A data warehouse is the central repository for a company’s data, bringing data from various sources into a single space for analysis and activation.
What is a data warehouse?
Historically, a data warehouse was an on-premise computer mainframe system built to extract, prepare, and store company data. But today, cloud-based data warehouses are more common. These systems bring company data from multiple sources together (usually through ELT pipelines), and eliminate the need for a clunky on-site storage solution.
Beyond centralizing company data, a cloud data warehouse creates a single source of truth from which data can be analyzed and activated. With the mass quantities of data companies have in today’s world, having a data warehouse is essential to performing the core operations that contribute to data-driven business.
5 benefits of a data warehouse
- Creating a single, reliable source of truth for company data.
- Querying complex data sets (more on that below).
- Activating your data through reverse-ETL.
- Enhanced data security, processing, and transformation.
- Improved measurement and reporting of core business metrics.
Read our full blog post to discover the Top Data Warehouses.
Data warehouse vs. database
In simple terms, a database is a set of related data organized and structured to be usable, usually from a single data source. While databases are an important part of many data operations processes, they are too simplistic for most modern business analytics to rely on for the full scale of their data operations.
A data warehouse, on the other hand, is a data storage solution that brings together much larger quantities of data from several sources, whether that’s internal databases, third-party apps and services, or customer support systems. It’s a more complex information system that stores data in different formats as well as historical data.
When to use a database, and when to use a data warehouse
A database uses Online Transaction Processing (OLTP), making them great for collecting data sets that are consistent in format and structure, like intake forms or Point of Sales systems. This makes databases functional for doing straightforward analyses using single data sets, whether small or large.
Data warehouses use Online Analytical Processing (OLAP), making them better suited for collecting varying and complex data sets, like data from intake forms, POS systems, website analytics, and more. Because of this, data warehouses are best used for complex querying and provide context, history, analysis, organization, and possibly even AI parsing.
Managing your company data with Weld
Data warehouses are at the center of the Modern Data Stack, and can easily become a central source of truth for a company’s data. But launching your data operations goes beyond setting up a data warehouse — you need to build the ELT pipelines that will populate your data warehouse, and model your data from the warehouse to make it usable for reporting, visualizations, reverse-ETL, and other activation techniques.
Weld is a complete data operations platform that brings all of your core data tasks into a single place. With dozens of pre-built ELT and reverse-ETL pipelines and a best-in-class modelling tool directly in the app, Weld is a quick, easy way to get up and running with a Modern Data Stack in a matter of minutes, rather than months or even years. To learn more about how Weld can power your data operations — and to get a managed Google BigQuery data warehouse — book a call with one of our data experts.