What's the Difference Between a Data Lake, Data Warehouse and Database?

Data Lake Definition

If you want full, in-depth information, you can read our article called, “What’s a Data Lake?” But here we can tell you, “A data lake is a place to store your structured and unstructured data, as well as a method for organizing large volumes of highly diverse data from diverse sources.”

The data lake tends to ingest data very quickly and prepare it later, on the fly, as people access it.

Data Warehouse Definition

A data warehouse collects data from various sources, whether internal or external, and optimizes the data for retrieval for business purposes. The data is primarily structured, often from relational databases, but it can be unstructured too.

Primarily, the data warehouse is designed to gather business insights and allows businesses to integrate their data, manage it, and analyze it at many levels.

Database Definition

Essentially, a database is an organized collection of data. Databases are classified by the way they store this data. Early databases were flat and limited to simple rows and columns. Today, the popular databases are:

Relational databases, which store their data in tables
Object-oriented databases, which store their data in object classes and sub classes

The Differences Between Data Lakes, Data Warehouses, and Databases

Data lakes, data warehouses and databases are all designed to store data. So why are there different ways to store data, and what’s significant about them? In this section, we’ll cover the significant differences, with each definition building on the last.

The Database

Databases came about first, rising in the 1950s with the relational database becoming popular in the 1980s.

Databases are really set up to monitor and update real-time structured data, and they usually have only the most recent data available.

The Data Warehouse

But the data warehouse is a model to support the flow of data from operational systems to decision systems. What this means, essentially, is that businesses were finding that their data was coming in from multiple places—and they needed a different place to analyze it all. Hence the growth of the data warehouse.

For example, let’s say you have a rewards card with a grocery chain. The database might hold your most recent purchases, with a goal to analyze current shopper trends. The data warehouse might hold a record of all of the items you’ve ever bought and it would be optimized so that data scientists could more easily analyze all of that data

The Data Lake

Now let’s throw the data lake into the mix. And because it’s the newest, we’ll talk about this one more in depth. The data lake really started to rise around the 2000s, as a way to store unstructured data in a more cost-effective way. The key phrase here is cost effective.

Although databases and data warehouses can handle unstructured data, they don’t do so in the most efficient manner. With so much data out there, it can get expensive to store all of your data in a database or a data warehouse.

In addition, there’s the time-and-effort constraint. Data that goes into databases and data warehouses needs to be cleansed and prepared before it gets stored. And with today’s unstructured data, that can be a long and arduous process when you’re not even completely sure that the data is going to be used.
That’s why data lakes have risen to the forefront. The data lake is primarily designed to handle unstructured data in the most cost-effective manner possible. As a reminder, unstructured data can be anything from text to social media data to machine data such as log files and sensor data from IoT devices.

What’s the Future of Data Lakes, Data Warehouses, and Databases?

Will one of these technologies rise to overtake the others? No, we don’t think so.

Here’s what we see. As the value and amount of unstructured data rises, the data lake will become increasingly popular. But there will always be an essential place for databases and data warehouses.

You’ll probably continue to keep your structured data in the database or data warehouse. But increasingly, companies are moving their unstructured data to data lakes on the cloud, where it’s more cost effective to store it and easy to move it when it’s needed. This workload that involves the database, data warehouse, and data lake in different ways is one that works, and works well. We'll continue to see more of this for the foreseeable future.

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