Difference Between Data Warehousing and Data Lake

  Difference Between Data warehousing and Data Lake

 

The difference between the two most popular options for storing big data but according to the nature of data both have some important differences also so first understand it individually after that we will also know the differences.

 Data Warehouse: - A data warehouse is a repository for structured, filtered data that

                                    has already been processed for a specific purpose.

 Data Lake:- The data lake is a vast pool of raw data, the purpose for which is not yet defined.

 Difference:-

 Data lakes store data from a wide variety of sources like IoT devices, real-time social media streams, users, and web application transactions. Sometimes this data is structured, but often, it’s quite messy because data is being ingested straight from the data source. Data warehouses, on other hand, contain historical data that has been cleaned to fit a relational schema.

 Data lakes are used for the cost-effective storage of large amounts of data from many sources allowing data of any structure to reduce cost because data is more flexible and scalable as the data does not need to fit into a specific schema.  However, structure data is easier to analyze because it’s cleaner and has a uniform schema to query from. Restricting data to a schema, data warehouses are very efficient for analyzing historical data for specific data decisions.

 Data lakes and data warehouses are useful for different users. Data analysts and business analysts often work within data warehouses containing explicitly pertinent data that has been processed for their work. Data Warehouses require a lower level of programming and data science knowledge to use. Data lakes are set up and maintained by data engineers who integrate them into data pipelines. Data Scientists work more closely with data lakes as they contain data of a wider and more current scope.

 Data engineers use data lakes to store incoming data. However, data lakes are not only limited to storage. Remember, unstructured data is more flexible and scalable, which is oftentimes better for big data analytics, big data analytics can run on data lakes using services such as Apache Spark and Hadoop. This is especially true for deep learning, which requires scalability to increase the amount of training data. Data Warehouses are typically set to read-only for analyst users, who are primarily reading and aggregating data for insights. Since data is already clean and archival, there is usually no need to insert or update data. 

 It should be no surprise that data lakes are much bigger in size because they retain all data that might be relevant to a company. Data lakes are often petabytes in size that are 1,000 terabytes! Data warehouses are much more selective on what data is stored