NoSQL + AI + Stream + Graph

Real-time data platform for building modern apps

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Ampere - BangDB on Cloud

Build modern application with BangDB data platform on cloud : No-Code Platform

  • Complete BangDB stack on cloud with AI, Graph and Stream Processing
    • Ingest and process timeseries events, analyze, take actions
    • Build Graphs, Run Cypher, do Data Science on Graphs
    • Build and deploy model, do predictions
    • SQL like query for database and streams
  • Get started with off-the-shelf stack, fully managed
  • Interactive dashboard for building apps without coding
  • Experience the convergence, high performance, and speed

NoSQL for building NextGen use cases

IOT monitoring & analytics
App & Infra analytics
Pattern & anomaly detection
Community | Enterprise

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Designed for data trend, built for developers

Real time streaming data
Native AI for train & prediction
Graph and cypher for linked data
Interactive CLI & dashboard

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Multi Cloud to Edge to Embedded

SaaS - Apps and database
Embedded for edges
Edge | On-premise | Cloud
Device, log, IOT, Clickstream

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Top Global NoSQL & Data Science Vendor

Stats and Reports | Analytics India | Gartner

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Multi Model Database
Aligns with the current and future need

Today’s use cases require different kinds of data to be ingested, processed, and queried at the same time for a given problem. BangDB supports most of the useful data formats to allow user to solve the problem in a simple manner.

BangDB is a multi-model, embedded, distributed, high performance, analytical, time-series NoSql database written in C/C++ and designed from scratch for solving contemporary and future problems in simple and easy manner which otherwise requires huge amount of time and resources.

BangDB natively integrates the necessary pieces together to align with the data trend

Just get BangDB community or enterprise version and build your apps and solve complex problems in time accelerated manner, leave heavy lifting to the BangDB

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Stream Processing
Real-time continuous data intelligence

Rise of real time data pushes for real time streaming and predictive data analytics for advanced and optimized business operations.

BangDB natively supports stream processing which allows it to continuously ingest and process data for real time predictive data analytics. Use cases such as continuous operational intelligence, infrastructure/IOT monitoring, log analysis etc. requires streaming data handling.

BangDB allows users to define Schema, aggregation, groupby, referral, filter, join and complex event processing, forecasting for tens of thousands of streams in high performance manner. Sliding window could be created as native construct, model could be trained and deployed on streams, and most importantly can automate all of the above with simple configuration

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Integrated AI
Train models and predict within BangDB

BangDB has inbuilt AI capabilities and provides APIs and mechanism to deal with model training, testing, deploying, prediction and measurement

BangDB allows users / data scientists to deal with Machine Learning, Deep Learning, Information Extraction in a simple manner. BangDB also provides large data storage feature to store model files, training test files and then deploy these in seamless way

Most of the use cases use AI in one way or the other. BangDB ML pipeline is simple and powerful which can run in autonomous manner to enable real world scenarios in true sense

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High performance
2X+ compared other leading databases in the market

Performance is one of the key design goals for BangDB. It has very high throughput for both write and read, as it's required to handle high speed data in an efficient manner.

BangDB implements several things to ensure high performance in various different conditions. Buffer pool, IO layer, true concurrent implementation are few elements which makes BangDB one of the highest performing NoSQLs in the market.

BangDB performs 2X+ with most of the other popular NoSQLs.

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Graph and Cypher
Data and relationship for linked events

BangDB works as graph store as well, storing data along with relationships. Many use cases require data to be interconnected to efficiently perform some queries or find some root cause or simply to connect two disparate set of events for better intelligence extraction. Moreover, many ML algorithms are based on graph model too.

BangDB stores triple data in a special way with natively built-in Graph-Store where the connections between nodes are already made and indexed. This allows BangDB to run queries way faster than other databases. Further finding all paths, shortest paths between two random nodes separated by thousands of degrees or more are as efficient as any other query.

Cypher can be used to interact with the graph, BangDB alters it a bit to make it simpler and to allow some more core functionalities

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Complex Event Processing
Finding patterns within data in real-time & absolute manner

CEP gives users the ability to extract meaningful business intelligence out of raw data. As real-time data becomes more and more abundant, CEP is a key component of event-driven architecture.

While most of the systems deal with point rule based eventing, it’s not sufficient to identify real world relevant patterns. CEP allows users to deal with state based , complex data pattern extraction from the raw streaming data in continuous and real-time manner to find anomalies, security threats, frauds, opportunities etc. and take appropriate actions as required.

BangDB implement scalable high performance CEP as part of the stream processing sub-systemt. It allows users to tell BangDB about the set of patterns that he/she is interested in , and BangDB keeps finding it continuously as data streams in. Upon finding, BangDB can further take some actions as configured by the user.

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Running Statistics
Hyper fast query | Anomaly detection | Forecasting | ML attributes

One of the major requirements for stream processing is to find patterns, anomalies etc. BangDB uses CEP, ML to do the same. However, all of these methods may require statistical aggregations for processing such rules. Since finding patterns in real-time task hence we need these statistical mesures as well in real-time.

