Data Warehouse
A data warehouse (DHW) is a specialized database that collects and stores large amounts of historical data from various sources and makes it accessible for analysis and business decisions. Expert knowledge is of the utmost importance for the selection and customized implementation.
What is a data warehouse?
By definition, a data warehouse is the heart of your data-driven corporate strategy. It is a central, specially developed platform that consolidates and stores data from various sources and makes it available for reporting, analysis and data mining. In contrast to traditional databases, a data warehouse is optimized to efficiently analyse large volumes of data, uncover trends and enable well-founded decisions.
We know how to do it
The aim is to provide you with a clear overview of your business-relevant key figures at all times - regardless of whether they originate from financial systems, CRM tools or external data sources. Plus: analytical data should be specifically separated from the real-time data of the operational business. If both run on the same system, this could reach its performance limits, as data analyses are usually very computationally intensive. A well-structured data warehouse ensures that your data is consistent, up-to-date and available in high quality. This gives you valuable insights and creates the basis for sustainable success.
Flexible instead of rigid - the agile data warehouse
Traditional data warehouse approaches quickly reach their limits when requirements are constantly changing. Modern companies need a data architecture that grows with them and adapts flexibly to new issues. With an agile data warehouse, you can react dynamically to changing requirements. Iterative development, rapid adjustments and continuous added value - this keeps your database up-to-date and fit for the future. This not only saves you time, but also gives you a long-term competitive advantage through data-driven decisions. Our article on agile data warehousing explains the principles behind this and how you can successfully shape change.
Our technologies
Microsoft SQL Server
Microsoft SQL Server is a powerful database solution that enables companies to securely manage and analyze large amounts of data. With extensive functions for database management, business intelligence and machine learning, SQL Server creates the basis for well-founded decisions and optimizes processes in a modern data strategy.
Microsoft SSAS
Microsoft SSAS is the powerful platform for OLAP and data mining that allows you to analyze your data efficiently and gain valuable insights. Use the possibilities of modern analysis models to make well-founded decisions and optimize your data strategies.
Microsoft Fabric
Microsoft Fabric is an integrated data platform that supports companies in analyzing and using their data. It enables centralized data management, fast analysis and informed decision-making. With functions for real-time analysis and AI-supported insights, Fabric optimizes the data strategy and increases efficiency.
Oracle Hyperion Essbase
Maximize the potential of your financial and business analysis with Oracle Essbase. This powerful OLAP solution enables the creation of complex data models and provides real-time analytics to help you make informed decisions and make your business processes more efficient.
Oracle Database
Maximum performance, security & scalability - Oracle Database is one of the most powerful databases for companies. Whether on-premise or in the cloud, it offers flexible solutions for efficient data management. Find out what features and benefits Oracle Database offers and how it differs from SQL Server & Snowflake!
Your expert for Data Warehouse
Do you have questions about data warehousing? Our expert Markus Bremer has many years of experience in digitization projects and will be happy to talk to you without obligation.
5 Advantages of a data warehouse
- Central data access: A data warehouse combines all relevant data from different sources in one central location. This gives you a holistic view of your company and facilitates access to consistent, complete data.
- Improved decision-making: Through structured and prepared data storage, a data warehouse supports well-founded, data-driven decisions. It helps you to recognize patterns and trends that would not always be visible with purely operational data sources.
- Real-time reporting and historical data analysis: A data warehouse enables you to carry out fast, precise analyses and visualize the latest information in clear dashboards and reports. This allows you to react to changes in real time. However, a data warehouse also stores historical data that helps you to identify long-term trends and make forecasts for the future. This is particularly valuable for strategic planning and risk management.
- Data quality and consistency: With a modern data warehouse, you benefit from the high quality and consistency of your data as standard. This ensures that all departments can work with the same, reliable information.
- Significant time savings: A data warehouse takes over the time-consuming task of data cleansing and preparation automatically. Instead of manually consolidating data from different sources, you benefit from a system that provides data in a standardized and usable form. This gives you and your team more time for value-adding tasks, such as in-depth analyses and strategic decisions.
3 mistakes you should avoid when developing
should avoid when developing a data warehouse
- Lack of integration of clean data sources: A data warehouse is designed to cleanse and consolidate data from different sources. Nevertheless, it is important that the source systems guarantee at least basic data quality. If the data sources are incorrect or incomplete, even a data warehouse cannot provide reliable analyses. Make sure that your data sources are stable and traceable in order to exploit the full potential of the data warehouse.
