a replacement for File System, thereby allowing out-of-memory computations and data handling without severe degradation in performance

    BangDB also uses SSDs in totally different ways to achieve cost-effectiveness and elasticity. SSDs are typically used by others as a replacement for file systems (or HDD) where the gain is limited and if not used properly life and performance go down as well. BangDB has written software to mimic SSD as an extension of memory by which the performance can be increased multifold and also the cost-effectiveness can be achieved to a great extent

    Remain predictive rather than only forensic or BI tool

    BangDB aims to be predictive. BangDB processes and analyses data in both an absolute and predictive manner. It uses complex event processing for absolute pattern recognition. It also uses supervised and unsupervised machine learning for the pattern or anomaly detection. BangDB platform provides simple ways to upload and train models. It also integrates with “R” for data science requirements

    Ease of use

    Both appliance and open-source models provide a technology platform where the new analytic application or processing code must be developed and deployed on the production system. This requires a typical test for production DevOps and release management. BangDB provides an integrated dashboard to make the platform totally extensible. Users can perform all actions using the dashboard without ever developing code or application. Further BangDB has developed pre-baked apps in different domains and uploaded them to its AppStore such that users can simply take those apps, configure and start dealing with real-time insights.

    Time to market

    BangDB platform is hosted on a cloud as a SaaS model along with AppStore with several solutions. This allows users to start within an hour or even less. There is no stitching time, deployment time, or even development time, everything is ready to go with a set of clicks

    Deployment model

    BangDB can be deployed within the device for state-based computations including CEP and ML processing. Further BangDB could be in LAN and Cloud too. All of these could be interconnected for supercharging orchestrations. BangDB has a subscription model and users can start within a minute using BangDB SaaS and then grow as needed. Get started with BangDB by simply downloading it

    Further Reading: Also check out two other blogs on similar topics which might be useful.


    Why a modern data processing platform must have Stream, AI, and Graph at the core

    Modern use cases are demanding modern ways to deal with data. These use cases have a lot in common. For example, they all have fast-moving data at the core, mostly coming from devices. They all need modern processing data in a lot more contextual and predictive manner which will require data to be linked in graph structure coupled with many AI models working together. The era of assembling a few pieces from open source to build such systems is gone already, most of the tools and systems in the market were created decades ago. Traditional architecture is failing to cope with the requirements increasingly. This is a great time to come up with the architecture and system which is designed from the ground up to address such problems. BangDB is a converged data platform that natively integrates AI, stream, and Graph processing within the NoSQL database.

    Please see this blog which covers some of these aspects as well in a nice manner

    Let’s look at the simple fact. In just a few years, the majority of the data will be generated by the devices. In less than a decade, device data will grow from being less than 2% to over 50% of the global data sphere [ Ref: IDC]. This change has been extremely rapid compared to the natural growth of the other systems and tools/technologies in the market. This has created an impedance mismatch and a huge gap when it comes to tackling the emerging or even the traditional use cases in the modern context.

    Data will mostly originate from devices in just a few years, which means device data will override the use cases

    By nature, these data move fast and carry lots of valuable insights which should be mined as soon as possible for higher value extraction. Graph structure gives these data much-needed context which could be leveraged in an absolute or probabilistic manner. Native AI provides real-time prediction support for various streaming events. And, since data may have many different formats, therefore, we must have a mechanism in place to deal with unstructured data in a scalable manner.

    Some of the use cases for modern data processing would be.

    1. Smart device data analysis for thousands to millions of such devices in a real-time and predictive manner for operational efficiency by harnessing local / edge data
    2. Vehicle sensor data analysis for finding or predicting anomalies, interesting patterns for safety, security, and maintenance of the vehicle
    3. Satellite image analysis for finding the signature of interest. UAV, SAR type image analysis for finding changes in topologies of any given area, from a security, agriculture, or climate analysis point of view
    4. Operational efficiency for Integrated large-scale system using various logs, sensors, devices, apps, systems, services, etc. data, all at the same time

    All the above use cases involve a plethora of log/data streaming into a single system (for linking, correlation). They also require real-time stream and Graph processing and analysis. Building several machine learning models for online predictions and mechanisms to take action. On top of it, we must do all these with sub-second latency as these data have quickly diminishing intelligence value.

    What are the problems with the existing systems/ architecture?

    1. There is a lack of convergence at the system level. If you see it from a high level, the problem is posing a challenge of convergence, by bringing too many disparate requirements to the same place. Therefore, from a solution perspective as well, we must converge to offset the problems and their challenges. Such as, we must natively integrate modern data procssing like Graph, Stream processing, AI, and unstructured data handling within the data platform. Whereas the reality is that we often deal with several silos instead of one unified platform
    2. Stitching too many tools or systems may not be effective. First, it may take several human resources for quarters to years to build such a system. Then enabling use cases on top of it may not be very effective. Further, such a system may not fulfill all the basic requirements. For example, copying data across multiple sub-systems, and the network overhead of bouncing packets from one place to another would simply make the latency unacceptable. Such systems would enforce many different constraints from different dimensions, for example, data could be kept in memory which will either make the proposition brittle and/or very costly
    3. True edge computing is required for most of these use cases. Which tells us that we must embed part of the system within the device itself. What it means is that we must have a scenario where part of the same system is deployed within the device (ARM processors) in an embedded manner where it can deal with hyper-local data analysis. The same system is deployed on the LAN or Cloud (or both) which is interconnected with all the embedded deployments within several devices. This forms a hybrid compute network which is cooperating, shares different responsibilities, and collaborates to achieve a single goal (or set of goals). Most of the existing systems or platforms in the area would not be able to do so, thereby defeating the basic need of the use cases

    How does BangDB fit into such a scenario?

