In a data lake, the data is raw and unorganized, likely unstructured. Though you’re storing their tools, your neighbors still keep them organized in their own toolboxes.īut what if your friends aren’t using toolboxes to store all their tools? They’ve just dumped them in there, unorganized, unclear even what some tools are for-this is your data lake. Some toolboxes might be yours, but you could store toolboxes of your friends or neighbors, as long as your shed is big enough. You might have lots (and lots!) of toolboxes in the shop. The tool shed, where all this is stored, is your data warehouse. This specific, accessible, organized tool storage is your database.

You store some tools-data-in a toolbox or on (fairly) organized shelves. In this, your data are the tools you can use. Lee Easton, president of data-as-a-service provider AeroVision.io, recommends a tool analogy for understanding the differences. Small and medium sized organizations likely have little to no reason to use a data lake. For a company that actually builds data warehouses, for instance, the data lake is a place to dump and temporarily store all the data until the data warehouse is up and running. Data lakes exploit the biggest limitation of data warehouses: their ability to be more flexible.Īs we’ll see below, the use cases for data lakes are generally limited to data science research and testing-so the primary users of data lakes are data scientists and engineers. Popular companies that offer data warehouses include:Ī data lake is a large storage repository that holds a huge amount of raw data in its original format until you need it. Organizations that use data warehouses often do so to guide management decisions-all those “data-driven” decisions you always hear about. Data warehouses help organizations become more efficient. Their specific, static structures dictate what data analysis you could perform.ĭata warehouses are popular with mid- and large-size businesses as a way of sharing data and content across the team- or department-siloed databases. For decades, the foundation for business intelligence and data discovery/storage rested on data warehouses. Data warehouses are large storage locations for data that you accumulate from a wide range of sources. The next step up from a database is a data warehouse.

( Learn more about the key difference in databases: SQL vs NoSQL.) What’s a data warehouse?

Creating reports for financial and other data.

Arguably, you could consider your smartphone a database on its own, thanks to all the data it stores about you.įor all organizations, the use cases for databases include:

We usually think of a database on a computer-holding data, easily accessible in a number of ways. What’s a database?Ī database is a storage location that houses structured data. Let’s start with the concepts, and we’ll use an expert analogy to draw out the differences. Define databases, warehouses, and lakes.Data lakes and data warehouses are very different, from the structure and processing all the way to who uses them and why. But for big data, companies use data warehouses and data lakes.ĭata lakes are often compared to data warehouses-but they shouldn’t be. For the lay person, data storage is usually handled in a traditional database. Data companies are in the news a lot lately, especially as companies attempt to maximize value from big data’s potential.

Database for file storage