- Lack of scalability: Make sure that your data warehouse is scalable from the outset in order to keep pace with growing data volumes and additional requirements. A non-scalable system can quickly reach its limits and make future expansions considerably more difficult.
- Lack of user-friendliness: A data warehouse should not only be technically sophisticated, but also user-friendly. If the user interface and analyses are too complex, the system will not be used effectively and will not be able to realize its potential.
Definition: What is a data warehouse?
A data warehouse is a central data platform in which large volumes of structured data from various sources are brought together, stored and prepared for analysis. It serves as the basis for reporting, business intelligence and data-based decisions. In contrast to operational systems, a data warehouse is optimized for the efficient processing of historical data. ETL (Extract, Transform, Load) processes are used to consolidate, cleanse and transfer data into a standardized format. In this way, the data warehouse enables consistent, reliable and efficient analysis - even over long periods of time. It helps companies to identify correlations, analyze trends and make strategic decisions.
Why have data warehouse systems
increasingly important?
The history of data warehousing begins in the 1980s, when companies first realized that their data from various sources was difficult to analyze. At that time, databases were primarily used for operational purposes, not for strategic decisions.
To solve this problem, companies developed central storage locations that were used exclusively for analysis: The first data warehouse systems. These tools were designed to collect and structure data and make it available for reports - a revolution in business analysis.
With the rise of big data and technologies such as the internet in the 1990s and 2000s, the volume and variety of data grew exponentially. At the same time, the requirements increased: Companies not only had to store data, but also be able to recognize patterns and predict trends.
Today, data warehouse tools are indispensable for strategically-minded companies. They make it possible to manage huge amounts of data efficiently and make it available in the form of real-time insights. From optimized supply chains to data-driven marketing strategies - the importance of these systems is constantly growing.
The development of the data warehouse in conjunction with the topic of business intelligence reflects the change in how companies use data as the key to success. It now functions not only as a storage location, but as a strategic tool that makes your decisions more precise and your analysis processes more efficient.
What challenges can be overcome
overcome with a data warehouse?
A data warehouse helps you to master the biggest challenges in handling data:
Carrying out data integration and consolidation
In most companies, data exists in a wide variety of formats and from different sources. A data warehouse consolidates this information so that you have a central, uniform access point. This helps you avoid redundant data and inconsistencies.
Optimize data quality
Poor data quality leads to incorrect analyses and decisions. A data warehouse provides a standardized and cleansed data structure that delivers the reliable, consistent data you need to make accurate business decisions.
Reduce complexity in data analysis
Processing large amounts of data from different sources and systems is difficult without a centralized system. A data warehouse provides you with a scalable solution to analyze data in real time and gain valuable insights.
Enable real-time data access
Outdated data can lead to wrong decisions. With a data warehouse, you have quick access to current and relevant data at all times, enabling you to make needs-based decisions.
Enable unlimited scalability
As data volumes grow, so do the demands on your infrastructure. A data warehouse can be scaled and adapted to grow with your company and remain efficient in the future.
Data warehouse architecture:
How is a modern data warehouse structured?
A modern data warehouse consists of several important components that work together to store, manage and analyze your data efficiently. The structure usually includes:
- Data sources and integration: Data is merged from various sources such as transaction systems, CRM databases and external data feeds. These sources are integrated into the data warehouse using ETL processes (extraction, transformation, loading) to ensure consistent and complete data collection.
- Data modeling: In contrast to a data lake, a data warehouse (DWH) also has toolkits for structured data modeling in order to divide the data into logical units - for example, dimensions (e.g. time, geography, products) and facts (e.g. sales figures, turnover). This makes it easier to quickly query and analyze the data.
- Data storage: Data is stored in a central repository that is optimized to efficiently process and access large volumes of data. Modern data warehouses rely on cloud-based storage solutions that offer scalability and high availability.
- Data analysis and business intelligence: The data warehouse makes it possible to integrate advanced analysis tools and BI platforms. With these tools, you can gain deeper insights into your data, for example through dashboards, reports or predictive analytics, in order to make better decisions.
- Data security and governance: Security policies and governance measures are crucial to ensure that your data is protected, consistent and processed in accordance with legal requirements. This includes encryption, access controls and audit logs.
A modern data warehouse is therefore not just a storage location for data, but an integrated platform that enables you to manage data efficiently, access it quickly and make data-based decisions in real time.
Data warehouse vs. data mart vs. operation
Data stores - what's the difference?