    BangDB is a converged database that has natively implemented following

    • Modern Data Processing
    • Stream Processing
    • Graph Processing
    • Machine Learning
    • Multi-model support


    Moving away from n-tier to space-based architecture is best suited for future anonymous and distributed computing

    From an architectural point of view, BangDB follows the convergence model which means instead of splitting the system into multiple pieces and scaling different pieces separately, it follows space-based architecture where all necessary pieces are always in a single space, and we scale the spaces as we need. In other words, instead of 3-tier or n-tier architecture, in space-based architecture, we always deal with several units of computing (or machines) where each unit of computing contains all necessary components.

    This avoids extra copy of data, network hops, distribution overhead for splitting requests, and combining responses which result in higher latency, computational overhead, and complex management apart from high cost and resource guzzling procedure. True convergence allows us to scale the system linearly without much overhead along with hyper-fast processing speed and ease of development and management.

    BangDB is a full-fledged database as well, it implements some of the advanced features of a mature database like transaction, write-ahead log, buffer pool/page cache, indexes, persistence, rich query support, etc. BangDB is available free of cost, download it and start building modern apps with ease

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

    E-commerce business needs to collect data from various sources, analyze them in real-time and gain insights to understand the visitor’s behavior and patterns which will allow the company to serve the customers in contextual and better ways to improve the conversion rate. A real-time data platform is needed of the hour which can combine stream analytics with Graph and AI to enable predictive analytics for better personalization for the users which significantly improves the e-commerce sales by 2X or even more. Therefore predictive real-time data platform is needed to boost the e-commerce sales

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

    Read a good article on this here to get more info about it

    Some of the general facts (statistical) which relate 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%
    • The majority of the data (more than 60%) is not even captured and analyzed for visitor or customer analytics
    • Less than 20% of data is captured in true real-time, which diminishes the potential of relevant and contextual personalized engagement with the visitors and hence leads to 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 on a 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 the following for predictive analysis

    • Personalized content in a 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 modeling, and machine learning to analyze current and historical data and make predictions about future events and boost thier e-commerce sales.

    With Predictive analytics, e-commerce businesses can do the following

    • Improve Lead scoring
    • Increase e-commerce sales
    • 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 is a brief list of use cases 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 a 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 1 on 1 basis, leads to much deeper engagement and higher conversion
    4. Projecting much relevant and usable LTV for every single user/visitor could lead to better decision making for personalization or targeting or offering
    5. Inventory prediction for different product/versions/offerings 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 the next campaign. Throw the right campaign to the 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, e-commerce 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 profit potential.

    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 a limited set of data
    2. Need to capture all data truly in a seamless and real-time manner
    3. Store different entities and their relationships in a graph structure and allow rich queries
    4. Need to auto-refresh and retrain the 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 easily and speedily in different contexts or domains as required

    Gaps with the existing systems in the market

    1. The majority of systems (GA, Omniture, etc.) can ingest a limited set of data. It’s virtually impossible to ingest other related data into the system for a better scoring model. Also, with these systems, it’s difficult to extend the ingestion mechanism for a custom set of data, coming from totally different data sources than just the clickstream. Therefore, there is a need for a system that can ingest heterogenous custom data along with typical CS data for better results and higher efficiency
    2. Most of the systems ingest data with latency not acceptable from the solution perspective. Forex; GA allows a limited set of data ingestion in real-time, the 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 the 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 in poor and non-relevant prediction. Therefore, there is a need to have an 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, the system must scale well. The speed and volume of data coupled with model preparation, training, deployment, etc. make it very difficult for such a 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 create a huge block in terms of scaling the system. Therefore, there is a need to have a platform that hides all these complexities and provides a simple mechanism to scale linearly for a higher volume of data, more num of websites, locales, or simply for a larger number of use cases as things move forward.
    5. Most of the system acts as a black box allowing lesser control on deployment and access to data in a larger sense. This results in 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” in the same place. Integration takes lots of time, resources and is sometimes not feasible at all
    7. Also, it provides huge restrictions in terms of dealing with ML since the models and their metadata 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 that allows us to have greater control of various processes along with the ability to reuse and extend 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. Clickstream, 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 a small fraction of data in real-time. This limits the scoring efficacy as real-time data is the basis for proper analysis. Omniture captures most of the data, but they are available for analysis in a few minutes rather than in a few milliseconds. Proper personalization or any action is best taken as soon as possible, not after a few minutes or hours

    Accelerated time to market

    BangDB comes with a platform along with a ready solution that implements the use cases as needed and has the 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 are 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 a simple manner

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

    • Integrated streaming system to ingest all kinds of data as required for analysis in real-time. Build your apps/solutions or extend the existing ones as needed by 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 trained and retrained frequently as more and more data arrives. It starts producing output from the model within a week 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.

    Install BangDB today and get started

    To check out BangDB, go ahead download it for free

    Quickly get started here

    Checkout the BangDB benchmark

    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. 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 NoSQL 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.

    Other Benefits of Hybrid NoSQL 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 NoSQL 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 NoSQL 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.

    hybrid nosql architecture iot

    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.

    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 a 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 daily, 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

    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, that 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.

    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.

    NoSQL Database 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 times and to handle different needs.

    It’s kind of like how you wouldn’t expect a computer from the early 90s 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 that 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 a 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 on 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 in 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 have 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 represent 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 by 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: