November 9th, 2023

Data integration: focus on data warehouse, data lake, data federation and co.

Data warehouse, data lake, data federation, enterprise application integration: anyone involved in data management and data integration cannot avoid these four concepts. But what exactly is behind the models? How are they connected? Where are the differences and where are the similarities? What advantages and disadvantages do they have? We bring light into the darkness.

Data Lake - flexible storage of raw data

Data lakes are used to store large volumes of data. Data of different types and from different sources can be stored in a data lake. A data lake can therefore contain both processed (structured) and unprocessed (unstructured) data. As all data is stored in its unprocessed format, large volumes of raw data can be stored for subsequent analysis - without the need for time-consuming preparation of a database system in advance. A data lake offers maximum flexibility. The easily malleable data can be used for a wide variety of purposes - especially for machine learning. However, data lakes require additional hardware and the effort required for data maintenance is high. Data lakes therefore mean additional costs.

Data warehouse - storage of structured data

A data warehouse is also a central database for storing large volumes of data. Data from various sources  is structured, processed and, if necessary, analyzed and made available. This data harmonization makes it possible to use the data in a data warehouse directly for business intelligence (BI) purposes - and thus convert it into insights that can be used directly by employees. Compared to a data lake, however, a data warehouse is less flexible due to the structuring of the data. Data lakes and data warehouses do not have to be seen as alternatives, but can complement each other. The concept of the data warehouse helps to digitize processes and allow direct access to data. It combines the flexibility of a data lake with the fast, context-related analysis options of a data warehouse.

Data Federation - the virtual database

In a data federation, data from different sources (e.g. among others from data lakes and data warehouses) is linked and presented as a common data model of distributed systems without the data having to be copied, synchronized or migrated. The data itself remains unchanged in its source or where it originates. The advantage is that users can use the data as if it were in a single source. Especially with XSPHERE data federation is an optimal alternative. XSPHERE is easy to install (without an implementation project) and easy to use. It requires no data maintenance, as the links follow new versions. This means that you have the guarantee of working with up-to-date data. Set links are automatically bidirectional and no additional hardware is required. You therefore benefit from immediate added value.

Enterprise Application Integration (EAI) - integrating business functions company-wide

Enterprise application integration (EAI) is also frequently mentioned in connection with data integration. The concept aims to seamlessly integrate heterogeneous and autonomous systems and company applications using various approaches. This facilitates the flow of information within the company and increases process efficiency. For example, EAI can be used to integrate data from different systems into data warehouses or the environment of a data federation.