What is Data Lake vs Data Warehouse?
Introduction: The Great Divide
For the past twenty years, the architecture of enterprise data has been defined by a choice between two systems: The Data Warehouse or the Data Lake.
These two systems represent fundamentally different philosophies regarding how data should be stored, processed, and consumed. While they have historically competed for IT budgets, the growth of data volume and the rise of Machine Learning led organizations to realize that they needed the capabilities of both.
Understanding the strengths and weaknesses of both architectures is essential to understanding why much of the industry has moved toward the modern Data Lakehouse.
The Data Warehouse: Order and Speed
The Data Warehouse (e.g., Teradata, Oracle, Snowflake, Amazon Redshift) was the original analytics platform, born in the 1980s and 1990s.
Its defining characteristic is Schema-on-Write. Before a single byte of data can be loaded into a data warehouse, a data engineer must explicitly define the structure of the table (the columns, data types, and relationships). The data is then processed via ETL pipelines to fit this defined structure.
- The Pros: Because the data is structured and optimized internally by the database engine, query performance is fast. Business analysts can connect BI tools like Tableau and get quick SQL results. It has long been the standard for reliable, historical business reporting and executive dashboards.
- The Cons: It is rigid. If a new data source emerges, the schema must be changed. Furthermore, because Data Warehouses traditionally couple expensive compute resources with proprietary storage, storing very large volumes of data inside them becomes expensive.
The Data Lake: Chaos and Scale
The Data Lake (e.g., Hadoop HDFS, Amazon S3, Azure ADLS) emerged in the 2010s to address the cost and rigidity problems of the Data Warehouse.
Its defining characteristic is Schema-on-Read. A data lake is a scalable repository where organizations store raw data in its native format: structured (CSV), semi-structured (JSON logs), or unstructured (images, audio files, PDFs). The data is dropped into low-cost storage. Structure is only applied later, when a data scientist actually attempts to read and process it.
- The Pros: It is inexpensive, allowing organizations to store large volumes of data for long periods. It is flexible and suits Data Scientists who need large amounts of raw data to train Machine Learning models.
- The Cons: Data Lakes on their own are file storage. They lack the transactional guarantees (ACID) of a database. If an ETL job fails halfway through writing a set of files, readers can see partial results. Because the data is unoptimized and messy, business analysts cannot easily connect Tableau to a Data Lake to run fast SQL queries; they often devolve into unusable “Data Swamps.”
The Two-Tier Architecture Problem
Because businesses needed the cheap storage and ML capabilities of the Lake, AND the fast BI dashboards of the Warehouse, they built “Two-Tier Architectures.”
They dumped all raw data into the Data Lake (Tier 1). Then, they wrote complex, brittle ETL pipelines to copy a subset of the best data out of the Lake and duplicate it into the Data Warehouse (Tier 2). This architecture is expensive (you store the data twice), fragile, and creates data silos where the BI team and the Data Science team are looking at two different, unsynchronized versions of reality.
The Solution: The Data Lakehouse
The limitations of the Warehouse and the Lake led to the Data Lakehouse.
By utilizing Open Table Formats like Apache Iceberg, organizations can add the transactional guarantees, schema enforcement, and statistics-based data skipping of a Data Warehouse on top of the low-cost, scalable files of a Data Lake.
Coupled with federated query engines like Dremio, the Lakehouse reduces the need for the two-tier architecture. Data is stored once in object storage, yet it is queryable by both business analysts running SQL dashboards and data scientists training machine learning models, unifying the enterprise data stack.
Learn More
To learn more about the Data Lakehouse, read The Lakehouse Built for Everyone by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.