For enterprise data leaders, the choice between a data lake and a data warehouse has often been a difficult one. Data lakes are great for storing large volumes of unstructured data, but they can be slow and difficult to query. Data warehouses are great for structured data and for fast querying, but they can be expensive and inflexible. A data lakehouse is a new data architecture that combines the best of both worlds, providing the scalability and the flexibility of a data lake with the performance and the governance of a data warehouse. This article explains what a data lakehouse is and why it is becoming the go-to architecture for enterprise analytics.
The key innovation of the data lakehouse is that it brings the capabilities of a data warehouse to the data lake. This is done by adding a transactional metadata layer on top of the data lake, which provides features like ACID transactions, data versioning, and schema enforcement. This allows you to run traditional BI and analytics workloads directly on your data lake, without having to move your data to a separate data warehouse. For a deeper dive into the different data architectures, see our article on a director’s guide to modern cloud data warehousing.
The Key Characteristics of a Data Lakehouse
The key characteristics of a data lakehouse include:
- Open Architecture: A data lakehouse is built on an open architecture that uses open data formats, such as Apache Parquet, and open standards, such as Apache Iceberg and Delta Lake. This prevents vendor lock-in and gives you the flexibility to use a variety of tools and engines to access your data.
- Support for Both BI and Machine Learning: A data lakehouse can support both traditional BI and analytics workloads, as well as machine learning and data science workloads. This is because it can store both structured and unstructured data and can be accessed by a variety of tools and engines.
- Transactional Support: A data lakehouse provides ACID transactional support, which ensures data consistency and reliability. This is a key feature that has traditionally been missing from data lakes.
- Schema Enforcement and Governance: A data lakehouse provides schema enforcement and governance capabilities, which help you to ensure the quality and the integrity of your data.
The Benefits of a Data Lakehouse
The key benefits of a data lakehouse include:
- Simplified Architecture: A data lakehouse simplifies your data architecture by eliminating the need for a separate data lake and data warehouse.
- Reduced Costs: A data lakehouse can reduce your costs by eliminating the need to move and to duplicate your data.
- Improved Performance: A data lakehouse can provide better performance than a traditional data lake by using techniques like caching and indexing.
- Increased Agility: A data lakehouse can help you to be more agile by allowing you to run a variety of workloads on the same data.
| Characteristic | Data Lake | Data Warehouse | Data Lakehouse |
|---|---|---|---|
| Data Types | Structured, semi-structured, and unstructured | Structured | Structured, semi-structured, and unstructured |
| Schema | Schema-on-read | Schema-on-write | Schema-on-read with schema enforcement |
| Performance | Slow for BI and analytics | Fast for BI and analytics | Fast for both BI and machine learning |
| Cost | Low | High | Low |
The Key Players in the Data Lakehouse Market
The data lakehouse market is still emerging, but there are a number of key players that are leading the way:
- Databricks: Databricks is the company behind Apache Spark and Delta Lake, two of the key technologies that underpin the data lakehouse.
- Snowflake: Snowflake is a cloud data platform that has recently added support for unstructured data and for external tables, which allows it to function as a data lakehouse.
- AWS: AWS offers a variety of services that can be used to build a data lakehouse, including Amazon S3, AWS Lake Formation, and Amazon Redshift Spectrum.
- Azure: Azure offers a variety of services that can be used to build a data lakehouse, including Azure Data Lake Storage, Azure Synapse Analytics, and Azure Databricks.
Conclusion
A data lakehouse is a powerful new data architecture that combines the best of both worlds: the scalability and the flexibility of a data lake with the performance and the governance of a data warehouse. By simplifying your data architecture and by reducing your costs, a data lakehouse can help you to unlock the full potential of your data to drive innovation and to create a more intelligent and competitive business. As you develop your data strategy, be sure to consider how a data lakehouse can help you to build a more modern and effective analytics platform. For a deeper dive into data strategy, see our guide on developing an enterprise BI strategy.
