What is Data Lakehouse? Hybrid Data Architecture Explained

Organizations of today have an enormous amount of structured, semi-structured, and unstructured data produced every single second. In the past, organizations were using two different platforms to handle all this information – data warehouses for fast business analytics and data lakes for economical, large-scale storage. 

Having two architectures results in data silos, complex management processes, and higher operating costs. Data lakehouse architecture helps to get rid of the problems above because of the combination of all the advantages of both technologies.

Learning the Basics: What is a Data Lakehouse?

This architecture is a new open data management technology that integrates all advantages of data warehouses and data lakes, like structured query speed and ACID transactions of the former and economical storage of the latter.

The data lakehouse architecture enables the ability to perform business intelligence queries and machine learning tasks by adding a metadata layer on top of cheap object storage.

Key Benefits of the Hybrid Approach

Having no barrier between the data warehouse and the data lake has its benefits for business intelligence and analytics:

• Simplified Architecture: No necessity to create complex and sophisticated ETL processes for transferring unstructured data from the lake to the separate data warehouse.
• Cost-effectiveness: Storing the data of the enterprise in an open format of object storage costs less than a proprietary data warehouse format.
• ML Directly From the Lake: Possibility to directly query raw data for AI/ML models with the help of the same system that is used for SQL queries.
• Data Governance Centralization: One unified platform provides security, access control, and audit of the whole process.

Key Technological Features That Enable This Model

A number of crucial technological features enable such a hybrid model to be realized:

• ACID transactions: They ensure that several people can read and write data simultaneously without corrupting the table’s consistency.
• Schema Enforcement and Evolution: It enables the prevention of incorrect or malformed data being put into tables but allows smooth schema evolution over time.
• Open File Formats: It involves the use of non-proprietary formats such as Parquet and ORC and open table formats (Apache Iceberg or Delta Lake), thus avoiding vendor lock-in.

Also Read: What is Data Annotation? Training AI Models Explained

Conclusion

It is getting more difficult to manage separate systems for analytics and machine learning. The realization of the data lakehouse helps to bridge the gap between cloud-based storage and structured reporting with great performance at reduced costs.