The terms “data warehouse,” “data lake,” and “data mart” might sound like different terms to describe the same thing. While data warehouses, data lakes, and data marts all describe data repositories, they are different. Confusing them can lead to problems with your data integration project.
This post provides an easy guide to the differences between data warehouses, data lakes, and data marts. Read on to learn why they’re different and where they might come into play in the enterprise.
What Is a Data Warehouse?
If you were to look at all of the data a company possesses, you would notice it comes in different formats in various sources. You would also see it was inconsistent between one source and another. That is where the data warehouse comes in; it consolidates information from all of those disparate sources and generates a unified, harmonized form.
A data warehouse might sound like a glorified database, though it is more than that. Data warehouses are optimized for analyzing and processing information rather than just handling transactions. These data repositories play an important role in data integration because the purpose of data integration is to bring information from various sources together for analysis.
What Is a Data Mart?
Warehouses are divided by shelves, and those shelves are organized by what they hold. Think of a data mart as a division of a warehouse (in this case, a data warehouse).
A data mart allows you to analyze a certain amount of data. An example would be that you might create a data mart for the accounts payable department so only people from that department could view it. The people from Marketing would not need to analyze this data, as it is not relevant to their jobs.
When might you run across a data mart? You could encounter them during the system integration process, in which corporate information is brought together in one platform for ease of access and analysis. Data marts also play a role in data integration.
What Is a Data Lake?
It is helpful to think of a data lake much as you would a real lake – a body of information that is fed by multiple sources. The content has not been processed yet.
An average business user would not go “swimming” in a data lake because he or she needs information prepared for analysis. However, data scientists dip into data lakes because they have the analytical skills to make sense of the raw information. A data lake would contain data that might ultimately be integrated with other sources but is not yet ready.
Knowing the difference between these forms of data repositories is crucial. If you use the wrong one, you will not get value from your data. Worried you are not a data expert? A trusted partner can help you navigate the journey of getting the most from your information. Contact us today and get an EDI Assessment. Absolutely free.