What’s the Difference Between a Data Lake and Data Warehouse

Posted by Trevor Warren, Data Architect, - Jul 15, 2020

Do you know the difference between a data lake and data warehouse? Don’t worry if you don’t; you’re not alone. Many people believe these terms are interchangeable. They aren’t. They’re distinctly different concepts.

They both are frequently used for storing big data but that’s where their similarities end. Let’s take a look at what each of these vital tools in the data world are and how they work. This way we can better understand the difference between a data lake and a data warehouse and how they come into play when you're using big data.

What’s a Data Warehouse and How Does it Work?

Data warehouses are the traditional, established and proven storage method for structured, processed data. They’re known for reliable performance. A data warehouse operating in the cloud provides unprecedented scalability in terms of data storage capability and analytics power.

A data warehouse stores data in two ways:

  • Fast storage for frequently accessed data
  • Cost-efficient storage for infrequently accessed data

The combination of processed data in multiple databases housed in an SQL environment is perfect for running query engines to get relevant, fast results. The data warehouse optimizes data query speeds by moving frequently accessed information to fast storage.

The data inside the databases is organized into a table/column format that can be customized with various descriptors. Each table can be organized into schemas. Query tools use these schemas in SQL to select which data tables to analyze for the most relevant results and provide informed decision making.

There are some drawbacks the way data warehouses store data. First, the massive storage capacity also means a large database server, which can put a dent into your budget.

analyzing dataData warehouses in the cloud can handle big data with ease, allowing you to run analytics and business intelligence on terabytes of data without issue. They have been the tried-and-true technology for business intelligence and analytics, and now with the cloud are able to scale with the organization’s ever-increasing data needs.

With machine learning and data science developing into a mainstream business tool for making use of both structured and unstructured, raw and curated data, we needed a platform different from the data warehouse. Enter the data lake.

How is a Data Lake Different?

When you think about a data lake, remember these two words, “all data.” That’s what you’ll find in a data lake. The good. The bad. The ugly. Data lakes aggregate all data from various sources into a single repository in its original format.

This includes raw, unstructured data, data that might have some structure and structured data. If you think about it, a real lake has all sorts of things in it, too. It’s a great analogy.

Data lakes are a newer concept than the tried-and-true data warehouses. They address many big data challenges work on a wide, “store everything” approach, handling heavy volumes of various kinds of data. This relation and non-relational data can come from such sources as:

  • IoT devices
  • Websites
  • Mobile apps
  • Social media
  • Corporate and line-of-business applications

What are the Pros and Cons of a Data Lake?

programmer with dataOne of the benefits of the data lake is that your organization can store all of their data there, and with proper metadata management can have data that is usable for machine learning and other important purposes. You can also scale any amount of data in your lake without structuring it and can hang on to it for as long as you need it.   

When you do need it, you can run various kinds of analytics from it, including big data processing, machine learning and even dashboards or visualizations, without moving it to an analytics platform. Data is easily accessible and can be used with a variety of analytic tools, including open-source frameworks, such as Hadoop, Presto, Apache and Apache Spark. 

Data lakes, like data warehouses, are very cloud-friendly and can be spun up with just a little effort towards data management and access control. With some additional effort, you can catalog the data in the data lake so it can be used in more advanced applications like machine learning.

Data lakes, like data warehouses, are very cloud-friendly and can be spun up with just a little effort towards data management and access control. @OnixNetworking 

One of the biggest drawbacks of the data lake is that without proper data pipeline management, as well as without proper cataloging, you can easily end up with a data swamp that is difficult to use for any real value. While It’s easy to put data in the lake, it’s tougher to sift through all of that information to find what exactly you need once it’s in there.       

A data process framework for your business needs is essential to utilizing the data lake and preventing the creation of a “data swamp.” Having well-defined data catalogs for your data lake will help data scientists and stakeholders utilize the data for business benefit.

Lastly, data lakes generally require more storage space than data warehouses because they will be used to store all of your organization’s data, including unstructured data like images and videos.

So How Do I Know Which One I Should Use?

The answer to the question of whether you should use a data lake or a data warehouse comes down to asking yourself how you want to use your data. 

female-hand-holding-cloudWhat type of analysis do you want to do? As we discussed, each of these options has pros and cons and focuses on different types of analysis because each has different purposes. 

In this era of a growing focus on machine learning, the raw data you find in a data lake is perfect for those uses. This is perfect for data scientists. 

On the other hand, the clean, structured approach to data stored in a data warehouse is the perfect solution for business professionals who want analytics but don’t have the know-how to sift through raw data to find the insights they need. The data and query system is more normalized through the use of such tools batch reporting, business intelligence and visualizations

One note: If you’re already using a data warehouse and want to jump into the data lake, either by moving to one or adding one to your existing environment (note: it’s possible to use both), you’ll need to undergo a transition period because the data stored in them isn’t the same.

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Trevor Warren, Data Architect

Trevor Warren, Data Architect

Trevor has nearly a decade of experience in solving problems for complex computer systems and improving processes. Trevor earned a Master of Science in Data Science. He is also a Google Cloud Certified Professional - Cloud Architect and Data Engineer.

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