The right data warehouse tool for your data strategy

Central database instead of data silos: what a data warehouse really does

A Data warehouse (DWH for short) is a structured, central data archive for your company. This is where information from various source systems is stored, consolidated and prepared for analysis.

Why this is necessary:
Data is constantly being generated in day-to-day business - through orders, invoices, stock movements or customer interactions. This data is often distributed:

• in accounting
• in the ERP system (e.g. SAP)
• in the merchandise management system
• or in the CRM

These systems enable fast transactions, but reach their limits when it comes to strategic data evaluation. This is where a data warehouse comes in - as a central basis for analysis across system boundaries.

The difference between operational and planning data

There are basically two types of data:

1. operational data
They arise in day-to-day business - e.g. through orders, payments or bookings - and are short-lived, transaction-oriented and changeable.

2. dispositive data
This data is processed, summarized and stored long-term for analysis. Typical questions are

• How has turnover per region developed over the last three years?
• Which products achieve peak seasonal sales?
• How do stocks develop over the course of the year?

Why is a data warehouse important?

A data warehouse helps companies to maintain an overview of their data and derive targeted insights for the future. It is the technical basis for many modern forms of corporate management - such as

• Business Intelligence (BI): i.e. the analysis of company data
• Key performance indicators (KPIs): e.g. profit, turnover, productivity
• Strategic decisions: based on reliable data instead of gut feeling

Normally, only specially authorized persons (e.g. analysts or controllers) can access the data warehouse - and usually only for Reading and evaluatingnot to change.

Data Warehouse Tools: The bridge between raw data and business insights

A modern data warehouse is at the heart of data-driven corporate management. However, the right tools are needed to turn the collected information into useful insights: so-called data warehouse tools.

You take on the tasks:

• Integrate data from different sources
• Structure, clean up and transform content
• Prepare analyses and provide them with high performance

Data warehouse tools are the link between the daily data flow in the company and the strategic use of this data for business decisions.

What is a data warehouse tool - and why do you need it at all?

A data warehouse tool is used to centrally store, structure and analyze large volumes of data from different source systems. It enables data to be prepared systematically, made available consistently and used strategically in the long term.

A data warehouse tool can only work effectively if it is supplied with data from other systems. It is not a system that generates data itself, but one that:

Receives data from various sources
(e.g. from ERP systems, accounting, CRM, Excel files, cloud services)
prepares and stores this data in a structured way
(e.g. according to time periods, regions, product groups)
provides the data for analyses
(e.g. for BI tools such as Power BI, which create reports and dashboards from it)

Data warehouse tools at a glance:

Azure & Fabric in focus - data integration and analysis in the Microsoft ecosystem

Companies with Microsoft infrastructure quickly come across Azure - the cloud platform for scalable data processing. With services such as Azure Synapse Analytics, Azure SQL and Azure Data Factory, a modular set of tools for data integration, storage and analysis is available.

Microsoft Fabric goes one step further: it combines previously separate services into one holistic platform. Whether data engineering, real-time analyses or BI - Fabric creates an end-to-end lakehouse model that breaks down silos and strengthens collaboration between IT and specialist departments.

SAP BW/4HANA

A scalable enterprise data warehouse based on SAP HANA. It offers in-memory processing, fast analyses and deep integration into SAP systems (e.g. S/4HANA). Ideal for companies with existing SAP environments that want to process high volumes of data with agility and high performance. Access for business users is often via SAP frontends - perfect for reporting, planning and advanced analytics.

Oracle Essbase & Hyperion

Essbase is a multidimensional OLAP cube server ("Extended Spreadsheet"), originally from Arbor/Hyperion, now part of Oracle. It enables:

• High-performance cubes
  with real-time aggregation
• Complex financial and business models
• Seamless integration in Excel, Word & PowerPoint

Hyperion complements the speed with comprehensive tools for planning, consolidation and reporting. Together, they stand for reliable, financially-oriented BI - even with large volumes of data and demanding calculations
Oracle Hyperion

(Usually closely integrated with Essbase)

As an EPM and planning platform, Hyperion supplements Essbase with strategic management reporting, budgeting and forecasting. Particularly suitable for CFOs, controlling departments and international corporations that want to implement structured planning processes.

Databricks (Lakehouse)

Databricks combines data warehouse functionality and data lake with the lakehouse approach:

• Spark-based processing on a large scale
• Batch & streaming support
• Ideal for data science, AI and ML
• Scaled, flexible storage and analysis

Snowflake

Snowflake is a fully cloud-based data warehouse platform that is characterized by its separation of storage and computing power, high scalability and user-friendliness.
characterized. Companies can load, analyze and share data flexibly - even across system and company boundaries. Thanks to automatic scaling and a pay-per-use model, Snowflake is particularly attractive for organizations with growing data volumes, variable workloads and modern analytics requirements.

Particularly suitable for:

• Data-driven companies with a high query load
• Multi-cloud strategies (Snowflake runs on AWS, Azure and GCP)
• Use cases such as data sharing, data products and real-time analysis

What do OLAP, OLTP and ETL mean - and why are they important for a data warehouse tool?

OLTP - The world of day-to-day business

OLTP stands for Online Transaction Processing - the processing of transactions in real time.

