Data Lake vs. Data Warehouse: What’s the Difference?

Modern data cloud architectures are responsible for importing, querying, transforming, and analyzing all kinds of data types. For many organizations, we’re talking hundreds of thousands of queries per day. Depending on the specifics of these workloads, data teams might be wondering whether they should choose a data lake vs. data warehouse architecture.

Each approach has its trade-offs, and the right choice will depend entirely on your use cases and business priorities. In this article, we’ll walk through each style and give you all the information you need to make the best decision for your organization.

What is a data warehouse?

Data warehouses are centralized digital storage systems that are designed to collect, integrate, and store large volumes of diverse data—structured and semi-structured, current and historical, etc. The primary goal of any data warehouse is to make data retrieval and analytics faster, easier, and more seamless.

data lake vs. data warehouse — Enterprise data warehouse components

Key features of a data warehouse include:

Centralized repository that combines data from multiple sources
Use of ETL (Extract, Transform, Load) to clean, standardize, and prepare data for various use cases
Combined storage of current and historical data to aid in trend analysis, comparative analysis, forecasting, and more.
Standardization of data formats across the organization to help maintain consistency and accuracy

What benefits do data warehouses offer businesses?

There are plenty of reasons to opt for a data warehouse structure despite higher storage costs and scalability challenges:

Enhanced decision-making. Data warehouses deliver consistent, reliable insights that make it simple and easy to retrieve desired information. This makes data-driven decision-making faster and more seamless.
Better data quality. Data warehouses cleanse and standardize data via robust ETL protocols, the result is a higher data integrity and reliability.
Improved query performance. Because data is already structured or semi-structured, queries can fetch information faster and more efficiently.
Built-in security and governance protocols. Data warehouses usually don’t require additional governance protocols

What is a data lake?

Data lakes are centralized repositories that store raw data in their native formats—no predefined schema is required. In addition to structured and semi-structured data, data lakes can also accommodate unstructured data.

Key features of a data lake include:

Flexible storage, as data lakes can accommodate text, images, audio, video, and more
Schema-on-read approach, which allows users to define the structure of data upon access rather than as a condition of storage
High scalability and ability to handle large volumes of data
Real-time and batch ingestion layer which organizes incoming data into logical folder structures
Data processing layer to transform, clean, and convert raw data into suitable formats for analysis
Mixed-workload support
Built-in optimization techniques like parallel processing, caching, and indexing

What benefits do data lakes offer businesses?

Although data lakes offer a more varied data quality and sometimes cause queries to run longer to fetch data, there are still plenty of reasons to go with a data lake approach:

Scalability and flexibility. Data lakes can easily handle larger volumes of data, making it an excellent choice for use cases that require more diverse types of information (e.g. ML reinforcement learning, where exploration is more important than exploitation)
Higher cost efficiency. Data lakes leverage inexpensive object storage and open formats, making it less expensive to store and process data.
Better data integration. Data lakes are more effective at storing data from diverse origins, as there’s no need to pre-process or transform those data.
Improved accessibility. Data lakes consolidate all data—structured, semi-structured, unstructured—into a single location that eliminates siloes.

Data lake vs. data warehouse: a comparison

So now that we’ve walked through features and benefits of data warehouses vs. data lakes, here’s a quick summary of how the two compare:

Criteria	Data Warehouse	Data Lake
Data structure	Highly structured and organized (structured and semi-structured)	Raw data (unstructured, semi-structured, and structured)
Schema	Schema-on-write: Structure is defined upon loading	Schema-on-read: Structure is applied upon access
Data quality	High data quality via ETL processes (cleansing, standardization)	Varies; raw data is stored, so additional processing may be needed
Query performance	Excellent performance for predefined, complex analytic queries	May require additional compute resources for optimal performance efficiency
Storage cost	Generally higher due to structured storage and indexing	Often lower due to open formatting
Governance & security	Strong governance, consistent data, and built-in security features	Requires additional governance layers; flexible but less controlled
Flexibility	Less flexible; requires predefined structure	Highly flexible; ingests data in its native format
Common use cases	Business intelligence, reporting, and historical analysis	Big data analytics, machine learning, and data discovery

Data lakehouse: the best of both worlds?

To take advantage of the strengths that both data lakes and data warehouses have to offer, many of the major data cloud providers—Databricks, RedShift, BigQuery, etc.—offer a data lakehouse architectural approach. This approach is exactly what it sounds like: an architecture that leverages the strengths of each approach to address their respective limitations.

Some key features of data lakehouses include:

Unified storage of structure, semi-structured, and unstructured data within a single repository
ACID (atomicity, consistency, isolation, durability) transactions to ensure data integrity among concurrent users
Implementation of predefined schemas to improve data governance and organization
Use of open file formats and APIs, which allows for broader compatibility with various analytics and business intelligence tools

This approach brings together the structured approach of a data warehouse, but maintains the flexibility and scalability of a data lake. As a result, users can improve data quality through ACID transactions and schema enforcement, while adopting a less expensive approach to cloud object storage.

Final thoughts on data lake vs. data warehouse

As with just about anything in the data and IT space, there’s no one right answer to the question of which is better: data lake vs. data warehouse. Different organizations have different use cases and desired business outcomes, each of which will determine the best architectural approach to use.

Hopefully this article has given you a high-level understanding of the pros and cons of each approach. At the end of the day, the key factors in choosing a data cloud architecture are: