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Glossar
Data Management

Data Lake

A data lake is a central repository that stores large amounts of raw data in its original format.

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What is a data lake?

In contrast to data warehouses, the structured data For specific analytical purposes, data lakes store all types of data, regardless of whether they textured, semi-structured, or unstructured are, and whether or not they are relevant to the current analysis.

Key features of data lakes

  • Raw data storage: Data lakes preserve the original state of data without transforming or structuring it before storage. This allows complete flexibility for later analyses.
  • Scalability: Data lakes are highly scalable and can collect large amounts of data from various sources record.
  • Cost friendliness: Storing raw data in data lakes is compared to data warehouses less expensive.
  • Flexibility: Data lakes are suitable for analyzing a wide range of data, including text, images, video, and sensor data.
  • Big data support: Data lakes are ideal for storing and processing large amounts of data (big data).

Benefits of data lakes

  • Improved data usage: Data lakes make it possible to use all available data, including unstructured data, for new insights and innovations.
  • Accelerated analyses: By storing raw data, analyses can be carried out faster and more efficiently.
  • Increased flexibility: Data lakes make it possible to create new data sources and easy to integrate analysis requirements.
  • Lower costs: Data lakes are less expensive to manage than traditional data warehouses.
  • Big data support: Data lakes are scalable and can handle large amounts of data.

Data lake use cases

  • Customer data analysis: Analyze customer data from various sources to understand customer behavior and improve customer satisfaction.
  • Fraud detection: Identifying fraudulent activity by analyzing transaction data and other relevant information
  • Risk management: Assessment and management of risks by analyzing market data, sensor data, and other relevant information.
  • Product development: Analyzing customer data and market data to develop new products and services.
  • Research and development: Analyze large data sets to gain new insights and innovations.

Data lake challenges

  • Data quality: The quality of the data in a data lake must be ensured before analysis.
  • Data security: Data lakes must be protected from unauthorized access.
  • Complexity: Managing and analyzing data in a data lake can be complex.
  • Lack of standardization: There are no uniform standards for implementing and managing data lakes.

We are happy to assist you with Design and implementation of data lakes or suitable solutions.

Note: Our team benefited from the support of AI technologies while creating and maintaining this glossary.

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Data Architecture

The right tools in the right place. Update the existing infrastructure or renew most of it.

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Data Governance

Not boring or stuffy at all: Aligning ideas and rules

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Data Organization

So that the handling and responsibility of data is structured and everyone knows its responsibilities.

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Reporting & Business Intelligence

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