If you're even tangentially involved in big data, you are aware that finding storage solutions for the volumes of data being created every instant is of utmost importance. In regards to managing data, data professionals may look at employing a data warehouse or an info lake as a data repository. So as to determine what's best for your company, let's first define what they are and then compare them.
What is a Data Lake?
Some wrongly believe that a data lake is just the 2.0 model of a data warehouse. While they are similar, they're different tools that should be used for different purposes. James Dixon, the CTO of Pentaho is credited with naming the idea of a data lake. He uses another analogy:
"If you think of a data mart as a store of water -- cleansed and packaged and structured for simple consumption -- that the data lake is a huge body of water at a natural state. The contents of this data lake flow in from a source to fill the lake and various consumers of the lake could come to analyze, dive in, or accept samples."
A data lake retains data in an unstructured manner and there's absolutely not any hierarchy or company one of the individual parts of data. It retains Data in its rawest form--it is not processed or analyzed. Additionally, a data ponds take and retain all Data from all Data sources, supports all data types and schemas (exactly the means by which the data is stored in a database) are employed only when the data is ready to be utilized.
What is a Data Warehouse?
A data warehouse stores data within an organized manner with everything archived and arranged in a defined manner. After a data warehouse is designed, a significant amount of effort happens during the first stages to examine data sources and comprehend business processes. Decisions are made regarding what data to include and exclude in the warehouse. Data is only loaded into the warehouse when a use for the data has been identified.
How do Data lakes and Data warehouses differ?
Data lakes retain all of the data--organized, semi-structured and unstructured/raw data. It's possible that a number of the data in a data lake won't ever be utilized. Data ponds retain all data too. A data warehouse just includes data that is processed (ordered ) and just the Data that is required to use for reporting or to answer specific business questions.
Since an Data lake lacks structure, it's relatively simple to make adjustments to models and queries. Data ponds are more flexible and can be configured and reconfigured as necessary based on the job you require it to do. It's a lot harder and time-consuming to modify the construction of a data warehouse due to the number of company procedures tied to it.
Since data warehouses are more mature than data lakes, the security for data warehouses can also be more mature. There's also concern that because all data is stored in 1 repository at a data lake which it also creates the Data more vulnerable. It certainly makes auditing and compliance simpler with only one shop to manage.
Data scientists are typically the individuals who access the data in data lakes since they have the exact skill-set to do a deep investigation. Technically, data lakes can encourage all customers and so are available to all. Data warehouses are utilized by specific small business users to extract and report a particular significance from the Data which was defined when the data warehouse has been put up; they are usually too restrictive for data scientists who must go beyond the boundaries of the warehouse to glean new investigation from the data.
Data lakes and Data warehouses are different tools for different purposes. If you already have a proven data warehouse, you might opt to employ an info lake along with it to solve for a few of the constraints you encounter with a data warehouse. To ascertain if it's the data lake or data warehouse is ideal for your requirements, you should begin with the target you are attempting to attain and use the Data repository which will help you meet your objective.