Understanding data stores: Reviewing data warehouses, data lakes, and lakehouses

Choosing the right technology for storing and processing data is crucial for organizations that want to derive insights from their data and implement data-driven decision-making. Three popular approaches for data storage and processing are data warehouses, data lakes, and a more recent concept, data lakehouses.

Each technology has its own strengths and weaknesses and choosing the right one for your business' needs depends on several factors, such as the type of data, the volume of data, and the use cases it will serve.

Explore the differences between data warehouses, data lakes, and data lakehouses, what each architecture does and doesn’t do well, and how to choose between them.

Understand the technology options

Data warehouses, data lakes, and data lakehouses are all used for storing and processing data, but they have different strengths and weaknesses depending on the use case.

Understanding each structure’s foundational purpose and key benefits can help identify the architecture best suited to your company’s needs and goals.

Data warehouses

A data warehouse is a centralized repository for structured data that’s optimized for querying and analysis. It’s designed to support business intelligence and reporting applications that require fast access to data.

Data warehouses are typically built using a relational database management system (RDBMS), such as AWS Redshift or Azure Synapse, and follow a schema-on-write approach, meaning data is structured and organized before it’s loaded into the warehouse.

Data warehouses specialize in providing consistent and reliable data for reporting and analysis. Because the data is structured and organized, it’s easier to query and analyze, and the results are more predictable. Data warehouses also support complex queries and aggregations, making them ideal for business intelligence applications, such as dashboards and automated, repeatable reporting and analysis.

Data warehouses are not well-suited for storing unstructured data, such as free-form text or images, and they can be expensive to scale. Additionally, data warehouses require a lot of upfront planning and design, which can be time-consuming and costly.

Data lakes

A data lake is a centralized repository for raw, unstructured data that’s optimized for storage and processing. It’s designed to support big data and machine learning applications that require large volumes of data.