Understanding a Data Warehouse: What You Need to Know

Posted by Trevor Warren, Data Architect, - Dec 17, 2020

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.

cloud benefitsA 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?

managing dataData 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.

There are two ways data warehouses store data — very fast storage for frequently accessed data and cheap object store for data accessed less often. @OnixNetworking

Data warehouses also feature metadata, which contributes to their highly organized structure and quality assurance. Metadata governs the entire warehouse structure and includes naming conventions, information about data sources and refresh schedules.

Because multiple databases feed data warehouses, data in each database is organized into tables with columns that use such descriptors as strings, integers and data fields. These tables are built using schemas that are typically defined before building out the data warehouse, and usually include some sort of aggregation forming logically sound groups of data sources based on business unit or initiative. Structuring your data in this way makes it very simple to expose data to the analytics and visualization layers of the data warehouse.

Why Should I Use a Data Warehouse?

While traditional databases are great for rapid, accurate data retrieval in the moment, a data warehouse relies on the power and capacity of multiple databases to give organizations a long-range view of information over time so they can analyze trends and performance. They are ideal for data aggregation.

Because data warehouses easily support complex queries, they are ideally suited for research, reflection and analysis into business operations and intelligence across multiple databases. This speeds up the analysis process and surfaces business insights to help your teams make crucial decisions.

Want to learn more about data storage and usage in the cloud? Check out our other data blogs:

5 Important Steps in Developing a Data Strategy

What’s the Difference Between a Data Lake and Data Warehouse?

Why Use a Data Lake?

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Meet the Author

Trevor Warren, Data Architect

Trevor Warren, Data Architect

Trevor has nearly a decade of experience in solving problems for complex computer systems and improving processes. Trevor earned a Master of Science in Data Science. He is also a Google Cloud Certified Professional - Cloud Architect and Data Engineer.

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