Data warehouses aren’t a new concept. These tried-and-true central information repositories have proven to be indispensable to organizations as they navigate the complexities of a competitive business environment.
Understanding a data warehouse and what it can offer in terms of data storage for the sake of business intelligence is a significant part of developing a data strategy. These repositories not only house data but also help power the analytics, reporting and insights that arise from that data. A data warehouse is built specifically to support analytics. Let’s take a closer look at how all of this works.
What is a Data Warehouse?
In a previous blog, we discussed the difference between a data lake and a data warehouse. Both of these repositories store data but do it in a different way.
While a data lake holds large amounts of different data types, including unstructured, semi-structured and information. It is not already formatted for easy retrieval and analysis and is stored in its native formats.
A data warehouse is more purposeful. The data it stores is structured. It comes from relational databases, transactional systems and other sources, such as line-of-business applications. All of this warehoused data is already in a tabular format that can be easily retrieved and analyzed through business intelligence tools directly from the warehouse.
Notable benefits of a data warehouse include:
- Optimized data storage
- A single-source of data access for an entire organization
- Guaranteed data quality, consistency and accuracy
- A history of all stored data
- Fast query results
- The ability to read large amounts of structured data to produce actionable insights
- Separation of transactional data from analytical data
How Does a Data Warehouse Work?
Data warehouses are built in tiers. These tiers include the front-end analytics tools where users access and review the data, the analytics engine and the database server where data is loaded and stored.
There are two ways data warehouses store data — very fast storage for frequently accessed data and cheap object store for data accessed less often. The warehouse automatically moves data to the correct storage type to optimize query speeds.