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In the world of big data, organizations are constantly looking for ways to store, process, and analyze vast amounts of information. Two common solutions for handling big data are data lakes and data warehouses. While they may seem similar at first glance, these two data storage paradigms serve different purposes and have distinct characteristics. In this blog, we explore the differences between data lakes versus data warehouses, provide examples of applications that can be built on top of each, and discuss a real-world use case from Amazon Web Services (AWS).
A data lake is a large storage repository that can hold raw, unprocessed data in its native format. Data lakes can store structured, semi-structured, and unstructured data, which makes them highly versatile. They are designed to be highly scalable, accommodating the exponential growth of data. Data lakes allow for real-time data ingestion and are ideal for organizations that need to store and analyze large volumes of diverse data types quickly.
A data warehouse, on the other hand, is a structured repository designed for storing, processing, and analyzing structured data. Data warehouses store data in a highly organized, schema-based manner, which makes it easy to query and generate insights. Data is typically cleaned, transformed, and aggregated before being loaded into a data warehouse. These systems are primarily used for business intelligence and analytical purposes, enabling organizations to make data-driven decisions.
A great example of a company utilizing AWS services to build a data lake is FINRA. FINRA leverages AWS to store and analyze over 500 billion market events daily, enabling them to detect fraud and market manipulation.
FINRA’s data lake, built on AWS, processes and stores vast amounts of diverse data types, including trade data, order data, and reference data. By using Amazon S3, FINRA can store and manage their data cost-effectively and securely, while Amazon EMR and AWS Glue enable them to process and analyze the data to identify potential violations.
You can learn more about this use case by reading the AWS case study for FINRA.
Data lakes and data warehouses serve different purposes and are suited for different types of data storage and analysis. Understanding the differences between these two storage paradigms is essential for organizations.
Once you’ve determined which path is right for you, be sure to build data resilience into your strategy. Keep an eye out for my next blog, which discusses advanced techniques for data resilience in RDS data warehouses and S3 data lakes.