Data warehouses store and process large amounts of data from various sources within a business. An integral component of business intelligence (BI), data warehouses help businesses make better, more informed decisions by applying data analytics to large volumes of information.
In this article, you’ll learn more about what data warehouses are, their benefits, and how they’re used in the real world. You’ll also learn how data warehouses differ from other similar concepts, explore common warehousing tools, and find relevant courses that can help you start exploring a career in data today.
Note: See bottom of article for complete acronym glossary.
A data warehouse, or “enterprise data warehouse” (EDW), is a central repository system in which businesses store valuable information, such as customer and sales data, for analytics and reporting purposes.
Used to develop insights and guide decision-making via business intelligence (BI), data warehouses often contain a combination of both current and historical data that has been extracted, transformed, and loaded (ETL) from several sources, including internal and external databases. Typically, a data warehouse acts as a business’s single source of truth (SSOT) by centralizing data within a non-volatile and standardized system accessible to relevant employees. Designed to facilitate online analytical processing (OLAP), and used for quick and efficient multidimensional data analysis, data warehouses contain large stores of summarized data that can sometimes be many petabytes large .
Data warehouses provide many benefits to businesses. Some of the most common benefits include:
Provide a stable, centralized repository for large amounts of historical data
Improve business processes and decision-making with actionable insights
Increase a business’s overall return on investment (ROI)
Improve data quality
Enhance BI performance and capabilities by drawing on multiple sources
Provide access to historical data business-wide
Use AI and machine learning to improve business analytics
As data becomes more integral to the services that power our world, so too do warehouses capable of housing and analyzing large volumes of data. Whether you’ve realized it or not, you likely use many of these services every day.
Here are some of the most common real-world examples of data warehouses being used today:
In recent decades, the health care industry has increasingly turned to data analytics to improve patient care, efficiently manage operations, and reach business goals. As a result, data scientists, data analysts, and health informatics professionals rely on data warehouses to store and process large amounts of relevant health care data .
Open up a banking statement and you’ll likely see a long list of transactions: ATM withdrawals, purchases, bill payments, and on and on. While the list of transactions might be long for a single individual, they’re much longer for the many millions of customers who rely on banking services every day. Rather than simply sitting on this wealth of data, banks use data warehouses to store and analyze this data to develop actionable insights and improve their service offerings.
Retailers – whether online or in-person – are always concerned about how much product they’re buying, selling, and stocking. Today, data warehouses allow retailers to store large amounts of transactional and customer information to help them improve their decision-making when purchasing inventory and marketing products to their target market.
There are many terms that sound alike in the world of data analytics, such as data warehouse, data lake, and database. But, despite their similarities, each of these terms refers to meaningfully different concepts.
A database is any collection of data stored electronically in tables. In business, databases are often used for online transaction processing (OLTP), which captures and records detailed information in real-time, such as sales transactions, and then stores them for later reference.
A data warehouse, meanwhile, is a centralized repository and information system that is used to develop insights and guide decision-making through business intelligence. A data warehouse stores summarized data from multiple sources, such as databases, and employs online analytical processing (OLAP) to analyze data.
A data lake, finally, is a large repository designed to capture and store structured, semi-structured, and unstructured raw data. This data can be used for machine learning or AI in its raw state and data analytics, advanced analytics, or databases and data warehouses after being processed.
Whether you’re looking to start a career in business intelligence or data analytics more generally, you should have a strong grasp of key data warehouse concepts and terms. Here are some of the most common to know:
The exact architecture of a data warehouse will vary from one to another. Data warehouses can be one-, two-, or three-tier structures. Perhaps the most common, however, is the three-tier architectural structure, which looks as follows:
Bottom tier: also called the data tier, in which the data is supplied to the warehouse.
Middle tier: also called the application tier, in which an OLAP server processes the data.
Top tier: also called the presentation tier, which is designed for end-users with particular tools and application programming interfaces (APIs) used for data extraction and analysis.
Traditionally, data warehouses were housed in servers within a business’s physical location. Today, though, more and more data warehouses use cloud storage to house and analyze large volumes of data. Some of the most common cloud data warehouse software, include:
Microsoft Azure data warehouses, particularly Azure Synapse Analytics and Azure SQL database
AWS’ data warehouse Amazon Redshift
Google cloud’s data warehouse Google Big Query
Snowflake data warehouse
Data warehouses are powerful tools used by businesses every day. Start your own journey toward working with data warehouses today by taking a flexible online course like IBM Data Warehouse Engineer Professional Certificate, which can help you develop job-ready skills for an entry-level role in data warehousing.
IBM’s BI Foundations with SQL, ETL and Data Warehousing Specialization, meanwhile, prepares course takers for BI Analytics success by developing hands-on skills for building data pipelines, warehouses, reports, and dashboards.
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Relational Database (RDBMS), Business Intelligence (BI), Enterprise Data Warehouse (EDW), SQL, Extract Transform Load (ETL), Data Science, Database (DBMS), NoSQL, Database (DB) Design, Database Architecture, Postgresql, MySQL, Relational Database Management System (RDBMS), Cloud Databases, Python Programming, Jupyter notebooks, Shell Script, Bash (Unix Shell), Linux, Database Servers, Relational Database, Database Security, database administration, Extraction, Transformation And Loading (ETL), Apache Kafka, Apache Airflow, Data Pipelines, Data Warehousing, Cube and Rollup, Star and Snowflake Schema, cognos analytics
AI: Artificial Intelligence
ATM: Automated Teller Machine
BI: Business Intelligence
EDW: Enterprise Data Warehouse
ETL: Extracted, Transformed, and Loaded
OLAP: Online Analytical Processing
OLTP: Online Transaction Processing
ROI: Return on Investment
SQL: Structured Query Language
SSOT: Single Source of Truth
IBM. “OLAP, https://www.ibm.com/cloud/learn/olap.” Accessed November 17, 2022.
Journal of Medical Engineering & Technology. "Transforming Healthcare with Big Data Analytics: Technologies, Techniques and Prospects, https://pubmed.ncbi.nlm.nih.gov/35852400/." Accessed November 17, 2022.
This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.