A data lakehouse is a data solution concept that combines elements of the data warehouse with those of the data lake. Data lakehouses implement data warehouses’ data structures and management features for data lakes, which are typically more cost-effective for data storage. Data lakehouses are useful to data scientists as they enable machine learning and business intelligence.
Features of a Data Lakehouse
As a combination of data warehouses and data lakes, data lakehouses feature elements of both data platforms. Namely:
Concurrent reading and writing of data
Schema support with mechanisms for data governance
Direct access to source data
Separation of storage and compute resources
Standardized storage formats
Support for structured and semi-structured data types, including IoT data
Advantages of a Data Lakehouse
The ability to derive intelligence from unstructured data (text, images, video, audio) makes handling these types of data critical for businesses. Traditionally, though, data warehouses were not optimized for these unstructured data types, making it necessary to simultaneously manage multiple systems – a data lake, several data warehouses, and other specialized systems. Maintaining various systems can be costly and even delay your ability to access timely data insights.
A single data lakehouse has several advantages over a multiple-solution system, including:
Less time and effort administrating
Simplified schema and data governance
Reduced data movement and redundancy
Direct access to data for analysis tools
Cost-effective data storage
Data Lakehouse vs Data Warehouse vs Data Lake
Many businesses operate their data warehouses independently of their data lakes, leveraging data warehousing to derive valuable business insights and using data lakes for storage and data science. Some businesses combine their data lake with their data warehouses in a single data platform — either a data warehouse working in parallel with their data lake or a data warehouse embedded in their data lake — that serves data for business intelligence and data science. Some businesses even add data marts to their data storage stacks, as well.
On the other hand, a data lakehouse serves as a single platform for data warehousing and data lake.
A data platform is not a disparate set of tools or services. Instead, it should be one integrated platform that performs many functions and workloads, including:
Rapid data access
Data engineering for ingestion and transformation of data
Data science for creating AI and machine learning models
Data application development and operation
Data marketplaces and exchanges for quickly and securely sharing data among authorized users
A flexible platform like Snowflake allows you to use traditional business intelligence tools and newer, more advanced technologies devoted to artificial intelligence, machine learning, data science, and other forward-looking data analytic activities. It combines data warehouses, subject-specific data marts, and data lakes into a single source of truth that powers multiple types of workloads.