We call an application data intensive if data management is one of the primary challenges in developing the application. While in compute-intensive systems the challenge is parallelizing a very large computation, in data-intesnive applications we usually worry more about things like storing and processing large data volumes The difference in use between backend engineers (who modify data & generally look at one user at a time) and business analysts/data scientists has led to a split between operational systems and analytical systems that are often kept separate. point query: a query that looks up a small number of records based on a key OLTP: Online Transaction Processing - a system that inserts, updates or deletes records generally based on a key OLAP: Online Analytical Processing - a system that generally scans a huge number of records to calculate aggregate statistics rather than returning individual records to the user They differentiate ClickHouse etc (Pinot, Druid? never heard…
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