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But , the velocity and volume of streaming data is massive , which has dictated that new architectures in data systems be invented . Ingesting the firehose of data , distilling down the useful parts , and routing it to this new breed of applications requires new and specialized designs . rate and the ability to process that data . Gaining access to streaming data for immediate processing requires special skills in Java , Scala or similar languages . Plain old Structured Query Language ( SQL ) can be used to query such data once it has landed into a SQL-compatible store .
Because these streaming systems are not siloed , they are typically used alongside and in conjunction with more traditional technologies like relational database systems ( PostgreSQL , MySQL , Oracle ), data lakes / data warehouse ( Hive , Impala ), or even NoSQL systems ( MongoDB , Elastic ).
Further , the modern enterprise is on a collision course between the desire to capture data at an increasing
Given the popularity of SQL skills across the industry over other specialized skills , it would be ideal to query real-time data with just SQL while it was still in the streaming system .
And thus , Streaming SQL was born . Streaming SQL solves this problem by continuously running the SQL processes on the boundless stream of business data . p
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