At a glance
- Stores structured, semi-structured, and unstructured data without requiring a predefined structure.
- Combines the flexibility of a data lake with the analytical capabilities of a data warehouse.
- Provides a foundation for AI applications and data-driven business management.
What would you like to know about data lakes?
Your input is sent to an AI service to answer it and is stored to improve the answers. Please do not enter any personal data. Privacy policy
Data lake definition
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.
„With Bissantz, we brought our scattered data sources together in a consistent architecture. Today our business units access uniformly defined KPIs instead of manually reconciling data states.“Head of Controlling
Industrial company
What types of data can a data lake store?
A data lake can store structured, unstructured, and semi-structured data:
| Data type | Examples |
|---|---|
| Structured data | Tabular datasets from relational databases, numerical information |
| Semi-structured data | JSON, XML, and CSV files |
| Unstructured data | Text documents, PDFs, audio and video files, images, social media content, emails |
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.
Trusted by market leaders and hidden champions
Is a data lake a database?
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.
Why are data lakes useful? – benefits of data lakes
Data lakes give organizations a powerful way to store, manage, and make large volumes of data available for analytics. Key benefits include:
Data lake vs. data warehouse: What is the difference?
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.
| Characteristic | Data lake | Data warehouse |
|---|---|---|
| Data structure | Raw, unstructured, schema-on-read | Structured, schema-on-write |
| Data types | Structured, semi-structured, unstructured | Primarily structured data |
| Flexibility | Very high | Limited |
| Primary use | Exploration, AI, machine learning, big data | Reporting, BI, standardized analytics |
How does a data lake work? – Data lake architecture
The architecture of a data lake consists of several layers:
- Data ingestion (Ingestion Layer): Takes in data from various sources, for example databases, ERP systems or IoT devices, both in real time and at regular intervals.
- Storage Layer: Stores large volumes of different data formats centrally, flexibly and scalably.
- Processing Layer: Cleanses, transforms and analyzes the data. Technologies such as Apache Hadoop or Spark enable the processing of large volumes of data.
- Data catalog and metadata (Metadata Layer): Documents the origin, structure and meaning of the data and makes it easier to find and manage.
- Access Layer: Makes the data available for analyses, reports and dashboards. For data visualization, you can use tools such as DeltaMaster, for example.
What is a data lakehouse?
A data lakehouse is a modern data architecture that combines the flexibility of a data lake with the structured analysis capabilities of a data warehouse. It integrates functions such as ACID transactions, data versioning and schema enforcement, thereby offering:
- Better data management without switching systems between lake and warehouse.
- Higher data quality and integrity through structured governance on a flexible data base
The data lakehouse eliminates data silos and makes it possible to manage and analyze structured and unstructured data together – on a single, consistent architecture.
Use cases: where are data lakes used?
Data lakes are used wherever a wide variety of data sources need to be consolidated centrally and made usable for analyses. Typical fields of application range from controlling to production.
Where would you like to use data lakes?
Your input is sent to an AI service to answer it and is stored to improve the answers. Please do not enter any personal data. Privacy policy
Data lakes and Bissantz
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
| Function | Benefit |
|---|---|
| Analysis of data sources | existing systems and business requirements are captured |
| Uniform KPI definition | consistent values for revenue, contribution margin and incoming orders |
| Data harmonization | different structures and data states are consolidated |
| DeltaMaster for analysis and reporting | raw data becomes usable for controlling and management |
„Technically, we had long stored our raw data centrally, but it was only the consulting from Bissantz that made it usable from a business perspective. We now create reports and variance analyses across all locations on a reliable foundation.“BI Manager
Retail company
Frequently asked questions
What is a data lake in simple terms?
A data lake is like a giant digital reservoir: all kinds of data can flow into it — whether tables, text, images, or videos — and the data is only structured when it is needed. Unlike a database, which structures data as it is stored, a data lake initially keeps data in its raw form.
What is schema-on-read, and why does it matter?
Schema-on-read means that the structure of the data is defined when it is queried rather than when it is ingested. This provides maximum flexibility: the same data can be interpreted differently for different analytical purposes without having to transform it in advance.
When should a company implement a data lake?
A data lake makes sense when an organization generates large volumes of heterogeneous data, plans to use AI or machine learning, or needs to break down existing data silos. For purely structured operational reporting, a data warehouse may be sufficient.
What is the difference between a data lake and a data lakehouse?
A data lakehouse combines the flexibility of a data lake with the structure and analytical capabilities of a data warehouse on a single platform. It adds features such as ACID transactions, schema enforcement, and data versioning to the data lake, potentially eliminating the need for a separate data warehouse in many scenarios.
How does Bissantz support the use of data lake data?
DeltaMaster integrates the data required for analysis, calculates metrics using consistent business logic, and presents them in dashboards and reports. This turns raw data in the data lake into decision-relevant information for controlling and management.
Summary
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.
Design data lake solutions step by step
Bissantz supports you with data integration, data architecture, and the design of effective data lake solutions for your Business Intelligence environment — step by step and tailored to your needs.
Data warehouse consulting for a robust BI data foundation