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Guidance for Financial Regulatory Reporting on AWS

Overview

This Guidance helps you build a data lake and a data analytics platform to address many of the issues that complicate regulatory reporting, such as data being in disconnected silos and distributed extract, transform, load (ETL) processes. Using a data lake, financial institutions will have a single source of data to help them meet regulatory requirements for a large volume of information. With this Guidance, financial institutions can gain insights through advanced analytics and machine learning—faster and at a lower cost. 

How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

Download the architecture diagram

Well-Architected Pillars

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

Data is persisted across three different layers. Data in the raw layer is untouched, giving you a baseline “input” dataset that does not change, regardless of what happens to the data in subsequent layers, such as the processing or consumption layer. 

Read the Operational Excellence whitepaper 

Lake Formation provides fine-grained access control for the S3 buckets in the data lake, and this data is encrypted at rest. To further secure your data, use only the consumption layer for reporting purposes. 

Read the Security whitepaper 

This architecture uses Amazon S3 , which can replicate data across AWS Regions or Availability Zones to help backup and restore critical data. 

Read the Reliability whitepaper 

To optimize this architecture, you can change the data stored in the consumption later into a data format that would provide the best performance for your needs. 

Read the Performance Efficiency whitepaper 

This architecture uses  Athena with Amazon S3 so you can run ad-hoc queries rather than having to keep an Amazon Redshift cluster up and running, even when querying is not needed.  You can save on costs by paying only for the queries you run rather than idle infrastructure.

Read the Cost Optimization whitepaper 

This architecture uses scalable services where possible so that resources are scaled up only according to business need.  

Read the Sustainability whitepaper 

Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.

References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.