If you’re even tangentially involved with big data, you know that finding storage solutions for the volumes of data being generated every second is of utmost importance. When it comes to managing data, data professionals can consider using a data warehouse or a data lake as a data repository. In order to determine what’s best for your organization, let’s first define what they are and then compare them.
What is a data lake?
Some mistakenly believe that a data lake is just the 2.0 version of a data warehouse. While they are similar, they are different tools that should be used for different purposes. James Dixon, the CTO of Pentaho is credited with naming the concept of a data lake. He uses the following analogy:
“If you think of a datamart as a store of bottled water – cleansed and packaged and structured for easy consumption – the data lake is a large body of water in a more natural state. The contents of the data lake stream in from a source to fill the lake, and various users of the lake can come to examine, dive in, or take samples.”
A data lake holds data in an unstructured way and there is no hierarchy or organization among the individual pieces of data. It holds data in its rawest form—it’s not processed or analyzed. Additionally, a data lakes accepts and retains all data from all data sources, supports all data types and schemas (the way the data is stored in a database) are applied only when the data is ready to be used.
What is a data warehouse?
A data warehouse stores data in an organized manner with everything archived and ordered in a defined way. When a data warehouse is developed, a significant amount of effort occurs during the initial stages to analyze data sources and understand business processes. Decisions are made regarding what data to include and exclude from the warehouse. Data is only loaded into the warehouse when a use for the data has been identified.
Data lakes retain all data—structured, semi-structured and unstructured/raw data. It’s possible that some of the data in a data lake will never be used. Data lakes keep all data as well. A data warehouse only includes data that is processed (structured) and only the data that is necessary to use for reporting or to answer specific business questions.
Since a data lake lacks structure, it’s relatively easy to make changes to models and queries. Data lakes are more flexible and can be configured and reconfigured as necessary based on the job you need it to do. It’s much more cumbersome and time-consuming to change the structure of a data warehouse due to the number of business processes tied to it.
Data scientists are typically the ones who access the data in data lakes because they have the skill-set to do deep analysis. Technically, data lakes can support all users and are available to all. Data warehouses are used by specific business users to report and extract a particular meaning from the data that was defined when the data warehouse was set up; they are usually too restrictive for data scientists who need to go beyond the boundaries of the warehouse to glean new analysis from the data.
Since data warehouses are more mature than data lakes, the security for data warehouses is also more mature. There is also concern that since all data is stored in one repository in a data lake that it also makes the data more vulnerable. It certainly makes auditing and compliance easier with just one store to manage.
Data lakes and data warehouses are different tools for different purposes. If you already have an established data warehouse, you might choose to implement a data lake alongside it to solve for some of the constraints you experience with a data warehouse. To determine whether a data lake or data warehouse is best for your needs, you should start with the goal you are trying to achieve and use the data repository that will help you meet your goal.