A data warehouse is a data storage system designed to ingest and analyze data in real-time typically to uncover business intelligence used in day-to-day operations. These systems draw relevant data from many other operational systems, like enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, billing systems, and supply chain systems, and apply sophisticated visualization, reporting, and business analytics to help inform companies easily.
While many data warehouses do archive data for posterity, this role more frequently falls to data lakes because data lakes accommodate extensive data storage for multiple non-compatible data types. Data warehouse storage, however, is still a highly relevant enterprise storage solution, especially for sensitive customer information, proprietary business information, and storing refined data.
Data warehouses play a downstream role in enterprise data pipelines. Either the warehouse will dip into a data lake, drawing out relevant data from the pool of structured, unstructured, and semi-structured data, or it collects data from multiple data sources and operational systems.
Then, upon ingestion, the data warehouse will clean, transform, or otherwise prepare the data to fit into a data schema that it can store and use—unlike data lakes, data warehouses operate on hierarchical systems instead of flat. From the warehouse, or as data is moved inside, analytics and further transformations are applied to it, sending the refined insights to reporting, or on to another system like a data mart, to serve smaller specialized sets of users.
Data warehouses are complex, automated processes, but data warehouse key concepts are:
Data warehouses are designed around a three-tier approach. As technology continues to advance, the three-stage model may be implemented differently but the standard data warehouse architecture currently supports the collection, ingestion, transformation, and analysis of unprocessed or raw data into data usable for other systems, or immediately actionable by the company.
Traditional data warehouses have increasingly moved to cloud supported architectures. The data warehouse value proposition combined with the scalable technologies in the cloud have created storage opportunities for enterprises to double down on the data efforts. Several of the cloud enabling advantages include:
Data warehouses, unlike data lakes, are considered scheme-write systems, meaning that when data is stored in a data warehouse, it is fitted into a predefined data scheme which helps in cataloging and organizing. This process alludes to the fact that data warehouses are designed to carefully prepare data before storage so that analysis can quickly follow.
Though data warehouses cannot store the same volume as data lakes, to try would be exceptionally cost-prohibitive, they are helpful in processing immediate, critical data metrics helpful to real-time business operations. Oftentimes, enterprises use data lakes as a base in their data stack, connecting it to data warehouses, or other AI and machine learning analytics through their data pipeline.
Data lakes are broader data repository systems with data ingestion as a primary concern over data analysis. Though analytics are developing around data lakes, data lakes are highly inclusive, accepting all data types, supporting all users, and easy to adapt. Because of these characteristics, data lakes potentially hold the deepest business insights. The challenge in drawing out those insights is defined by the very data lake characteristics that enable deep insights, so much data and the breadth of diversity requires time to process and analyze.
In contrast, data warehouses standardize data formats at ingestion so that insights can be quickly delivered about domain specific channels in a timely manner, such as marketing insights, or account billings. Conceptually, data warehouses represent an increase in data refinement at the sacrifice of data scope over data lakes.
Data warehouses can also be classified into three main types based on their use.
Enterprises deploy data warehouses as a storage solution to control and understand their data assets, and thereby make faster quality business decisions. For many reluctant businesses, basic data warehousing is a necessary cost to remain viable, but leading companies are leveraging them and integrating technologies to improve their competitive advantages.