What is a data lake?

A data lake is a centralized storage platform that ingests large volumes of structured, semi-structured, and unstructured data in its raw form and makes it available for analytics, machine learning, and big data applications. Unlike traditional databases, a data lake does not impose a fixed structure when data is ingested. Instead, the schema is defined when the data is queried. Bissantz helps companies design and leverage data landscapes that include data lakes.

A data lake is a centralized storage platform that enables organizations to store and manage large volumes of data in a wide range of formats. Raw data is ingested and stored in its native format. This means that both structured data, such as database tables, and unstructured data, such as text documents, videos, or social media content, can be stored in a data lake.

The stored data can then be used for big data analytics, machine learning, real-time analytics, reporting, and other modern applications. Data lakes provide a comprehensive and flexible view of an organization’s data, creating a strong foundation for data-driven decision-making.

A data lake can store structured, unstructured, and semi-structured data:

The ability to store data in different formats makes data lakes a valuable resource for organizations that need to work with broad and diverse datasets for analytics and innovative applications.

A data lake is not a traditional database. It is a flexible storage platform designed to ingest large volumes of raw data in its original format. While databases are specifically structured to store particular types of data in tables and support real-time queries, a data lake serves as a central repository for a wide range of data types, from structured to unstructured data.

Data lakes give organizations a powerful way to store, manage, and make large volumes of data available for analytics. Key benefits include:

A data warehouse typically relies on relational database management systems and organizes data from multiple sources in a central repository using predefined schemas. Data warehouses are optimized for structured data.

A data lake, by contrast, can also store data from non-relational databases in its raw format without imposing a fixed schema, making it more flexible. Data lakes are often preferred when working with unstructured and semi-structured data.

A data lake architecture consists of several layers:

A data lakehouse is a modern data architecture that combines the flexibility of a data lake with the structured analytical capabilities of a data warehouse. It incorporates features such as ACID transactions, data versioning, and schema enforcement, providing:

• Better data management without switching between systems
• Higher data quality and data integrity through structured governance on a flexible data foundation

A data lakehouse helps eliminate data silos and makes it possible to manage and analyze structured and unstructured data within a single, consistent architecture.

A data lake provides the technical foundation for making large volumes of diverse data centrally available. But to generate real value, it is crucial to structure, integrate, govern, and prepare that data for specific business requirements.

Bissantz helps companies develop the right data architecture and integration strategy — from analyzing existing data sources and systems to defining appropriate data models and metrics and designing efficient analytics and reporting processes. The focus is not on technology for its own sake, but on determining what information the organization actually needs for planning, controlling, and management — and how that information can be delivered reliably.

Another key focus is turning technical data assets into business-ready information. Bissantz helps organizations harmonize data sources, define metrics consistently, and prepare data so that business teams and management can derive reliable insights from it. Tools such as DeltaMaster can be used for analytics, planning, and reporting.

Practical example

An international company wants to bring its sales, finance, and production data together in a data lake. Although the data already exists in various systems, differences in data structures, metric definitions, and data quality make consistent analysis difficult.

As part of a consulting project, Bissantz first analyzes the existing data sources and the business requirements of the participating departments. Relevant metrics — such as revenue, contribution margin, and order intake — are then defined consistently, and the required data flows and structures are designed. This creates a robust architecture that brings operational data sources and analytical requirements together.

The benefits quickly become apparent: Instead of manually reconciling different versions of the data, business teams can work with consistent and traceable information. Analyses and reports can be created faster, and deviations can be evaluated consistently across departments and locations. The data lake thus becomes more than a data repository — it becomes a solid foundation for effective business management

A data lake provides a flexible foundation for modern data architectures: it stores all types of data in their raw form, helps eliminate data silos, and creates a foundation for AI, machine learning, and big data analytics. Combined with a data warehouse — or implemented as a data lakehouse — it can form a powerful, future-ready data infrastructure. With DeltaMaster, Bissantz makes data stored in the data lake usable for controlling and Business Intelligence: structured, visualized, and focused on better decisions.

data warehouse data governance controlling business intelligence AI