Picture it: Keanu Reeves and Sandra Bullock play an architect and a doctor who live 2 years apart in the same property and exchange letters over time…

Wait, that’s just the 2006 movie The Lake House! That has nothing to do with data!

We’ve already spoken about data warehouses, but they’re not the only method for storing data in large volumes. You might have heard about data lakehouses, and while you too may have gotten confused with the film, we’re sorry to tell you that a data lakehouse isn’t a beautiful waterfront property filled with millions of pieces of data (that’s also free for rent on weekends).

So What is a Data Lakehouse?

In short, data warehouses are structures that are designed to store data in a tailored, categorized format, like how we store most of our personal computing data in files and folders. A data lake, on the other hand, stores all of that data in its raw format – it takes all of your data from all of the sources provided, and saves it as is, uncut and uncensored.

This means that data lakes can store data that’s been manipulated, like tables and spreadsheets, right alongside the raw input data itself. The reality, though, is that this data is critically important but it can also be messy. It’s valuable, but can be incredibly complicated to organize out of the gate before anyone even dives in to find what they need.

Why are Data Lakehouses Important?

Data lakehouses essentially combine the concepts of a data warehouse and a data lake together. They allow for the storage of all of that data in a raw format, but use tools like a warehouse to make that data easy for business intelligence, reports, data science and analytics, and machine learning.

Instead of transforming the data before sorting it, data lakehouses use a metadata layer to track files and understand what they are, such as multiple variations of a single table. These allow end users to have better access to the data for research purposes without the labor of categorizing the data in advance.

This allows you to use the raw data in a data like much like you would in a data warehouse. It makes the data easily accessible for analytics, but data lakehouses are an even better fit for those looking to pursue AI. In the financial services world for example, AI can build in the necessary privacy framework to overlay your raw data, and then transform it into something usable for analysis and research.

For your client data, you’re often dealing with significant data besides financials such as recordings of customer service phone calls, CCTV video from branches, etc. A data lakehouse is the place to store it in its raw format, but then allow a privacy framework to protect the data so that only the permissible parts are usable by AI to garner better business intelligence.