7T - SevenTablets, Inc.

Data Warehouse vs. Data Lake: What They Are and How They’re Used in Tandem

Read Time: 3 minutes

There is a lot of confusion surrounding data warehouses vs. data lakes and some use the terms interchangeably. But if you’re ready to perform a data governance overhaul for your business or organization, then it’s important that you understand the difference between these two platforms and how they can be used in tandem. 

What is a Data Lake? – The Definition

A data lake is a large data storage portal for raw and unstructured data. This data has not been processed, filtered or otherwise modified from its original state. 

It’s common for a company to configure many of its systems to feed data into a data lake. For example, a retail company may populate its data lake with information from its vendors and suppliers, its online sales portal and its point of sale system in brick and mortar stores.

The only process that’s performed with the data involves indexing. Data is indexed and tagged as it enters the data lake, making it possible for users to query and pull data sets for a report or analysis. Notably, data lakes can store raw data regardless of its format. 

What is a Data Warehouse? – The Definition

A data lake may feed into a data warehouse, which is used to store pre-filtered and structured data. So let’s say you query the data lake to pull and organize online sales data from the past six months. You also use a data visualization tool to generate charts. This new data set would then be stored in your data warehouse, since it has been filtered, structured and manipulated. 

Some may find it helpful to visualize an actual lake and warehouse. You have a lake that’s filled with data instead of water. On the lakeshore, you have a large factory and warehouse. Massive pipes funnel raw data from the lake into the factory, where it’s organized, filtered and structured. The now-manipulated data is then moved into the warehouse, where it’s stored for future use. 

How Are Data Lakes and Data Warehouses Used Together? 

It’s good practice to store data in its raw form, as this will ensure its integrity in the future. Multiple divisions of a single company may need to utilize a piece of raw data, so it’s useful to have the raw, unprocessed data stored in a data lake for posterity. Otherwise, you may start out with already-modified or pre-filtered data that will skew your analysis. 

While your raw data is housed in the data lake, your data warehouse is home to filtered, structured data. It’s common for a developer to write a piece of software that integrates these two platforms by automatically pulling from the data lake, applying filters and then saving the data set in the warehouse. 

A data warehouse can also be useful in limiting liability when dealing with sensitive data. Let’s say your company collects sensitive personal information from hospital patients. All of this data is stored in the data lake, but granting access to this information creates some serious liability issues. There is a solution, though. It’s possible to query the data lake so you pull just a portion of the data — like the patient’s first and last initials, date of birth and their physician’s name. So you pull the data that’s required, while omitting sensitive information that increases liability — such as full name and social security number. 

A data warehouse can be an extremely helpful time-saver in scenarios where multiple users are seeking the same data set. A pre-filtered set of data can be stored for use whenever the need arises, without the need to repeatedly query the data lake. In this way, the warehouse is effective in limiting access to the data lake, where large volumes of potentially-sensitive data are stored. Setting different user roles and permissions within the data lake can also limit access to sensitive data.

Additional platforms — such as predictive analytics engines and data visualization tools — can be connected to the data lake, data warehouse, or both. In fact, this is one of our specialties at 7T. Our product, Sertics, provides users with the services and tools they need to make the most of their data, whether it’s data lake creation, data governance, or supporting data visualization and predictive analytics. 

Managing massive volumes of data is no small feat, especially when you’re tasked with merging stand-alone platforms in a way that’s secure and user-friendly. But that’s where 7T can help. Our data governance experts, mobile app and software developers and system integration specialists will build a robust architecture that’s certain to boost your bottom line. 

Based in Dallas, 7T also has regional offices located in Chicago and Houston. We work with clients worldwide. If you’re in search of an innovative development partner to help guide your data governance strategy, contact the team at 7T today.

Reach out to our team today!

Harshit Gupta

Harshit Gupta

Harshit Gupta has more than eight years of experience as a Senior Developer. He holds a bachelor’s degree in Computer Software Engineering and an MS degree in Computer Science from Arkansas State University. Some of his most notable accomplishments include his dissertation on 3-Tier MapReduce, being named runner-up in 7T's Annual Hack-a-thon, and receiving the “think/WOW” award for his work on Lockton-Dunning Benefits.


Generic selectors
Exact matches only
Search in title
Search in content
Search in posts
Search in pages