BangDB = NoSQL + AI + Stream – Converged NoSQL database

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Modern approach to data

NoSql + AI + Stream + Graph

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Predictive | Streaming | Realtime | Analytics

Data Model : KV, Document, Time-series, Large files, Graph, Cypher, Ontology
Index : Primary, Secondary, Nested, Composite, Reverse, GeoIndex
Stream : Ingest, Analyze, Act, Timeseries, Sliding Window, CEP, Running Stats
AI : Prepare/Train/ Pred on file/tables/tream, automate, version, deploy
Deployment : Embedded (within device or process), Client/Server, p2p distributed
Supports : Transaction (OCC), WAL (durability and crash recovery), Replication
Query : API (programs), Simple SQL like in CLI - for DB, stream, ML and others
Performance: 200K+ IOPS, 10K+ Events/sec, IO Layer, Buffer Pool, Concurrency

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
Integrated stream & AI
Text, images, binary
Interactive CLI & dashboard

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Multi Cloud to Edge to Embedded

SaaS - Apps and database
Embedded (in-device)
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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BangDB
Built to solve problems | aligned with future

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 manne, leave heavy lifting to the BangDB

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Converged data platform
Break silos | faster to build | easy to scale

This emerging and rapidly growing data trend demands a novel data framework & design to address the problems in powerful yet in simple way.

Convergence of different necessary dimensions into a single unit of compute is required to break silos and allow developers to just focus on problem at hand and let BangDB provide the streaming and AI and database support.

BangDB breaks the silos by fusing different elements into a single space and then scales that space by adding machines as needed

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High performance
Fully concurrent | IO Optimized

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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Streaming in BangDB
Ingest | Transform | Analyze | Act

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

BangDB has inbuilt streaming capabilities and provides APIs and mechanism to deal with time-series data in a simple yet powerful manner.

BangDB allows users to define Schema, aggregation, groupby, referral, filter, join and complex event processing 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

Finally, action could be taken in the form of mails, reports, notification, or API trigger for further steps in real time

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AI in BangDB
Train | Test | Deploy | Automate

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

When AI is a silo, it’s almost impossible to bring in the efficiency and automation for predictive analysis. As data flows in, we need to train or retrain the model, test it, deploy it and do prediction for set of streams.

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

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Rich query support
Document | timeseries | key-val

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

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Cloud Native platform
Build your apps with BangDB Ampere

Use interactive dashboard to build powerful domain specific apps and scale on cloud as required. The platform is cloud agnostic and works on most of leading cloud providers

The platform has built appstore as well where many pre baked apps/solutions are published.
Appstore : install, configure and get started in few minutes — Ecommerce analytics app
— Datacenter monitoring app
— Linux server monitoring app
— Database analytics app (mysql | mongodb | oracle)
— Full stack analytics app
— Payment predictive analysis app
— And many more…

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Continuous operational intelligence

For optimized and efficient operations, continuous processing and intelligence is basic requirement. Which demands shift from traditional BI towards the real time automated framework

— Log file analysis, drill down, find pattern & anomalies
— IOT monitoring and predictive analysis for streams
— Real time 1-0n-1 personalization for higher conversion
— Predictive lead scoring
— Full stack analysis for application and infrastructure

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Testimonials

“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”