For a long time, I do not understand the concepts of Data Lake and Data Warehouse. It seemed to me that they are the same thing — a data storage where I can find needed data and process it for my purposes.
I wasn't wrong but there is a difference.
Data Warehouse supports the flow of data from operational systems to analytics/decision systems by creating a single repository of data from various sources (both internal and external). In most cases, a Data Warehouse is a relational database that stores processed data that is optimized for gathering business insights🔍. It collects data with predetermined structure and schema coming from transactional systems and business applications, and the data is typically used for operational reporting and analysis.
𝐹𝑜𝑟 𝑒𝑥𝑎𝑚𝑝𝑙𝑒, 𝑙𝑒𝑡’𝑠 𝑠𝑎𝑦 𝑦𝑜𝑢 ℎ𝑎𝑣𝑒 𝑎 𝑟𝑒𝑤𝑎𝑟𝑑𝑠 𝑐𝑎𝑟𝑑 𝑤𝑖𝑡ℎ 𝑎 𝑔𝑟𝑜𝑐𝑒𝑟𝑦 𝑐ℎ𝑎𝑖𝑛. 𝑇ℎ𝑒 𝑑𝑎𝑡𝑎𝑏𝑎𝑠𝑒 𝑚𝑖𝑔ℎ𝑡 ℎ𝑜𝑙𝑑 𝑦𝑜𝑢𝑟 𝑚𝑜𝑠𝑡 𝑟𝑒𝑐𝑒𝑛𝑡 𝑝𝑢𝑟𝑐ℎ𝑎𝑠𝑒𝑠, 𝑤𝑖𝑡ℎ 𝑎 𝑔𝑜𝑎𝑙 𝑡𝑜 𝑎𝑛𝑎𝑙𝑦𝑧𝑒 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑠ℎ𝑜𝑝𝑝𝑒𝑟 𝑡𝑟𝑒𝑛𝑑𝑠. 𝑇ℎ𝑒 𝑑𝑎𝑡𝑎 𝑤𝑎𝑟𝑒ℎ𝑜𝑢𝑠𝑒 𝑚𝑖𝑔ℎ𝑡 ℎ𝑜𝑙𝑑 𝑎 𝑟𝑒𝑐𝑜𝑟𝑑 𝑜𝑓 𝑎𝑙𝑙 𝑜𝑓 𝑡ℎ𝑒 𝑖𝑡𝑒𝑚𝑠 𝑦𝑜𝑢’𝑣𝑒 𝑒𝑣𝑒𝑟 𝑏𝑜𝑢𝑔ℎ𝑡 𝑎𝑛𝑑 𝑖𝑡 𝑤𝑜𝑢𝑙𝑑 𝑏𝑒 𝑜𝑝𝑡𝑖𝑚𝑖𝑧𝑒𝑑 𝑠𝑜 𝑡ℎ𝑎𝑡 𝑑𝑎𝑡𝑎 𝑠𝑐𝑖𝑒𝑛𝑡𝑖𝑠𝑡𝑠 𝑐𝑜𝑢𝑙𝑑 𝑚𝑜𝑟𝑒 𝑒𝑎𝑠𝑖𝑙𝑦 𝑎𝑛𝑎𝑙𝑦𝑧𝑒 𝑎𝑙𝑙 𝑜𝑓 𝑡ℎ𝑎𝑡 𝑑𝑎𝑡𝑎.
Although data warehouses can handle unstructured data, they don’t do so in the most efficient manner. With so much data out there📈 , it can get expensive to store all of your data in a database or a data warehouse. Also, data that goes into data warehouses need to be processed before it gets stored — with today’s massive amount of unstructured data, that could take significant time and resources. In response, businesses started maintaining Data Lakes, which store all of an enterprise’s structured and unstructured data at scale in the most cost-effective manner possible. Data Lakes store raw data, and could be set up without having to first define the data structure and schema.
Data Lakes allow users to run analytics without having to move the data to a separate analytics system.
Photo by Tom Gainor on Unsplash
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