Cookie Settings

By clicking "Agree," you consent to the storage of cookies on your device to enhance site navigation, analyze site usage, and support our marketing efforts. For more information, please refer to our Privacy Policy.

Reject
Agree
Glossary
Data Management

Data Lake

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

Meaning in detailContact us now

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.

Abstrakte Form eines Pfades

We are convinced that data is the key to success in today's world.

Mike Kamysz

Contact now
Abstrakte Form eines Pfades

Do you have questions around
Data Lake
?

Let´s TalkContactFollow us on LinkedIn

Passende Case Studies

Zu diesem Thema gibt es passende Case Studies

Alle Case Studies entdecken
Babymarkt is data-driven
eCommerce
5
Minuten Lesedauer

From data audit to individual architecture sketch

eCommerce thrives on data — babymarkt wanted to make even better use of it.
Couple reading a newspaper
Medien
5
Minuten Lesedauer

MediaPrint reinvents itself in terms of data technology

The publisher wanted a clear structure and the development of a data culture.

Which services fit to
Data Lake
?

<svg width=" 100%" height=" 100%" viewBox="0 0 62 62" fill="none" xmlns="http://www.w3.org/2000/svg"> <g clip-path="url(#clip0_5879_3245)"> <path d="M54.25 3.875H31C29.9726 3.87603 28.9876 4.28461 28.2611 5.01109C27.5346 5.73758 27.126 6.7226 27.125 7.75V27.125H7.75C6.7226 27.126 5.73758 27.5346 5.01109 28.2611C4.28461 28.9876 3.87603 29.9726 3.875 31V58.125H58.125V7.75C58.124 6.7226 57.7154 5.73758 56.9889 5.01109C56.2624 4.28461 55.2774 3.87603 54.25 3.875ZM17.4375 54.25V40.6875H25.1875V54.25H17.4375ZM54.25 54.25H29.0625V38.75C29.0625 38.2361 28.8584 37.7433 28.495 37.38C28.1317 37.0166 27.6389 36.8125 27.125 36.8125H15.5C14.9861 36.8125 14.4933 37.0166 14.13 37.38C13.7666 37.7433 13.5625 38.2361 13.5625 38.75V54.25H7.75V31H31V7.75H54.25V54.25Z" fill="currentColor"/> <path d="M34.875 15.5H38.75V19.375H34.875V15.5ZM46.5 15.5H50.375V19.375H46.5V15.5ZM34.875 27.125H38.75V31H34.875V27.125ZM46.5 27.125H50.375V31H46.5V27.125ZM34.875 38.75H38.75V42.625H34.875V38.75ZM46.5 38.75H50.375V42.625H46.5V38.75Z" fill="currentColor"/> </g> <defs> <clipPath id="clip0_5879_3245"> <rect width="62" height="62" fill="currentColor"/> </clipPath> </defs> </svg>

Data Architecture

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

<svg xmlns="http://www.w3.org/2000/svg" width=" 100%" height=" 100%" viewBox="0 0 32 32"><path fill="currentColor" d="m16 23l-2.139-1.013A5.022 5.022 0 0 1 11 17.467V11h10v6.468a5.021 5.021 0 0 1-2.861 4.52zm-3-10v4.468a3.01 3.01 0 0 0 1.717 2.71l1.283.608l1.283-.607A3.012 3.012 0 0 0 19 17.468V13zm-8.88 4.656A2 2 0 1 0 1 16c0 .789.46 1.464 1.124 1.79a13.946 13.946 0 0 0 9.569 11.535l.614-1.904a11.954 11.954 0 0 1-8.186-9.765m25.205 2.652l-1.904-.616a11.954 11.954 0 0 1-9.765 8.187A2 2 0 1 0 16 31c.789 0 1.464-.46 1.79-1.124a13.947 13.947 0 0 0 11.535-9.568m.551-6.098a13.946 13.946 0 0 0-9.569-11.535l-.614 1.904a11.954 11.954 0 0 1 8.186 9.765A2 2 0 1 0 31 16a1.992 1.992 0 0 0-1.123-1.79M16 1c-.789 0-1.464.46-1.79 1.124a13.947 13.947 0 0 0-11.535 9.568l1.904.616a11.954 11.954 0 0 1 9.765-8.187A2 2 0 1 0 16 1"/></svg>

Data Governance

Not boring or stuffy at all: Aligning ideas and rules

<svg width=" 100%" height=" 100%" viewBox="0 0 62 62" fill="none" xmlns="http://www.w3.org/2000/svg"> <g clip-path="url(#clip0_5879_2705)"> <path d="M7.75 50.375H15.5V58.125H7.75V50.375ZM27.125 50.375H34.875V58.125H27.125V50.375ZM46.5 50.375H54.25V58.125H46.5V50.375ZM48.4375 31H32.9375V27.125H29.0625V31H13.5625C12.5351 31.001 11.5501 31.4096 10.8236 32.1361C10.0971 32.8626 9.68853 33.8476 9.6875 34.875V46.5H13.5625V34.875H29.0625V46.5H32.9375V34.875H48.4375V46.5H52.3125V34.875C52.3115 33.8476 51.9029 32.8626 51.1764 32.1361C50.4499 31.4096 49.4649 31.001 48.4375 31ZM17.4375 3.875V23.25H44.5625V3.875H17.4375ZM21.3125 7.75H25.1875V19.375H21.3125V7.75ZM40.6875 19.375H29.0625V7.75H40.6875V19.375Z" fill="currentColor"/> </g> <defs> <clipPath id="clip0_5879_2705"> <rect width="62" height="62" fill="currentColor"/> </clipPath> </defs> </svg>

Data Organization

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

<svg xmlns="http://www.w3.org/2000/svg" width=" 100%" height=" 100%" viewBox="0 0 32 32"><path fill="currentColor" d="M15 20h2v4h-2zm5-2h2v6h-2zm-10-4h2v10h-2z"/><path fill="currentColor" d="M25 5h-3V4a2 2 0 0 0-2-2h-8a2 2 0 0 0-2 2v1H7a2 2 0 0 0-2 2v21a2 2 0 0 0 2 2h18a2 2 0 0 0 2-2V7a2 2 0 0 0-2-2M12 4h8v4h-8Zm13 24H7V7h3v3h12V7h3Z"/></svg>

Reporting & Business Intelligence

Not just beautiful dashboards, but information at a glance.

Follow us on LinkedIn

Stay up to date on the exciting world of data and our team on LinkedIn.

Follow on LinkedIn
Abstrakter Pfad des Data Institutes