Apache Hadoop emerged on the IT scene in 2006 with the promise to provide organizations with the capability to store an unprecedented volume of data using commodity hardware. This promise not only addressed the size of the data sets, but also the type of data, such as data generated by IoT devices, sensors, servers, and social media that businesses were increasingly interested in analyzing. The combination of data volume, velocity, and variety was popularly known as Big Data.
Schema-on-read played a vital role in the popularity of Hadoop. Businesses thought they no longer had to worry about the tedious process of defining which tables contained what data and how are they connected to each other — a process that took months and not a single data warehouse query could be executed before it was complete. In this brave new world, businesses could store as much data as they could get their hands on in Hadoop-based repositories known as data lakes and worry about how it is going to be analyzed later.