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How can companies make optimal use of their continuously growing data pools? In many companies, silo architectures still hinder the flow of data. Data Lakes are suitable for breaking these down and enabling smart data analyses.

We explain how a data lake differs from a data warehouse, which application scenarios it opens up and which advantages and disadvantages companies should take into account.

Definition: What is Data Lake?

A data lake is an IT architecture for data storage. Many companies use the term synonymously with data warehouse, data mesh or data hub, although the concepts are clearly different.

Our definition of Data Lake:

A data lake (also enterprise data lake or big data lake) is a central data store in the company in which structured and polystructured data is kept across departments and applications for analytical and operational purposes. The data pools collect file copies or original data from different storage locations.

Advantages and Disadvantages of a Data Lake?

Although Big Data Lakes are no longer the latest concept for data storage, there are still many reasons to use them. However, the technology has its limitations.



Traditional data warehouses are more difficult to scale due to their data structure. A data lake grows relatively inexpensively with increasing data volume.

Flexible data schemes

In Data Lakes, data is stored in different schemas. Even schema-free storage is possible, for example in a Hadoop Data Lake. Data can thus be retrieved in any schema, which makes it extremely versatile.

Advanced Analytics

Data Lakes store data in a way that makes them ideal for use with Machine Learning and AI algorithms. These technologies, in turn, enable companies to perform faster and more accurate data analysis and make better, data-driven decisions.


Complicated data flow

Although the data in the data lake can be easily merged, the preparation for different applications is technically relatively complex. Data hubs and other IT architectures are better positioned here.

No integrated quality management

There is no quality control of the collected data in the data lake. This must be done in the application system. A centralization and thus simplification of data quality management is not achieved.

Data Lake vs. Data Warehouse: What's the Difference?

The concepts of data lake and data warehouse are very similar, but not congruent. An enterprise data lake cannot replace a data warehouse or vice versa.

The key commonality is that both data stores retain data that is to be used by the business for analysis purposes.

While copies of data are stored in the data lake, the original data from different applications are brought together in the data warehouse.

The data in the data warehouse is usually required for clearly defined applications and is available in a structured form. The processing procedures are clearly defined. In contrast, polystructured data is collected in the data lake, some of which is not (yet) assigned to a clear purpose and has not undergone any quality assurance. The data can be used well for explorative analyses. The application scenarios for this "raw data" are versatile.

Data Lake Architecture: Optimal Value Creation Through Technology Mix

Data Lake vs. Data Warehouse vs. Data Hub? Many companies are faced with this decision. However, they achieve the best added value when they combine different data storage architectures, as they differ in their function.


In the course of an exploratory analysis of data from the enterprise data lake, data scientists can evaluate potential applications. From now on, the data is made available for standardized, scalable evaluation in the data warehouse in a structured process. External partners who are also to have access to the evaluations are connected via a data hub.

In order for the investment in a data lake to pay off, companies should definitely think through the entire value chain from data collection to use in advance. Projects often fail because the necessary structures around the data lake have not been created to operationalize the insights from the newly acquired data.

Data Lake Use Cases from Practice

In contrast to many earlier repository concepts, companies can evaluate their data much more extensively with the help of data lakes. Data lake use cases are imaginable for every industry and in almost every business area.

Example online marketing

A lot of user data can be collected during web tracking on one's own website, even beyond a clear use case within the framework of legal requirements. This data can be collected in a data lake and used if an application scenario presents itself, for example to improve the user experience.

Example logistics

Companies can use sensors on their trucks to collect various movement data, for example on acceleration behavior, kilometers driven, and fuel consumption. If the data is stored in a data lake, forecasts can be derived from the data pool with the help of a machine learning algorithm in order to predict the wear and tear of components, for example, or to optimize maintenance intervals or driving speed.

Example product development

Operators of video streaming services collect data about the behavior of their customers: Which movies were watched and when? Which films are liked by the same customers? The data can initially be collected without a clear objective and evaluated in an exploratory analysis for approaches to offer improvements or new product ideas.

Data Lake: Examples for Technology Providers

Data Lakes can be implemented using various technologies. Hosting is possible both on-premise and in the cloud. The largest technology providers for this are Apache Hadoop from IBM, Microsoft Azure and Amazon's AWS. In addition to pure hosting, they offer various additional services.

Hadoop Data Lake

Apache Hadoop from IBM is an open source platform that enables companies to create data pools at low cost. Due to the distributed processing of the data and the use of commodity computers, the deployment is very reliable and easily scalable.

Azure Data Lake

Microsoft is marketing its Data Lake cloud solution as part of the Cortana Intelligence Suite. This is to ensure seamless processing of stored data in BI tools such as Azure Synapse Analytics, Power BI and Data Factory.

AWS Data Lake

Enterprises using AWS Cloud solutions can also create a highly available data lake here. AWS provides an architecture for the AWS Cloud - with an easy-to-use console for searching and requesting data sets.

Conclusion: Data Lakes - A Tool for Data Analytics of the Future

With the increasing importance of data analysis for the competitiveness of companies, data lakes have established themselves as a data management tool. The operation of departmental data repositories is not designed for Big Data and is becoming a disproportionate cost factor. Data warehouses are also inflexible and limited in their application. Data Lakes are much more cost-efficient, flexible and scalable. They also enable the use of future technologies such as machine learning and AI.

However, the added value of the new data repository does not come automatically with the implementation. In the best case, a data lake forms a cog in the clockwork of the overarching data analytics strategy. Legal regulations, goals and requirements must be taken into account in order to effectively integrate the data lake into the IT architecture. In the meantime, there are also newer approaches to data storage, such as data hubs, which are just as well or better suited to optimize data usage, depending on the business requirements.

Frequently Asked Questions About Data Lake

Better data analytics?

A data lake makes your company fit for smart data analysis. We will clarify together how you can best realize an implementation in a non-binding consultation. Simply write to us to request an appointment.
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