BangDB maintains running statistics for various attributes in the stream which can be utilized by any such methods or queries at run time in real time. This also adds to high performance that BangDB achieves for stream processing.

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ACID transaction as part of configuration

BangDB supports full ACID transactions for embedded and client server model. To achieve high performance and concurrency with full ACID transaction, the db implements OCC (optimistic concurrency control). Simply start the server with transaction mode ON and use simple APIs to club operations within transaction boundaries.

Transaction txn = new Transaction();
// db operations

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Faster and richer query to solve modern problems

Create multiple indexes on structured or non-structured data for quicker and efficient access. The user may create as many indexes as required and DB provides plenty of options to configure each indexes if needed to suit the requirement.

These indexes enables powerful and high performance query on the data which is multi fold faster than many other leading dbs in the market

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Intelligent light weight data ETL agent

The continuous collection and streaming of data are complex, hard and of utmost importance when it comes to analyzing the data, even more so when we deal with all sorts of data.

BangDB provides a high capacity, low footprint agent for the job. It is a service that can run locally or remotely, and then just point it to data source and it will start the job. Users have lots of control on the agent and can even clean, enrich, transform (structure) the data at the agent level itself.

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Interactive command line interface for BangDB

BangDB Command Line Interface (CLI) allows user to interact with the BangDB in easy and efficient manner. User may perform almost all task using the cli.

For most of the db queries, SQL like language is used. For graph related queries, Cypher syntax is supported. SQL like language is supported even for streaming and ML related activities, like training, prediction, deployment of models etc.

CLI aims to simplify the interaction with the db hence in several places wherever needed, a cli initiates a workflow with simple question and answer session to effectively conclude the process.

Further CLI can be used for admin purposes as well related to server configuration, replication master and slave switching, agent configuration and settings.

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Rich query support
Document | timeseries | key-val | cypher

To support rich query, BangDB provides several different index support such as primary, secondary, nested, composite indexes, which allows users to run powerful queries for faster access to data. It also supports text indexing (reverse) for search capabilities.

It uses SQL like query support from the CLI while allows developer to leverage DataQuery abstraction for writing queries.

For streaming, BangDB allows queries to execute in continuous & autonomous manner to look for certain patterns/ anomalies in the data and take actions accordingly

For graph, it uses Cypher from the CLI. From client users can use the simple API for storing and querying the data (triple)

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Grafana for charts and dashboards

BangDB supports REST APIs which could be used for data visualisation using grafana.

The REST API is available DB, Stream, AI, Graph and BRS (resource server), therefore users can do almost every operations (CRUD) using these apis.

Users may use CLI for creating db resources, streams, schema, tables, indexes etc. while grafana can be used for data visualisation

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Ampere : No-Code Platform
Create Apps, process data and visualize

BangDB offers is an interactive dashboard to ingest, process and visualise data. Users can create the solution of app just following the workflows provided for different activities

The dashboard allows users to define and do ETL, process, train models, find anomalies and patterns, take actions, create dashboards and charts for monitoring and visualisation etc. Further all these can be wrapped inside an App and published for further distribution.

App store offers set of apps which can be installed with a single click and get started immediately

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“IQLECT has worked on initial ontology project for Cisco product and support pages to help automate processes in the CRM team. Together we deployed the platform along with solution within Cisco in Q3 2017 and we are exploring deeper integration with the platform for more use cases. This has also been presented to Cisco leadership who are very impressed with their overall work. Their approach towards solving the data-related problem is unique and that combined with their home- grown converged platform only allows users to enable complex use cases effectively and in a short period of time. An integrated ML/IE layer gives our solution a much-needed edge. We foresee lots of potential going forward and look forward to a much deeper engagement with them.”

– Amit D , CISCO

“We partnered with IQLECT to develop an end to end marketing automation and lead generation app which increased the conversion rate by 2x and generated 15% more incremental leads. IQLECT is the next best thing for the next best action. IQLECT purpose-built NoSQL, BangDB brings a convergence of AI and streaming to enable hyper real-time scenarios which are needed to solve such advanced problems. We did not find any other vendor who could match the business outcomes generated by IQLECT.”

– Haider Khan , Accenture

“Unbelievably simple way of doing the incredibly complex task of analyzing our data from multiple sources in real time, correlating them and helping us predict &“

– Airpay

“The notes show this to be a great database. It has convinced me to replace Oracle BerkelyDB java with BangDB”

– Sello Mtshali

“We have been using BangDB embedded and we are very impressed”

– Marcus Gabriel

“We have chosen BangDB for its persistence due to its high performance in place of Redis”

– Joan

“I’m a new user of BangDB and very attracted to its performance in comparision with other NoSQL DBs in the market”

– Wusheng

“We have evaluated BandDB and concluded that its one of the best one in terms of performance ”

– Mohammed Kutty

“We have a startup project that require a fast database and we have decided to use BangDB as it performed best in our benchmark”

– Natinka Ivanoiva

“BangDB performed better than Google’s LevelDB in our performance comparision. Great job! Cool”

– Dmitry Gavrilov
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