In this article, we will overview the different layers of a well-built data lake, as well as highlight common pitfalls and challenges to consider when implementing a data lake architecture.

Overview:

A typical data lake allows for decoupling of storage, processing, and analytics. The data is segmented into a landing, raw, trusted, and curated zones to store data depending on its consumption readiness. Each layer should be designed with cost optimisation, flexibility, and security in mind. Let’s take a closer look at what each layer does and which services are typically used.

Ingestion:

AWS allows for a myriad of data producers, and ingestion methods can process structured, unstructured, and semi-structured data loaded with batch or in real-time (e.g., streaming with Kinesis Firehose). The files are stored in the AWS S3 service, which is excellent as it has no storage limit. When data arrives, it can be in any format; this is called schema on read. S3 is also budget-friendly as it delivers automatic storage cost savings when access patterns change with intelligent tiering. Last but not least, it boasts excellent security features, from fine-grain access policies to robust encryption with KMS.

Processing and Transformation:

In a Data Lake or Lake House architecture, the data processing layer is responsible for transforming data into a consumable state through data validation, cleanup, normalisation, transformation, and enrichment. AWS Glue is used to track and catalogue the data. Purpose-built components perform a variety of transformations, big data processing (EMR), and near-real-time ETL. Amazon Athena can be used for quick probing into data with SQL-style queries.

Analytics/Visualisation:

This layer provides data access and consumption primarily through S3. SageMaker allows for machine learning models to be trained and deployed. Visualisations and dashboards can be built using tools like AWS QuickSight or offloaded to Tableau (SaaS service). It’s essential to note that the quality of the analytics and visualisations depends on the quality of the data stored in the data lake.

Common pitfalls:

• The main challenge with a data lake architecture is that raw data is stored without the oversight of its contents. For a data lake to make data usable, it needs to have defined mechanisms to catalogue and secure data. Without these elements, data cannot be found or trusted, resulting in a “data swamp.”
• A Data Lake is not the answer to all data analytics needs; businesses may need to leverage a data warehouse. When these two are combined, we talk about a Lakehouse architecture.
• Governance and access control mechanisms are also necessary to ensure the data being accessed is trustworthy. Often overlooked in the implementation phase, these issues later cause frustration with teams not having enough flexibility to re-process and consume data in a democratic way. When familiar issues with data silos come into play, a data mesh architecture may be the answer.

By understanding the different layers of a well-built data lake, businesses can realise the full potential of their data, enabling advanced analytics, generating insights, and improving decision-making processes.

As always, for any data platform needs, contact us at DataPhoenix — small end-to-end data solutions.