A Comprehensive Guide To The Data Warehouse

what is data warehouse
what is data warehouse

As the variety and volume of data continue to grow, businesses are always looking for a solution for data warehousing that can grow and adapt. Those looking for growth in revenue and profitability know that data is critical if they want to gain a competitive advantage. Digitally transforming business operations to capture and analyze a greater variety and volume of data can inform better business insights.

Valuable data comes from an increasing range of different sources and it can be challenging to integrate it due to its diverse characteristics. However, it is worth the effort due to the visibility to offers into business processes. A data warehouse provides an infrastructure for storing and accessing a vast amount of data in a user-friendly and efficient way.

What is a Data Warehouse?

Businesses today are struggling to find appropriate data stores. A DW can be very simple or it can accommodate a complex workload and is designed to support complex queries on vast sets of data.

In its most complex form, it contains data from many systems using different technologies, standards and different methods of extraction. The DW remodels the data and stores it in a consolidated format. A predefined structure and the architecture means that consistent data is available for the whole organization to use for analysis. Data loading and data retrieval are two important operations.

Many organizations use multiple DWs to support different functions and geographical areas. It makes data integration manageable and those who want to access data from a single place can do so rather than going to various operational applications.

Characteristics of a Data Warehouse

A data warehouse is usually organized around subjects, such as products, customers, sales and so on. It integrates data from a variety of sources and in various formats.


A DW is more stable and less volatile than an Operational Data Store (ODS). A DW continuously inserts records into existing tables and aggregates data across historical views.


A number of systems in an organization may be set up in isolation and it is possible to define how a DW presents information using a consistent view of the organization. This means being able to pull up reports and compare data from all departments, systems and locations in a single place.

Time variant

DW updates usually happen in scheduled batches and may change a few times a day. They may not update in real-time but they contain a large volume of data. It is the non-volatile selection of data that can help to support comprehensive, long-term decision-making. This means, in other words, that DWs are more suitable to questions about strategy based on long-term data trends, such as “which employees are meeting their targets?”


DWs are schema-on-write. This means that incoming data goes through a process to clean, harmonize and organize it according to the warehouse schema. As the data warehouse is being loaded, the ETL process (Extract, transform, load) filters out errors so as to ensure that all information being reported on is consistent and correct.

Data scope

DWs integrate new data with the existing contents to form a large repository of data. They can support analytic activities where large volumes of data are required.

Growth rate

DWs that contain both new and historical data can grow exponentially. Size can expand very quickly, which is why many of them rely on cloud infrastructure. This allows them to scale up easily according to demand.

evolution of data warehouse
evolution of data warehouse


As DWs have been around for some time, their security facilities are usually mature and consistently upgraded, enabling them to meet cyber threats in the best possible manner.

Without a DW, businesses cannot see what has changed over a set time frame or access key metric information that compares current performance with the performance last week or last year.

A DW has many different use cases. For example, it offers the ability to access data for analyzing sales results over time. It makes it possible to identify trends over time. Pricing, market segmentation strategies and the development of new products as well as communication campaigns all benefit from being able to analyze long-term data.

Evolution of the Data Warehouse

One of the major shortfalls of the traditional DW is its inflexibility. DWs are currently evolving and becoming able to support real-time reporting and decision-making to impact operational business decisions as well as strategic reporting.

A modern Operational Data Warehouse (ODW)combines the strengths of other approaches. The traditional data warehouse is a great source of integrated data but it is not that flexible. Operational data stores are fast but may not extend to more strategic enterprise needs. Data lakes can store big and varied data at less cost but they are poor at predictable performance and governance.

The modern ODW can handle modern data, which usually comes in hybrid combinations and it has substantial data integration capabilities. The best of them have very low latency and use cases include operational reporting, monitoring of business activity and real-time analytics.

A modern ODW uses the most recent advances in data platforms and tools. From in-memory execution and cloud-based databases to scalable clusters and distributed file systems, various advances have brought about improvements in speed, scale and functionality.

A final word

Data warehouses are a very useful solution for businesses to use in data consolidation and reporting. Most of them contain snapshots of the state of data at certain historic points in time. A comparison of these can reveal historical changes and trends. This is the kind of data that is not available in operational systems. However, adding a new data source tends to take a long time. This disadvantage is resulting in the evolution of the data warehouse.

The operational data warehouse or ODW does not have the shortfalls of the traditional data warehouse. It can handle a wide variety of data types at a vast scale with high speed and performance. The implementation may itself be hybrid, spanning both on-premise systems and systems in the cloud.


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