Data Warehouse

What is a data warehouse?

A data warehouse is a system that stores all important business data in one organised place. Many companies use different programs for sales, accounting, inventory, or production. Each system keeps its own data, which makes it hard to get a complete picture of how the business is doing. A data warehouse solves this problem by collecting data from all these systems and storing it together.

The main purpose of a data warehouse is to make information easy to access and analyse. Instead of searching through several spreadsheets or databases, users can find everything in one central location. The data is cleaned and structured so that reports and dashboards show accurate and consistent numbers.

The benefits of a data warehouse include:

• Better decisions: management and teams can work with reliable data instead of guessing.

• Time savings: reports can be created automatically instead of manually collecting data from many sources.

• Consistency: all departments use the same information, which avoids confusion or conflicting figures.

• Historical view: the warehouse keeps old data so trends and changes over time can be analysed.

In simple terms, a data warehouse acts as the memory of an organisation. It keeps track of everything that happens in the business and makes it easy to turn that information into insight. While operational systems like an ERP, warehouse management system or accounting application are optimised to store and process transactions, a data warehouse is optimised for analysis and reporting.

A data warehouse can be built with many different technologies, each designed to store and process large amounts of data efficiently. Some systems run in the cloud, others on company servers, but they all share the same goal: to give businesses a reliable place to analyse their data.

Common examples include Microsoft SQL Server and its cloud version Azure Synapse Analytics, Snowflake, Google BigQuery, Amazon Redshift, Databricks Lakehouse, and Oracle Autonomous Data Warehouse. Traditional enterprise options such as Teradata Vantage and IBM Db2 Warehouse are still widely used, while open-source databases like PostgreSQL are sometimes extended with analytics features for smaller projects.

Recent evolutions in data warehousing

Modern data warehouses have changed a lot in recent years. The biggest shift is the move from on-premises databases to cloud-based platforms. This makes it possible to separate storage and compute, meaning data is stored cheaply in the cloud while computing power can scale up or down when needed.

Another major trend is the rise of the lakehouse. This combines the flexibility of a data lake with the reliability of a warehouse. Data is stored in open formats, such as Delta Lake or Apache Iceberg, while still allowing structured queries and strong data consistency.

At the same time, the traditional ETL process (extract, transform, load) is being replaced by ELT (extract, load, transform). This means data is first loaded into the warehouse and then transformed there, simplifying maintenance and improving speed.

Many new systems are now serverless, which removes the need for teams to manage infrastructure. They also support both real-time and batch data in a single platform, and include built-in governance features such as security, access control, and data lineage.