What is the difference between a data warehouse and a data lake?

Data Processing: Schema-on-Write vs Schema-on-Read

The key difference is when the data is processed and structured:

• Data Warehouse (Schema-on-Write): Data is cleaned, transformed and structured before being loaded into the warehouse (ETL process – Extract, Transform, Load). The data schema is precisely defined at the warehouse design stage.
• Data Lake (Schema-on-Read): Data is loaded into the lake in its raw form. The structure and meaning of the data are only given when it is read and analyzed (ELT process – Extract, Load, Transform or other approaches).

Type of data stored

Data warehouses mainly store structured, processed and aggregated data, ready for business analysis and reporting. Data lakes can store all types of data – structured (e.g., from databases), semi-structured (e.g., server logs, JSON/XML data) and unstructured (e.g., text files, images, video, social media data) – in their original format.

Users and applications

Data warehouses have traditionally been used by business analysts and managers to generate reports, dashboards and decision support (BI) analysis. Data lakes are more commonly used by data scientists (data scientists) and data engineers for data mining, discovering new patterns, building machine learning models and advanced analytics on raw data.

Storage technologies

Data warehouses are typically built on relational databases optimized for analytical queries (such as Snowflake, BigQuery, Redshift, Teradata). Data lakes often use distributed file systems (such as HDFS) or cloud object stores (such as AWS S3, Azure Data Lake Storage, Google Cloud Storage) as a basis for storing data in various formats.

Flexibility vs structure

Data lakes offer much greater flexibility for storing a variety of data types and avoiding the need to define a schema at the outset. They are more suitable for exploring and discovering previously unknown relationships. Data warehouses, on the other hand, provide greater consistency, quality and ease of access to structured data for reporting and analytical purposes.

Coexistence and evolution (Lakehouse)

Nowadays, there is increasing talk of hybrid approaches, such as the Lakehouse architecture, which seeks to combine the flexibility of a data lake with the structure and data quality management mechanisms typical of a data warehouse, often using technologies such as Delta Lake or Apache Iceberg on top of the data lake.

Summary

A data warehouse and a data lake are two different approaches to managing analytical data. A data warehouse focuses on storing processed, structured data for BI purposes, while a data lake stores raw data in a variety of formats, enabling advanced analytics and data science. The choice between the two (or a combination of the two) depends on an organization’s specific analytical needs and data management strategy.