What is a Data Lakehouse?

Data Lakehouse

A big trend a few years ago, it has long since become reality in most companies: We are talking about Big Data. Digitization is causing more and more IT systems to produce more and more data. Information can be derived from data, which is why Big Data now plays a very important part in management decisions. Nowadays, only those who know their company, the market and their competitors precisely can remain competitive. There are different data architectures for the preparation, analysis and evaluation of data, whereby a new term has become established in recent years: the data lakehouse. At its core, it is a new type of data architecture that combines the advantages of a data lake and a data warehouse.

In this article, we want to have a closer look at the Data Lakehouse and show which advantages it offers and how it is used in reality.

What is a Data Lakehouse?

Unlike data warehouses, which have a structured data architecture, a data lakehouse follows a semi-structured or unstructured architecture, as in a data lake. It is a hybrid data architecture that stores and processes structured and unstructured data in a central repository.

In the Data Lakehouse, data is stored in its original form, regardless of whether it is structured, semi-structured or unstructured.

Unlike a data lake, a data lakehouse has built-in schema management that allows data to be organized in a structured format. This facilitates data access and analysis without the need for complex ETL (Extract, Transform, Load) processes. A data lakehouse can be implemented in a number of ways, including the use of cloud services such as Amazon S3 or the use of open source tools such as Apache Hadoop and Apache Spark.

Benefits of a Data Lakehouse

What advantages does a data lakehouse offer over a data warehouse or data lake? One of the biggest benefits certainly is the improved data quality and the higher speed of data processing. Because data can be uploaded quickly to a data lakehouse and stored in a structured manner, errors and inconsistencies in the data are more effectively identified and resolved. A major advantage of data lakehouses is their scalability. With a data lakehouse, data can be processed in real time. This in turn allows companies to respond quickly to changes in the business environment. Finally, a data lakehouse is also more cost-effective than traditional data warehouses because it is based on lower-cost storage technologies.

Where does a data lakehouse come into play?

As mentioned, a data lakehouse is used whenever large volumes of structured and unstructured data are to be stored and analyzed. The areas of its use range from Big Data analysis to Data Science and Machine Learning. Typical use cases for a data lakehouse include analyzing customer behavior, monitoring production processes, or creating personalized marketing campaigns. With the ability to analyze data very quickly, companies can respond and make informed decisions just as quickly.

Which technologies are used?

Some of the key technologies for implementing a data lakehouse are Delta Lake, Apache Hudi, and Apache Iceberg. These technologies provide organizations with a powerful infrastructure to manage Big Data and enable them to access data quickly and effectively. However, there are also some challenges in implementing a data lakehouse.


A data lakehouse is a powerful type of data architecture that helps businesses access data and make informed decisions quickly and in real time. If data exists in many different formats as well as in structured and unstructured form, a data lakehouse is best suited for processing and analyzing this data. The use of a data lakehouse is also worthwhile in terms of costs, although the hurdles in terms of data quality and data security should be taken into account.