What Is the Difference Between a Data Lake and a Data Warehouse?

Data lakes and data warehouses are both data storage solutions, but they’re not the same thing. Each has its own purpose, and the type of data they store differs. Sometimes the two can even be combined in a data architecture known as a data lakehouse.

Though these two storage solutions are easily confused, it’s important to make a distinction between the two, so you know which is right for your organization. Some organizations may need only one of these solutions, while some may need both. Let’s dive deeper into the definitions for these two forms of storage and their differences.

What Is a Data Lake?

A data lake is a type of storage repository. Data lakes can store a large amount of data and the data can be structured, unstructured, or semi-structured. A data lake doesn’t need to make distinctions among the data it stores. It essentially provides a large space for all types of data in their native format to be held and queried when necessary. There are no limits on the size of the data.

A data lake can quite literally be visualized as a lake. Imagine a data architecture that has multiple sources of data flowing in and out, just as the different water sources that feed into a lake. A data lake is also very easy to scale. Data lakes are best for companies that have a lot of data to store but they don’t need to immediately process or analyze that data.

What Is a Data Warehouse?

A data warehouse can collect and manage your data, while also providing the structure for analysis and information about the data. Data warehouses are more structured, can transform data, and can process advanced queries that a data lake can’t. Typically, if a company has a data lake, they will likely have a data warehouse as well.

Data Lake vs Data Warehouse: What’s the Difference?

Now that we have a general definition of data lakes and data warehouses, let’s take a look at some of the biggest differences between the two:

Data Structure

The way the data is structured in data lakes and data warehouses marks one of the most significant differences between the two:

• Data Lake Structure - Data lakes have a raw data structure. They typically store raw and unprocessed data in extremely large volumes. Raw data in large volumes is especially useful for machine learning but requires data governance and quality to ensure the data is useful.
• Data Warehouse Structure - Data warehouses store processed data. Data warehouses don’t usually need as much space, since it only keeps useful data. Processed data is also easier to analyze by users.

Users

Data lakes and data warehouses can differ based on the users that are intended to access and make use of the data.

• Data lake users - Unless you can easily understand unprocessed data, data lakes won’t be particularly useful. Data lake users are primarily data scientists who know how to parse and analyze this type of data. However, there are tools that can help companies make the most of data lakes by providing them with self-service analytics.
• Data warehouse users - Most employees in an organization can understand processed data from a data warehouse.

Purpose

Both storage solutions have their own particular purposes and uses.

• Data lake purpose - Data lakes aren’t incredibly organized. They’re usually used as mass storage for big data, so you can have data available just in case you may need it later. The purpose of data lake data isn’t always known.
• Data warehouse purpose - Data warehouses usually have a determined purpose to serve a specific need or department of an organization. In other words, the purpose is known.

Ease of Access

Data lakes and data warehouses may have different access measures in place.

• Data lake access - Data lakes are typically easy for anyone in an organization to access, making the data easy to change or manipulate.
• Data warehouse access - Data warehouses, with their specific purpose, have a particular structure to the data that is stored. This makes it more difficult to change and manipulate the data.

These are just a few of the primary differences between these two data architectures. The next step is choosing which structure is right for your organization.

Choosing the Right Solution for Your Needs

Typically, organizations utilize both data lakes and data warehouses. Data lakes help you take in all of your big data that can be accessed for machine learning or future purposes. Data warehouses can provide employees and other members of the organization with the analytics and advanced queries they need for business decisions.

With that being said, not every organization will require both. In general, data lakes are more flexible. You can take in all data formats, store more data, provide access to all users, easily scale, and get quick access to data. The in-depth analytics of data lakes may typically be only easily analyzed by data scientists, but the rise of self-service data tools can make data lakes useful to all employees. That’s where Narrator comes in.

Enhanced Data Management with Narrator

If you want a truly powerful data platform that can handle any of your data management requirements, Narrator can help. Narrator is a data platform that empowers your team with self-service analytics that can pull from all of your data sources. Simply put, you can make the most of your stored data.

With self-service analytics, your employees can query with the simple click of a button and get full analytics, without having to rely on your data team. In the meantime, your data team can focus on strategic and impactful work, instead of constantly responding to data requests. They’ll also get finer control of your data architectures and a more effective way to maintain data quality.

If you want to enhance your data management, book your demo with Narrator today.