Clumio announces $75M Series D and 4X YoY growth in ARR

// 15 Jun 2023

Data Lakes vs Data Warehouses: Understanding the Differences and Use Cases

Jacob Berry, Field CISO

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).

What are Data Lakes and Data Warehouses?

Data Lakes

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.

Data Warehouses

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.

Example Applications for Data Lakes and Data Warehouses

Data Lake Applications

  • Sentiment Analysis: Data lakes can be used to store and analyze large volumes of unstructured data, such as social media posts and customer reviews. This data can then be processed using natural language processing and machine learning (ML) algorithms to determine customer sentiment towards a product or service.
  • IoT Data Processing: Data lakes are ideal for storing and processing massive amounts of data generated by IoT devices. The real-time ingestion capabilities of data lakes allow for the immediate processing and analysis of streaming data, enabling organizations to monitor and optimize their IoT networks.
  • Data Storage for ML/AI Training: In the medical and biotech fields, data lakes can be used to store data from diverse sources, such as genetic sequences, patient records, and clinical trial results. Machine learning algorithms can be applied to this unstructured data to identify patterns or correlations, aiding in uses from the development of personalized treatments to predicting disease outbreaks. Furthermore, algorithms can analyze real-time data from medical devices to monitor patient health and alert caregivers to any significant changes (See IoT above).

Data Warehouse Applications

  • Sales Analytics: Data warehouses can be used to store and analyze sales data, allowing organizations to identify trends, track performance, and make data-driven decisions to improve sales strategies. The structured nature of data warehouses makes it easy to create sales reports, dashboards, and visualizations.
  • Customer Segmentation: By analyzing customer data stored in a data warehouse, organizations can segment their customer base and tailor marketing efforts to target specific demographics. Data warehouses can store structured customer data, such as purchase history and demographic information, enabling organizations to create detailed customer profiles.

Example Use from Financial Industry Regulatory Authority (FINRA)

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.

Happy reading!


About the author

Jacob's background is in Cyber Security and Technology, focused on helping customers build secure cloud operating environments. He has extensive experience in offense and defense security, security operations, and working across multiple verticals in both private and public sectors.