Data Warehouse

Why a data warehouse is the basis for data-driven decisions in B2B marketing

In this article, you will learn what a data warehouse is, how it is defined and how it differs from a data lake. We also explain why companies can hardly do without a data warehouse these days and what added value it offers for your data strategy.

What is a data warehouse?

A data warehouse is a specially designed database that centralizes large amounts of structured data from different sources. The aim is to provide a consistent and reliable database that can be accessed by analysis and reporting tools.

In contrast to operational databases, the focus here is not on daily transaction processing, but on the efficient evaluation of historical and current data. The data is stored in a defined model (e.g. star or snowflake schema) and is optimized for analytical purposes. This enables companies to make data-driven decisions more quickly and on a more sound basis.

Data Warehouse Definition

A data warehouse can be defined as a central, topic-oriented, integrated and historicized database system that supports decision-making processes within the company. The data originates from various operational systems and is transferred to the warehouse in a standardized process – the ETL process (Extract, Transform, Load). During the transformation, the data is cleansed, standardized and prepared for analysis. The structure of a data warehouse is generally designed to enable high performance for queries and reports. This ensures that management and specialist departments can access important information quickly and reliably.

Data warehouse vs. data lake

Although both a data warehouse and a data lake store data centrally, the two approaches follow different concepts. A data warehouse processes and stores structured data in predefined schemas that have been specially developed for reporting and analysis. A data lake, on the other hand, takes raw data in any form – structured, semi-structured or unstructured – and stores it cost-effectively without immediate processing. While a data warehouse is ideal for standardized reports and KPIs, a data lake is more suitable for explorative analyses and machine learning applications. Today, both systems are often combined in hybrid architectures in order to utilize the advantages of both worlds.

Why do you need a data warehouse?

A data warehouse is indispensable if companies want to gain valuable insights from their scattered and heterogeneous data sources. Without a central data platform, information is often incomplete, inconsistent or difficult to compare. The data warehouse provides a remedy here by integrating, cleansing and standardizing all relevant data. This creates the basis for meaningful reports, dashboards and strategic analyses. Ultimately, a well-structured data warehouse supports managers in making fact-based decisions and strengthening the company’s competitiveness.

Data Warehouse – Basics and Architecture

The technical foundation for your data strategy

A data warehouse is based on a clearly defined architecture that ensures that data can be collected, processed and analyzed efficiently. The architecture is designed to consolidate large volumes of data from different sources and make them available for analytical purposes. Various components and models are used to structure the structure and operation of a data warehouse. This section provides you with an overview of the most important elements and architectural concepts of a data warehouse. This will help you to understand how the individual building blocks interact and why a clean architecture is crucial for success.

Components of a data warehouse

From the data source to the report

A classic data warehouse consists of several central components that together enable the smooth processing and analysis of company data. The data sources form the starting point and provide information from various operational systems such as ERP, CRM or accounting systems. In the next step, the ETL process (Extract, Transform, Load) ensures that this raw data is extracted, transformed and loaded into the data warehouse. The actual data warehouse (DWH) then serves as a central storage location where the processed data is available in a structured form. Finally, the data is visualized for the end user via business intelligence tools (BI tools) and made available for reports, analyses or dashboards.

Architecture models of a data warehouse

When setting up a data warehouse, different architecture models are used, which vary according to complexity and application purpose. In a single-tier architecture, an attempt is made to combine all functions – storage, transformation and analysis – in a single system, but this is rarely used. The two-tier architecture separates the data storage from the analysis tools, but is often not scalable in larger systems. The most widespread is the three-tier architecture, in which the data source, data warehouse and presentation layers are clearly separated from each other. This structure enables high performance, flexibility and scalability when processing and evaluating large volumes of data.

Data Warehouse vs. Data Lake

Two approaches for different requirements

Although data warehouses and data lakes are both central data storage solutions, they pursue different approaches and areas of application. While the data warehouse stores structured data in a defined schema and is optimized for analysis purposes, a data lake also stores unstructured or semi-structured data in its raw form. The data lake is particularly suitable for explorative analyses, data science and machine learning applications because it offers maximum flexibility. The data warehouse, on the other hand, is suitable for standardized reports, key figures and analytical evaluations with high data quality. In modern data architectures, both systems are often combined to enable both classic business analyses and innovative data science projects.

