The first chapter covers the functional requirements analysis that led to the architecture definition.
In this section, we go through the study of the business requirements that have driven us to the choice of the technical architecture. This analysis is key to achieve the development, in only two months, of a regtech platform capable of processing hundreds of Go of data per day for financial market monitoring.
STEP 1: LOOK AT THE INPUT DATA
STEP 2: CONSIDER THE VARIOUS USE CASES (“Data Pipeline”)
STEP 3: MATCH THE REQUIREMENTS
STEP 4: ARCHITECTURE CHOICE
ZOOM ON THE KAPPA ARCHITECTURE
We will explain how the Kappa Architecture, Apache Kafka and the choice of a recover upon failure capable stream processor and a reliable idempotent NoSQL datastore allowed us to fulfill all client’s requirements.
The second chapter covers the architecture that has been set up.
THE DETAILLED ARCHITECTURE IN 1O MAIN POINTS
- The input data
- Integration layer
- Saving RAW data
- Functional Coherency / Reference Control
- Functional Coherency / Reference Check
- Data Storage
Finally, the last chapter discuss the performance results and some technical conclusions we made.
PERFORMANCES AND KAPPA ARCHITECTURE IS LIFE
We run numerous sets of benchmarks on very different use cases to try to identify the most common bottle neck.
We tell you if the pilot has been a success and what was the key factor of it.
For years, we weren’t fully satisfied with big data architecture in the finance environment. Of course, in some context, the volume of data to process enforce the use of Hadoop solutions. But most solutions were based on distributed batch implementation of MapReduce paradigm which is slow, complex to setup and hard to maintain. These never fit to our vision of modern use cases in Finance.
With this report we want to demonstrate the pertinence of new architectures and their role in fostering the transition to digital finance.