The design and maintenance of a data lake can be a cumbersome process. You need to provide and customize infrastructure, transfer data from disparate sources into the data lake, remove the schema and apply metadata tags to make it accessible from other tools or applications. In addition, you also need to enforce security policies and manage user authentication and authorization for each data segment.
Last August, Amazon announced the general availability (GA) of their AWS Lake Formation service, an automated data lake solution that allows quick ingestion from different sources using pre-defined templates, the ability to transform the data into formats like ORC and Parquet for easier analytics, and protection of your data lake. This all makes it accessible for downstream use such as machine learning purposes.
So when should I use it?
Ask yourself a few of the following questions:
- Would you want to tap into data lakes in a matter of days instead of months?
- Are you tired of the ongoing management of your data lakes?
- Are you interested in analyzing your data more efficiently?
- Are you fed up of wasting time on data lake maintenance?
- Would you like to focus on getting insights from your data instead of maintaining it?
Tell me more about AWS Lake Formation capabilities
From my experience as a data engineer, using AWS Lake Formation service eliminates and automates many of the complex manual measures that are normally needed to be done in the process of building a data lake. In addition to that, it also cleans and duplicate data using machine learning to improve data consistency and quality. And all “this magic” is for free! You will only pay for the underlying services being used, for example, S3 storage.
AWS Lake Formation combines encryption, storage, research, and machine learning tools with the underlying AWS services and configures them dynamically with an easy-to-use online interface, to conform with your centrally specified access controls. It also allows the processing of batch and stream data into Amazon S3 and can transform the data into many data formats of your choice.
Moreover, the ability to access controls for data at-rest and in-transit, including line, row, and column level management and encryption policies. You can then control the data lake using a wide range of computational and machine learning tools from AWS. All-access is safe, maintained and auditable. Optionally, there is also the possibility to clean the data and eliminate duplicates, fill in missing values, link data documents through data sets and delete data errors.
In my extensive experience building cloud-based data lakes and data pipelines, I’ve realized that the AWS Lake Formation service provides the ability to focus on the things that really matter such as the data, and extract insights from it without being bothered with its maintenance. If you want to learn more about how you can start leveraging your data, get in touch!