Data models in the data warehouse

Structured organization for efficient analyses

An important part of any data warehouse is the choice of a suitable data model that defines the structure of the stored information. The star schema is one of the most commonly used models: it focuses on a central fact table that is supplemented by several dimension tables. The Snowflake schema goes one step further by further normalizing the dimension tables and breaking them down into additional hierarchies – this increases complexity, but can save storage space. The Galaxy schema, also known as Fact Constellation, combines several Star schemas and is particularly suitable for large and complex data landscapes. The choice of the right data model significantly influences the performance and flexibility of the analyses in the data warehouse.

Data warehouse and the ETL process (Extract, Transform, Load)

How to get your data into the warehouse in a structured way

The ETL process is at the heart of every data warehouse project and forms the bridge between the operational systems and the analysis platform. Data is first extracted from various sources (Extract), then transformed (Transform) – i.e. cleansed, enriched and brought into a uniform structure – and finally loaded into the data warehouse (Load). This process ensures that all relevant information is clean, consistent and optimized for analysis. Without an ETL process, a central and reliable database would hardly be possible, as raw data is often incomplete, incorrect or inconsistent. A well-implemented ETL process therefore increases data quality and creates the basis for fact-based decisions.

Tools & technologies for the ETL process

There are numerous tools and technologies available today for implementing the ETL process. Well-known open source tools include Talend or Apache Nifi, which are highly flexible and adaptable. In the enterprise sector, established solutions such as Informatica PowerCenter or Microsoft SQL Server Integration Services (SSIS) are often used, which enable stable and scalable data integration. In addition, many cloud providers offer their own ETL services that can be seamlessly integrated into existing cloud ecosystems. The choice of the right tool depends on the requirements, the data landscape and the available resources.

Alternative: ETL process for cloud data warehouse

With the triumph of cloud data warehouses such as Snowflake, BigQuery or Redshift, an alternative approach has become established: the ELT process (Extract, Load, Transform). In contrast to the classic ETL approach, the extracted data is first loaded raw into the data warehouse and only then transformed within the DWH system. This enables greater scalability, as the computing power for the transformation can be flexibly adapted. In addition, there is no need to operate separate ETL servers, which simplifies the system architecture. ELT is particularly suitable for modern cloud environments and large volumes of data where performance and flexibility are paramount.

Data Warehouse Architecture

Development of a scalable analysis platform

The architecture of a data warehouse defines the technical structure that describes the path of the data from origin to analysis. As a rule, this architecture follows the three-tier model, which consists of a data source, data warehouse and presentation level. This structure is supplemented by the ETL or ELT process, which handles the integration and preparation of the data. In modern architectures, cloud-based components, data lakes and streaming technologies are increasingly being added in order to process unstructured or real-time data. The aim of a well-designed architecture is to provide data efficiently, securely and scalably while maximizing the analysis capability for the specialist departments.

Data warehouse solutions

On-premise, cloud or hybrid - which solution is right for you?

Today, companies can choose from a variety of data warehouse solutions depending on their requirements, budget and infrastructure. Traditional on-premise solutions such as Microsoft SQL Server or Oracle Data Warehouse offer full control, but require their own hardware and IT resources. Cloud solutions such as Snowflake, Amazon Redshift, Google BigQuery or Azure Synapse are characterized by high scalability, flexibility and usage-based cost models. These solutions are often the first choice for companies that already work in the cloud or want to modernize their infrastructure. Alternatively, there are hybrid approaches in which parts of the data storage and analysis take place both locally and in the cloud.

Choosing the right data model has a significant influence on the performance and flexibility of analyses in the data warehouse.

