The horizontal components are architected to run on individual machines (bare metal or VM) or clusters.
- Ingestion Module
- Transformation Module
- Data Store Module
- Reporting/BI Module
- Management Module
- Security Module
- Rule Engine and ML Engine
The ingestion/transformation modules are based on kafka
+ flink/spark(with flare) and can support any complex ETL requirement.
The ingestion module scales to data rates of millions of data records per second.
For low throughput but high complexity batch jobs, we also support Talend.
The data store can either be a pure RDBMS (Postgres with XL extensions) or a Hbase based tabular store with SQL support (Hawq or Trafodion). Other stores when stable, like kudu
will be added as needed in future. We believe a SQL interface is key to any operational data store.
For log writes, time series type data Cassandra/ScyllaDB
is used.
The idea is to map data to stores that do not result in a semantic mismatch.
Finlibre components will pre-qualified for performance on large cluster hardware and commodity storage hardware to avoid any additional effort on optimal hardware sizing.
These benchmarks will be done in conjunction with our hardware and industry partners on standard available configurations and real customer scenarios.
IIT-M is also creating the RISEMarks™ series of benchmarks to enable objective comparison of enterprise class systems
with domain specific benchmarks.
FT @ RISE Group will incubate and host a variety of BFSI centric open source projects.
The first project aims to provide a comprehensive analytics and decision making
framework for the Banking and Insurance sectors.
Funding for the first project is
through CSR grants from City Union Bank Ltd.
We welcome every organization in BFSI sector to participate and reap the collective benefits.
