What is a data lakehouse?

A data lakehouse is an open data management architecture that combines the flexibility and cost-efficiency of data lakes with the data management and structure features of data warehouses, all on one data platform.

Simply put: The data lakehouse is the only data architecture that stores all data — unstructured, semi-structured, AND structured — in your data lake while still providing the data quality and data governance standards of a data warehouse.

This is beneficial to data scientists, as data lakehouses support machine learning and business intelligence while also supporting SQL analytics, real-time data applications, and data science.

Compare: data lakehouse vs data warehouse vs data lake

Your data architecture must evolve to meet the needs of your business today while scaling to the promise of your business tomorrow. The good news? You have better choices than a low-cost data swamp of enterprise data or a rigid, limited data ingesting machine without artificial intelligence capabilities.

Data lakes don't provide the data governance capabilities that you need to manage big data securely and data warehousing only ingests structured data without giving you the flexibility or scalability your growing business needs. They simply weren't built for the data challenges of today.

Data warehouses support business intelligence and SQL applications. While efficient and largely secure, data warehouses are costly and unable to ingest semi-structured or unstructured data.

Data lakes emerged, able to handle all types of data — and with cheaper storage. Data lakes also supported data science and machine learning capabilities. Unfortunately, they were missing a critical element of data warehouses: They don't support ACID transactions or enforce data quality and governance, making working with this data clunky and time consuming.

Features of a data lakehouse

Lakehouse architecture combines the best features of the data warehouse and the data lake, providing:

- Cost-effective storage
- Support for all types of data in all file formats
- Schema support with mechanisms for data governance
- Concurrent reading and writing of data
- Optimized access for data science and machine learning tools
- A single system to help your data teams move workloads faster and more accurately without needing to access multiple systems
- Concurrent reading and writing of data
- Real-time capabilities for data science, machine learning, and data analytics projects
- Scalability and flexibility
- The advantage of being open source

Advantages of a data lakehouse

Gleaning business intelligence from unstructured data is the goal — how companies handle their raw data is critical.

Outdated data architectures can prove difficult: Data lakes ETL and store and compute big data from the entire enterprise in low-cost object storage (allowing for common machine learning tools) — but are often disorganized and poorly maintained. While data lakes are low-cost, they are typically slow to access. Improved query engine designs in data lakehouses allow for high-performance SQL analysis and data layout optimizations.

Data warehouses weren't created to ingest unstructured data types, meaning that users must toggle between multiple systems. With multiple ETL steps (and room for error), this data architecture requires regular maintenance and is a significant concern for data analysts and data scientists alike.

With a data lakehouse, your business is handling their big data with:

  • Simplified schema
  • Better data governance
  • Reduced data movement and redundancy
  • Faster and more efficient use of your team's time

This is an advantage to multiple roles across the company. Data engineers can build data pipelines faster than ever (and faster than most data warehouses!). Data scientists can streamline machine learning projects. With all data teams on one platform, you can reduce operational inefficiencies across the board.

The data lakehouse architecture is delivered as a service on AWS, Microsoft Azure, or Google Cloud — examples include the Databricks Lakehouse Platform (Delta Lake) and Snowflake.

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