What is the difference between a Data Warehouse and Data Lake?

Data warehouses and data lakes are two of the most used tools for storing and managing large volumes of data used by data engineers, business analysts, and more. Both have their advantages, but each performs a different role.

While a data warehouse’s purpose is to be queried and analysed, a data lake has multiple sources of structured and unstructured data that flow into a repository, much like a real lake.

WHAT IS A DATA WAREHOUSE?

A data warehouse stores structured, information from multiple sources such as databases or business applications to support analytics or business intelligence (BI). It provides dashboards and reporting but increasingly enables self-service for business users to analyse trends and patterns to gain insights into things like customer behaviour or market conditions for better decision-making purposes.

Data warehouses are optimised for query performance so queries can be run quickly on large datasets without compromising accuracy or reliability of results.

WHAT IS A DATA LAKE?

On the other hand, a data lake is an unstructured storage solution suitable for big data analytics or for unstructured data sources. Unlike data warehouses where all incoming records must conform with predefined schemas before being stored, data lakes store any type of raw digital asset including text files, images, videos etc. This can be suitable for users performing data science using complex machine learning or Artificial Intelligence operations since no pre-processing steps need be taken prior performing the analysis. This can be used for predictive analysis tasks like anomaly detection and the implementation of pattern-based algorithms.

WHAT ABOUT BOTH?

Increasingly organisations are implementing data architectures using features of both data warehouses AND data lakes. Each offers different benefits, but you don’t need to pick just one for your business. Combining the features and benefits of each approach appropriate to you and your business is a great way of making the most of your data. In a Data Vault data platform solution, we sometimes talk about creating a Persistent Staging Area (PSA) which often has similar characteristics to a data lake combined with a Data Vault data warehouse. However, you need to ensure that you have the correct people for the job. We would recommend either recruiting generalised specialists in your team, or a larger team including Data engineers, scientists, and analysts is the way to go.

SUMMARY

In summary, both Data Warehouses and Data Lakes offer great benefits depending on your specific use case. You can use both in your business, but consider factors like query speed, required accuracy levels, and desired scalability capabilities, when planning your data platforms and remember that a modern approach can involve having a data architecture with the best of both worlds.