The terms data warehouse, data marts and operational data stores (ODS) refer to different types of data storage and use that differ in terms of scope, objective and function.
Data warehouse
A data warehouse is a central collection of data from various sources that is used for comprehensive analyses and reporting. It stores historical data and provides a comprehensive view of the company's performance. Data warehouses are particularly suitable for complex, long-term analyses and strategic decision-making.
Data mart
A data mart is a specialized section of a data warehouse that focuses on a specific topic or department. It contains only the data relevant to certain user groups, such as sales figures for the sales team or financial data for the finance department. Data marts are more focused and enable faster data processing for specialized analyses.
Operational Data Store (ODS)
An ODS stores current, operational data in near real time and is often used for daily business processing. Unlike the data warehouse, which includes historical data, an ODS focuses on current, transactional information and allows for quick retrieval of real-time data.
To summarize:
- The data warehouse is responsible for long-term, comprehensive analyses.
- Data marts offer specialized, department-related data for targeted analyses.
- The ODS processes current, operational data and supports daily business decisions.
3 tips for developing a data warehouse
Rely on a step-by-step implementation
Instead of developing the entire data warehouse including software all at once, implement it step by step. Start with the most important data sources and expand the system iteratively. This will ensure that you can identify and rectify problems at an early stage without jeopardizing the entire project. Targeted prioritization in data warehouse management also helps to use resources more efficiently and achieve initial successes quickly.
Use a data vault model for maximum flexibility
The data vault model allows you to flexibly integrate data from different sources while ensuring a high level of scalability. It offers a structured way of dealing with the unpredictable requirements of the future, making the data warehouse more resistant to changes in the data landscape.
Focus on real-time data integration
Most data warehouses use batch processing, but this can lead to delays when you need real-time data. So if you want to make business-critical decisions in real time, you need to integrate data streams continuously and in real time. Rely on streaming data integration and technologies such as Apache Kafka or Lambda architectures to make the data immediately available. This enables you to react to market changes at lightning speed and adapt your business strategy.
Our services
- Identification and prioritization / selection of use cases
- Analysis of requirements (data sources, data volume, requirements for hardware and software resources, etc.)
- Selection and development of a data warehouse platform
- Prototyping
- Standardization of processes for data provision (incl. compliance)
- Data storage, historization and archiving
- Automation of data transfers and reports
- Documentation
- Deployment and continuous delivery
- Definition of SLAs
- and much more.
How we develop your individual tool in 6 steps
Phase 1: Listening & understanding
In an initial consultation meeting, we analyze your specific challenges together. We look at where bottlenecks occur in your operational processes and identify hidden opportunities for optimization. Together, we develop solutions to make your operational business planning more efficient.
Phase 4: Briefing & handover
After a comprehensive quality check by our QA team, we implement your new operational planning system in your company (as a SaaS solution or in the cloud). From now on, you will benefit from a digital solution for integrated business planning with many intelligent functions that will make your life easier.
This is Partake
- Experience since 2007 in the areas of consulting & software development
- Special expertise in the areas of corporate planning, finance, business intelligence & project management
- Experience from over 800 successfully implemented digitization projects
- Professional project management and agile development
- Specialized in digitalization solutions for companies with complex structures
What our customers say
Our projects require a very high degree of customer focus, quality and reliability. This is exactly why we rely on a long-standing partner in Partake. In addition to high flexibility, we value the combination of professional and technical expertise in a very distinctive form.
"For us at DFS Deutsche Flugsicherung, safety and reliability are of the utmost importance. We also have this requirement in the information system and in the digitalization of processes. Our long-standing collaboration with Partake shows us that sound knowledge and IT experience make a decisive contribution to this."
We chose Jochen Brühl (GF) and his team because I was impressed by their expertise from years of collaboration on previous projects. With the help of the low-code platform from their technology partner nedyx®, we can drive our digitalization roadmap forward.
Make data-driven decisions from now on
- with a modern data warehouse
Do you regularly struggle with large, complex data volumes in data management? Do you want clear structures for storing your analysis data? Would you like to speed up both reporting and decision-making?
Then a modern data warehouse is the ideal solution for you! In a free consultation, we will show you how you can bundle and optimize your data management processes with the help of a data warehouse system - and thus gain a decisive competitive advantage.
Sounds promising? Then arrange your free consultation now.
Arrange a meeting
Select the desired date and arrange a non-binding and free initial consultation with Dieter Höfer (Partner & Sales Director). We will be happy to answer your questions and discuss solutions for your individual requirements.