Abby FieldsBranded Content Specialist
- April 22, 2022
- 7 minute read
- Abby is a data-driven content marketer with certifications in inbound marketing and content marketing. She’s written hundreds of articles covering digital marketing topics — a few of her specialties include first-party data, marketing and data analytics, marketing strategy optimization, and SEO. When she isn’t writing or optimizing content, Abby loves to spend time reading the latest mystery novel, having movie marathons with lots of snacks, and jamming out to Shania Twain.
Data lakes and data warehouses both help you effectively collect and store big data. But while the two terms sound similar, they have several differences. A data lake is a collection of raw, unorganized data. A data warehouse collects, stores, and filters processed data for easy analysis.
These two terms are often used interchangeably, but have many differences and uses that can help you maximize your marketing campaigns with data.
That’s why we’ll dive into the key differences between a data lake vs. data warehouse on this page. So keep reading to learn more!
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Defining a data lake vs. data warehouse
Before we dive into what makes data warehouses and data lake different, let’s define each term below:
What is a data lake?
A data lake is a system that allows you to store all of your data at any scale. Data lakes can help you collect unorganized, raw data in any size that you can analyze later.
Think of data lakes as actual bodies of water. You can store a vast amount of data in the data lake that floats around until you or another team member dive in to examine or analyze it.
What is a data warehouse?
Think of data warehouses as actual warehouses. With a data warehouse, you can collect your data in aisles or rows to keep it organized. You can then take out a stock inventory or data report to analyze specific data sets and metrics.
3 key differences between a data lake and a data warehouse
So, what’s the difference between a data lake and a data warehouse exactly? While these two terms might sound interchangeable at first, there are some significant differences between them.
Here are three key differences between a data warehouse and a data lake:
1. Data types
When it comes to the difference between a data warehouse and a data lake, the types and formats of the data these systems store can vary.
A data warehouse stores processed and refined data. Processed data is data that is collected and translated into usable information. In other words, processed data can provide actionable insights to help you improve your marketing campaigns and processes to drive better results for your business.
For example, with processed data, you can analyze a collection of user demographic data to view the location of the majority of your website visitors.
You can then use this information to learn more about your audience and implement more effective campaigns that target users in that location.
Data lakes store raw and unprocessed data. Raw data is data that has been collected from a source but hasn’t yet been processed. Unlike processed data, raw data is stored in a data lake in its original form and can’t provide any actionable insights for your marketing strategies.
For example, you can collect valuable information from a source, such as their location, job title, industry, and more. However, this data isn’t analyzed with data from other sources to enable you to understand the job title and industry of the majority of your leads.
Another significant difference between a data lake and a data warehouse is its purpose. Businesses will use a data warehouse vs. data lake for various reasons.
You can use a data warehouse to not only store data but also organize, manage, and analyze data from a variety of sources. With a data warehouse, you can create custom dashboards that help you analyze your collection of data in an organized and easy-to-understand way.
As a result, you can use a data warehouse to analyze a visual representation of your website data to gain valuable insights into how visitors interact with your site.
On the other hand, you can use a data lake to store a vast collection of raw data that you’ll process and analyze in the future. Unlike a data warehouse, data lakes don’t enable you to take advantage of analytics tools to help you interpret and understand your data.
Most businesses use a data lake to store a large amount of data to organize and process it using another platform on their in-house team.
Another difference between a data warehouse vs. data lake is the people and companies that use them.
From small to medium-sized businesses (SMBs) to enterprises, various companies can use data warehouses to store and analyze their data. Because a data warehouse offers numerous analytics tools and features to help you interpret your data, they are usually a preferred choice amongst businesses.
In addition, if you have a smaller team or lack data analysts altogether, you can use a data warehouse to help you organize your data and save you both time and resources.
Larger businesses with a sizable team of data processors and analysts typically invest in data lakes. That’s because companies usually use data lakes to store raw, unprocessed data.
Their team of data analysts and processors can then interpret and organize the data to create actionable insights to inform their marketing strategies.
Data lake vs. data warehouse: Which is the right fit for your business?
So, now that you know the major differences between a data warehouse vs. data lake, you might wonder which one is the best fit for your business. Your choice can depend on the unique needs and goals of your business.
If you need to store a vast amount of data and have the resources to later organize and process this data, a data lake could be a good fit for your business.
On the other hand, if your business lacks the time and resources to organize large amounts of data or you want to take advantage of custom dashboards and analytics tools to interpret your data, a data warehouse could be the perfect fit for your company.
Due to their user-friendly interfaces and analytics features, data warehouses are usually the preferred option for companies just getting started with data-driven marketing. That’s because data warehouses provide actionable insights that enable you to optimize your marketing strategies to drive more sales and revenue for your company.
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Can’t decide between a date lake and a data warehouse?
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Abby is a data-driven content marketer with certifications in inbound marketing and content marketing. She’s written hundreds of articles covering digital marketing topics — a few of her specialties include first-party data, marketing and data analytics, marketing strategy optimization, and SEO. When she isn’t writing or optimizing content, Abby loves to spend time reading the latest mystery novel, having movie marathons with lots of snacks, and jamming out to Shania Twain.
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