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.
All the data is stored as-is in the raw layer without undergoing any changes.
Data undergoes basic transformations in the processed layer, such as normalizing the date to a certain format or cleaning up empty rows.
The consumption layer contains the final “cleansed” copy of the data to be used across a number of different use cases, including regulatory reporting.
AWS Glue Data Catalog provides a view of the metadata of all the data across the different S3 buckets.
AWS Lake Formation centrally manages access to the available datasets and applies fine-grained permissions for all users accessing the data.
Use Amazon QuickSight, a data visualization and business intelligence service, for reporting. Use Athena for interactive analytics.
The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems in the cloud. The six pillars of the Framework allow you to learn architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable systems. Using the AWS Well-Architected Tool, available at no charge in the AWS Management Console, you can review your workloads against these best practices by answering a set of questions for each pillar.
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.
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.
This architecture uses Amazon S3, which can replicate data across AWS Regions or Availability Zones to help backup and restore critical data.
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.
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.
This architecture uses scalable services where possible so that resources are scaled up only according to business need.
A detailed guide is provided to experiment and use within your AWS account. Each stage of building the Guidance, including deployment, usage, and cleanup, is examined to prepare it for deployment.
The sample code is a starting point. It is industry validated, prescriptive but not definitive, and a peek under the hood to help you begin.
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.