What is a data lakehouse?

Data Lakehouse: The next evolution of data-driven corporate management

A big trend years ago, now a reality in most companies: We're talking about big data. Digitalization means that more and more IT systems produce more and more data. Information can be derived from data, which is why big data is now very important in management decisions. Only those who know their company, the market and the competitors in detail today can remain competitive.

There are different data architectures for the preparation, analysis and evaluation of data, although a new term has become established in recent years: the Data Lakehouse. At its core, this is a new type of data architecture that combines the benefits of a data lake and a data warehouse. In this article, we want to take a closer look at the Data Lakehouse and show you what benefits it offers and how it is used in practice.

What is a data lakehouse?

In contrast to data warehouses, which have a structured data architecture, a data lakehouse follows a semi-structured or unstructured architecture, as is the case with 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.

In contrast to a data lake, a data lakehouse has built-in schema management, which makes it possible to organize data in a structured format. This makes it easy to access and analyze data without the need for complex ETL (extract, transform, load) processes. A data lakehouse can be implemented in various ways, including using cloud services such as Amazon S3 or using open-source tools such as Apache Hadoop and Apache Spark.

Benefits of a data lakehouse

What are the advantages of a data lakehouse compared to a data warehouse or a data lake?

• Improved data quality and higher processing speed
  Since the data can be quickly loaded into a data lakehouse and stored in a structured manner, errors and inconsistencies are identified and resolved more effectively.
• Scalability & real-time capability
  With a data lakehouse, data can be processed in real time. As a result, companies can react more quickly to changes in the business environment.
• Cost efficiency
  A data lakehouse is based on cost-effective storage technologies and is therefore usually cheaper than traditional data warehouses.

Where is a data lakehouse used?

A data lakehouse is always used when large amounts of structured and unstructured data need to be stored and analyzed. The areas of application range from big data analysis to data science and machine learning.

Typical applications include:

• Analysis of customer behavior
• Monitoring of production processes
• Create personalized marketing campaigns

By being able to analyze data very quickly, companies can react just as quickly and make well-founded decisions.

Which technologies are used?

Some of the key technologies for implementing a data lakehouse include:

• Delta Lake
• Apache Hudi
• Apache Iceberg

These technologies provide companies with a powerful infrastructure to manage big data and provide fast and effective access to data. However, there are also challenges, such as data quality and governance, that must be considered during implementation.

Conclusion

A data lakehouse is a powerful type of data architecture that helps companies access data quickly and in real time and make informed decisions. If data is available in many different formats, both structured and unstructured, 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.

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