Typical examples:

• A customer orders a product in the online store
• An invoice is generated in accounting
• A warehouse posting is made in the ERP system

This data is generated in the day-to-day business and are stored by systems such as ERP, CRM or store systems. They are Important in the short termbut Not ideal for long-term analyses.

OLAP - The world of evaluations

OLAP stands for Online Analytical Processing - the systematic analysis of data.

Examples:

• How are sales developing in different regions?
• Which products are particularly profitable?
• What does the annual comparison look like for cost centers?

OLAP required Structured, prepared datato enable fast and reliable analyses. This data usually comes from a data warehousethat she has collected and prepared beforehand.

ETL - The link between the two worlds

ETL stands for Extract, Transform, Load:

1. Extract (Extract): Retrieve data from different systems
2. Transform: Clean up, standardize, calculate data
3. Load: Feed data into the data warehouse

ETL is the technical process by which operational data is transferred from the OLTP world into an analyzable OLAP form.

What does all this have to do with a data warehouse tool?

A good data warehouse tool:

• Connects to various source systems (e.g. ERP, CRM, Excel)
• uses ETL to cleanse and prepare the data
• Stores the transformed data in a structured way in a data warehouse
• provides the data for OLAP analyses - e.g. via BI tools such as Power BI

Without these basics, companies cannot make reliable evaluations. With a data warehouse tool, however, you can transform unstructured, scattered data into a Clear basis for decision-making.

In order for this processed data to be usable, a suitable front end is required - in other words, software that translates complex data models into comprehensible reports and dashboards.

This is exactly where a business intelligence tool like Microsoft Power BI comes into play. It connects to the data warehouse, accesses the cleansed data and presents it clearly for specialist departments, management or the board.

Power BI in check - functions, advantages & limits

Power BI is Microsoft's BI platform for visualization, analysis and reporting. It is particularly suitable for Microsoft-driven infrastructures - from SMEs to large corporations.

Typical features:

• Connection to Excel, SQL, SAP, Salesforce, Azure and much more.
• Interactive dashboards & reports
• Data modeling with DAX

Power BI Desktop is free of charge. For collaboration or web publishing Power BI Pro required. For app developments Power BI Embedded ready.

With automated updates, clear access rights and interactive dashboards, Power BI offers more than traditional Excel solutions.

When does Power BI make sense for SMEs?

• With existing Microsoft infrastructure (e.g. Office 365, Azure).
• When specialist departments should work independently with data
• If a low-cost entry is desired without immediately implementing a complete data warehouse

Power BI vs. alternatives - Is Snowflake better suited?

Is Power BI better than Excel?

Power BI is the modern answer to many Excel problems: no version chaos, automated updates, user-friendly dashboards, clear assignment of rights. For more complex BI requirements, Power BI is clearly superior to Excel.

Snowflake in focus: Why the cloud DWH is so popular.

Snowflake is a cloud-based data warehouse that impresses with its separation of storage and computing power, automatic scaling and simple user interface.
It is suitable for companies that work with very large volumes of data or want to develop data products.

Advantages:

• Fully cloud-based
• High scalability
• High performance even with many simultaneous queries

What alternatives are there to Power BI?

• Tableau: Very visual, high level of customization
• Qlik: Associative data model, good performance
• Looker (Google): Model-driven BI, especially strong in GCP projects
• ETL tools: e.g. Talend, Apache NiFi for data integration

Integration into existing systems - what you should bear in mind

Which data sources can be connected?

Power BI supports a wide range:

• Databases: SQL Server, MySQL, PostgreSQL, Oracle
• Files: Excel, CSV, XML
• Cloud services: Azure, Google Analytics, Salesforce
• SAP systems: e.g. via OData or BW Connector

Snowflake can be linked directly to many systems via ETL tools or API.

How well does Power BI work with SAP & Co.

SAP data can be integrated, but requires additional connectors or middleware. Semantically clean integration requires know-how, but is technically possible. 

What technical requirements should be met?

• User and rights management (e.g. via Active Directory)
• Clear data governance (access rights, data models)
• Integration with existing cloud/on-prem systems

Tool selection in the context of your digital strategy: what you should consider

The selection of the right tool should not be made in isolation, but always in the context of your overall strategy. In many cases, a targeted technology mix is more effective than trying to cover everything with one solution.

Decision support: When to use Power BI, when Snowflake, when something else?

Scenario	Tool
Self-service analyses & visualization	Power BI
Planning with approvals & workflows	Power BI + Low Code (e.g. nedyx)
ETL and data integration	Azure Data Factory, Talend
Central, scalable DWH	Snowflake

Three strategic questions that you should clarify before choosing a tool:

1. Which systems and data sources need to be connected - today and tomorrow?
   
2. What knowledge do specialist departments and management need for control?
   
3. How flexible and scalable does your architecture need to be in the corporate context?

Conclusion

Whether Power BI, Snowflake or a combination of several tools - the decisive factor is how well the solution fits into your existing system landscape, which requirements you want to cover and how future-proof your architecture is. Thinking strategically here not only saves costs, but also creates the basis for sustainable, data-supported decisions throughout the company.