Jochen Maier, CEO summ-it

Data Warehouse Software

Tools for implementation and operation

In addition to the architecture and infrastructure decisions, the choice of the right data warehouse software also plays a central role. This includes not only the core platform, but also accompanying tools for data integration, transformation, monitoring and analysis. In the open source sector, tools such as Pentaho or Apache Hive are very popular, while commercial providers such as SAP BW, IBM Db2 Warehouse or Teradata offer comprehensive enterprise solutions. In the cloud, specialized platforms such as Snowflake or BigQuery dominate with their high performance and easy scalability. When selecting software, it is important that it fits into the existing IT landscape and supports future requirements such as data volumes, real-time analysis or data governance.

Advantages and challenges of a data warehouse

Opportunities and stumbling blocks on the way to becoming a data-driven organization

A data warehouse offers companies numerous advantages, but also brings with it a number of challenges that should be taken into account during planning and implementation. The central storage and processing of data creates new opportunities for analyses and strategic decisions. At the same time, setting up a powerful data warehouse requires a high level of technical expertise, budget and process discipline. In this section, we highlight the key benefits and challenges you should be aware of before investing in a data warehouse initiative. This will help you to better assess whether a data warehouse is the right path for your company.

Advantages of a data warehouse

Why investing in a data warehouse is worthwhile

A data warehouse brings your company numerous advantages and becomes a strategic success factor in a data-driven world. The most important advantages can be divided into three core aspects:

Central data storage

A key advantage of a data warehouse is the central consolidation of all relevant company data. Instead of scattered information being stored in different departments, systems or files, the data warehouse offers a single, reliable source. This so-called single source of truth not only facilitates access to information, but also reduces inconsistencies and contradictory evaluations. As a result, all departments can access the same, verified database and make fact-based decisions. Central data storage saves time in the long term and increases transparency within the company.

Historicization

Another advantage of a data warehouse is the ability to historicize the data. In contrast to operational systems, which often only display the current data status, a data warehouse also stores past statuses. This allows you to track developments over longer periods of time, identify trends and carry out historical analyses. This traceability is particularly important for budget planning, market analyses or monitoring business processes. Historization also enables legally compliant data storage, for example in the context of compliance requirements.

Data Warehouse Performance

Data warehouses are specially optimized to analyse large volumes of data quickly and efficiently. The use of indexing, caching and optimized data models (such as Star or Snowflake schema) significantly increases performance. Even complex queries and evaluations can be carried out in a short time without overloading the operational systems. This enables high availability of reports and analyses for management and specialist departments. The increased performance makes a significant contribution to being able to make faster and more informed decisions.

Challenges when using a data warehouse

What you should consider when introducing a data warehouse

Despite the numerous advantages, setting up and operating a data warehouse also poses a number of challenges that should not be underestimated. These relate to both technical and organizational aspects and require careful planning.

Data quality

An often underestimated problem when introducing a data warehouse is ensuring data quality. Central storage is of little use if the underlying data is incorrect, incomplete or inconsistent. Poor data quality inevitably leads to incorrect analyses and can jeopardize the acceptance of the data warehouse in the company. It is therefore crucial to carry out thorough data cleansing and validation as early as the ETL process. Clear responsibilities for data maintenance and quality assurance should also be defined.

Complexity & costs

Setting up a powerful data warehouse is technically demanding and cost-intensive. The integration of different data sources, data modelling and the implementation of ETL processes require specialized expertise. In addition, there are ongoing costs for licenses, hardware or cloud resources as well as for the operation and maintenance of the system. Coordination between specialist departments and IT can also be time-consuming. A careful cost-benefit analysis is therefore essential to ensure the long-term added value of the project.

Governance & Security

The central storage of large amounts of sensitive company data also increases the importance of governance and data security. Clear rules must be defined as to who can access which data on and how access is controlled. Legal requirements such as the GDPR or industry-specific compliance requirements must also be taken into account. Protection against unauthorized access, data loss or manipulation must be guaranteed at all times. A structured data governance strategy is therefore essential to ensure security and trust in the data warehouse solution.

Modern data warehouse solutions

Flexible, scalable and future-proof

The selection of data warehouse solutions has grown significantly in recent years. Today, companies not only have classic on-premise systems at their disposal, but also powerful cloud and hybrid solutions. The decision for a suitable solution depends on various factors, such as budget, scalability, security requirements and existing IT infrastructure. Modern cloud solutions make it possible to process data in real time, scale flexibly and adapt operating costs to actual requirements. At the same time, they offer a solid foundation for professional use thanks to integrated security and governance mechanisms.

