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STACKIT Data Lake

The Solid Foundation for Your Data-Driven Future
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Published: September 18, 2026 | Reading time: approx. 4 minutes

Summary (TL;DR): Data Lake

  • Core Function: The STACKIT Data Lake serves as a central, scalable cloud solution for storing and analyzing structured, semi-structured, and unstructured raw data (schema-on-read).
  • Advantages: Maximum digital sovereignty and GDPR compliance through operation in certified European data centers (Germany/Austria), flexible scalability, and a transparent pay-per-use pricing model.
  • Difference from a data warehouse: Flexibly stores data in its original format without a predefined schema—ideal for exploratory data analysis and historical data.
  • Applications: Development of AI and machine learning models, IoT and sensor data processing, clickstream analytics in e-commerce, system integration, and legally compliant raw data archiving.
  • Target audience: Data scientists, developers, data analysts, and companies seeking a future-proof foundation for data-driven business models.

STACKIT Data Lake: Your Path to a Data-Driven Future

Data volumes are growing exponentially—from diverse sources, with varying structures and at different rates. Companies face the challenge of not only storing this information but also integrating, analyzing, and deriving value from it. Traditional databases are increasingly reaching their limits in this regard.

A data lake offers a flexible, scalable solution: It enables the centralized storage and analysis of large volumes of structured and unstructured data—serving as the foundation for modern data-driven business models. In combination with STACKIT, you benefit from a reliable, secure, and high-performance cloud offering for your data infrastructure. Developed and operated in Europe—for maximum control over your data.

Key Terms Related to Data Lakes and STACKIT

Your benefits with a data lake in the STACKIT Cloud

A data lake is more than just a storage location. It is the central foundation for modern data analysis, flexible data management, and the development of data-driven products and solutions. When combined with the STACKIT Cloud, it opens up new opportunities for businesses—secure, scalable, and GDPR-compliant.

Centralized Storage of Large Data Volumes

Whether it’s log data, sensor data, images, or traditional transactions: All raw data can be centrally stored in the data lake and retained for later analysis—regardless of format or structure. STACKIT offers scalable cloud resources with high data throughput for this purpose.

Flexible integration of various data sources

A data lake serves as an open repository for data from a wide variety of sources—such as ERP systems, web services, IoT platforms, or external data providers. Modern interfaces and open formats make it easy to integrate existing tools.

The Ideal Foundation for Data Analysis and Machine Learning

Thanks to the ability to store raw data long-term and unchanged, historical data can also be included in analyses within the data lake. This creates a data foundation for machine learning, predictive models, or visual analyses—with full control over data access.

High Security and Digital Sovereignty

STACKIT operates its infrastructure in certified data centers in Germany and Austria. This ensures that your data is stored and processed without relinquishing control to providers outside Europe. Encryption, access control, and firewall management ensure a high level of security.

Developer-friendly and future-proof

Whether you’re creating new data products, testing data structures, or developing analytical models, a data lake in the STACKIT Cloud offers you maximum flexibility. You decide how data is processed, combined, or versioned—based on your requirements.

Resource-efficient scalability

You pay only for what you need—whether it’s small prototypes or large-scale production environments. With elastic provisioning of storage and computing power, you avoid overprovisioning and use your resources efficiently.

What Is a Data Lake and Why Is It Important?

The digital world produces massive amounts of data every day: click behavior in online stores, sensor data from industry, transactions in ERP systems, or image data from apps. This data is valuable—provided it can be properly stored, integrated, and analyzed. This is exactly where the data lake comes in.

A data lake is a central repository for all types of data—regardless of its structure, source, or format. Unlike a traditional data warehouse, which processes only structured data in tabular form, a data lake also allows for the storage and processing of unstructured or semi-structured data. This includes, for example, text files, images, log data, or JSON files.

The goal of a data lake is to capture as much data as possible in its raw format so that it can be explored later as needed. As a company, this allows you to create a flexible data foundation that you can use for a wide variety of purposes—from traditional business intelligence and exploratory data analysis to machine learning applications.

How does a data lake work?

A typical data lake follows four basic steps:

1. Data Ingestion

Raw data is transferred to the data lake from various sources—such as CRM, ERP, or logistics systems—via interfaces, APIs, or data pipelines.

2. Storage

The data is stored in a central location—often in a distributed file structure. It is retained in its original form (schema-on-read).

3. Processing

Depending on the needs, the data can be aggregated, filtered, normalized, or structured—for example, for further processing in a data warehouse or for direct analysis using tools such as Apache Spark, Python, or SQL engines.

4. Analysis and Use

Users—such as data scientists or business departments—access the data lake using appropriate tools. They can create new analytical models, generate reports, or use the data for their own products.

Typical Use Cases for Data Lakes

Visualization of flowing digital data streams and binary code for modern IoT and sensor data management.

IoT and Sensor Data Management

Machines or devices send data streams at short intervals—a data lake stores this data efficiently and makes it available for later analysis.

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Clickstream Analysis in E-Commerce

Visitor behavior on websites can be tracked in real time and analyzed later for personalization or optimization.

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Archiving Raw Data

Legal requirements or quality assurance necessitate the long-term storage of original data—the data lake fulfills this requirement without incurring high operating costs.

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Development of AI Models

Training data for machine learning requires large, diverse datasets. The data lake provides these—unmodified, versionable, and available at any time.

Graphical representation of merging digital documents from various source systems into a central database.

Data Integration from Various Systems

When information from ERP, CRM, and external platforms needs to be integrated, data lakes serve as a central repository and enable consistent analysis.

Tips for successfully implementing a data lake

A data lake only realizes its full potential when planning, structure, and security are in place. The following tips will help you set up your environment efficiently and operate it sustainably.

Define clear goals early on

Not every data lake needs to store all data from the very beginning. Define specific use cases: Do you want to perform analyses, store historical data, or train machine learning models? Your requirements will determine the design, structure, and tools.

Don’t Neglect Data Quality and Metadata

A common mistake is failing to describe the stored raw data. Without metadata, it becomes impossible to trace later what was stored and in what format. Therefore, use a metadata management system—such as a central repository for documenting the source, format, and content.

Consistently implement access controls

Especially with heterogeneous data sources, access rights should be controlled at a granular level. STACKIT supports role-based access concepts as well as integration into private networks. This ensures security and meets regulatory requirements—particularly when it comes to personal data.

Select tools strategically

Not every tool is suitable for every data type. Use proven SQL tools for structured analyses; for large data sets, Apache Spark or comparable frameworks are suitable. Make sure to ensure integration with existing cloud environments and workflows.

Keep an eye on costs and resources

A data lake is scalable—but that doesn’t mean unlimited, uncontrolled growth. Plan using storage classes (e.g., for infrequently used data), automate data archiving, and analyze access patterns. STACKIT supports you with a transparent pay-per-use model and monitoring features.

Define data lifecycles

Not all information needs to be stored permanently. Establish policies for retention, archiving, and deletion. This protects against unnecessary costs and helps ensure compliance with legal requirements such as the GDPR.

Promote Collaboration

A data lake is a shared resource for developers, analysts, and business units. Establish clear interfaces, roles, and processes so that everyone involved can work efficiently—without redundant storage locations or inconsistent results.

Data Lake with STACKIT

A data lake is the modern solution to complex data requirements. It enables centralized storage and flexible processing of large volumes of data—regardless of whether they are structured, semi-structured, or unstructured. This makes it the foundation for cutting-edge data analytics, machine learning, and data-driven products.

STACKIT offers the ideal cloud infrastructure for this: European, sovereign, and GDPR-compliant. Companies benefit from high security, scalability, developer-friendly integration, and transparent resource management. Whether for developing new solutions, archiving, or real-time analysis—with a data lake in the STACKIT Cloud, you lay the technical foundation for your data strategy—and uncover new potential in your existing data.

FAQ – Frequently Asked Questions About Data Lakes with STACKIT