On-Premise vs. Cloud

When choosing between on-premise and cloud solutions, companies should carefully weigh up their individual requirements and framework conditions. On-premise data warehouses offer maximum control over infrastructure, security and data storage, but entail high investment and operating costs. In contrast, cloud data warehouses enable a high degree of flexibility and scalability with lower entry costs, as no hardware is required. Cloud solutions are particularly attractive for growing companies or those with highly fluctuating data volumes. In some cases, a hybrid model can also make sense, in which sensitive data is stored locally and analytical workloads run in the cloud.

Well-known data warehouse providers

Several providers with different strengths have established themselves in the field of modern data warehouse solutions. Snowflake is one of the best-known cloud platforms and impresses with its simple scalability and the separation of storage and computing power. Google BigQuery scores with high performance for SQL-based analyses and a serverless approach that eliminates the need for infrastructure management. As part of the AWS cloud, Amazon Redshift offers seamless integration with other AWS services and is suitable for extensive analytical workloads. Microsoft Azure Synapse Analytics combines data warehousing and big data analytics in a unified platform and is aimed in particular at companies with a Microsoft technology stack. All solutions offer high availability, security and performance – the choice depends on individual requirements and preferences.

Data warehouse best practices & success factors

What matters in practice

A data warehouse can only achieve its full potential if certain best practices are observed during implementation and operation. Technical excellence alone is not enough – organizational and process-related factors also play a decisive role. The following success factors have proven themselves in practice and will help to ensure the long-term success of your data warehouse project.

Clean data modeling

Well thought-out and clean data modeling is the foundation of a powerful data warehouse. The selected data model should take into account both the current analysis requirements and possible future requirements. Proven models such as the star schema or snowflake schema are often used to ensure a clear structure and good performance. Errors in modeling often lead to performance problems, redundant data or confusing structures. You should therefore invest sufficient time in planning and coordination with the specialist departments.

Governance strategies

Without clear data governance strategies, the operation of a data warehouse can quickly become confusing. Governance includes, among other things, the definition of data responsibilities, access rights and quality standards. The aim is to ensure transparency and traceability across the entire data lifecycle. Legal aspects, such as compliance with data protection guidelines (e.g. GDPR), also play a key role here. An active governance culture creates trust and acceptance among users.

Data warehouse performance tuning

Performance tuning is a decisive success factor, especially with large amounts of data. Query times can be significantly reduced through the targeted use of indices, partitioning and materialized views. Equally important is the ongoing analysis of usage patterns in order to identify bottlenecks at an early stage and make optimizations. In cloud environments, you should also make conscious use of scalability in order to flexibly shift compute-intensive processes to more powerful resources. A high-performance data warehouse increases acceptance within the company and promotes the use of data-based decision-making.

User Enablement & Self-Service BI

A data warehouse only unfolds its full benefits when it is actively used by the specialist departments. User enablement means enabling users to carry out analyses independently through training, documentation and user-friendly BI tools. The trend is towards self-service BI, where users can create individual reports and dashboards without IT support. For this to work, data models, metadata and access concepts must be designed to be comprehensible and transparent. A well-implemented self-service offering promotes a data-driven corporate culture and increases efficiency.

Data Warehouse – Conclusion & Recommendations

When it is worth using and what you should look out for

For many companies, a data warehouse is an indispensable tool for gaining valuable insights from their data. The introduction of a data warehouse is particularly useful for organizations with a large number of data sources and a high need for analysis. The investment is worthwhile if you want to bundle your data centrally, ensure quality and consistency and create a solid basis for data-based decisions.

Before you start implementation, however, you should keep an eye on the most important success factors. These include proper data modeling, clear governance structures and an efficient and well-administered system. It is equally important to involve future users at an early stage and provide them with suitable tools. In this way, you create the conditions for a successful data warehouse strategy that delivers real added value for your company in the long term.

Get in contact now