What is Continuous SQL?
Continuous SQL is, at its core, the usage of Structured Query Language for stream processing. With it, you can use SQL to create computations against boundless streams of data as well as create materialized views of that data. Continuous SQL is typically used with technologies such as Apache Kafka, Apache Flink, AWS Kinesis, and Apache Pulsar.
Increasing business demands for faster and easier access to streaming data are driving the use of Continuous SQL. Every company is a data company, and with the rapidly increasing growth of streaming data, every company must become a streaming data company in order to compete effectively—or at all. Streaming data massively boosts an organization’s ability to create the killer applications that provide highly differentiated, competitive, and compelling user experiences.
However, distilling down these massive data streams, performing useful computations, and creating the materialized views to power the new breed of applications requires new and specialized designs, which take time and resources that many organizations do not have easy access to. This slows down time to market and adds pressure to already over-tasked data teams, which hinders business operations and innovation.
Organizations are looking to Continuous SQL—also known as streaming SQL—to simplify and accelerate how they are able to build, join, manage, scale, and deliver critical streaming data pipelines. Now, instead of requiring specialized Java and Scala knowledge and the extensive timeline required for deployment, a broader group can inspect and reason about streaming data using SQL. This enables organizations to take advantage of their streaming data and build the systems, applications and more that rely on this critical data and that drive the business forward.
Get Started with Continuous SQL in Minutes
Start benefiting from SQL on streams today with the Eventador Platform, the streaming data engine for building killer applications, using Continuous SQL.
In minutes, you can deploy a robust Continuous SQL interface to streamline how you interact and reason with your event streaming data in addition to:
- Creating stream processors, computations, and materialized views faster by using ANSI SQL with Kafka.
- Iterating on SQL statements instantly with an intelligent SQL parser.
- Deploying production stream processing jobs quickly using Continuous SQL.
- Scaling up your streaming jobs with a single click.
- Managing state simply with the ability to stop and restart streaming jobs from savepoint.
Continuous SQL versus Traditional SQL
Running SQL against boundless streams of data requires a mindset change. While much of the SQL grammar remains the same, how the queries work, the results that are shown and the overall processing paradigm is different than with traditional relational database management systems (RDBMS). In a RDBMS, SQL is interpreted and validated, an execution plan is created, a cursor is spawned, results are gathered into that cursor, and then iterated over for a point-in-time picture of the data. This picture is a result set, and it has a start and an end.
In contrast, Continuous SQL queries continuously process results to a sink or destination of some type. The SQL statement is interpreted and validated against a schema, the statement is then executed and the results matching the criteria are continuously returned. Jobs defined in SQL look a lot like regular stream processing jobs with the difference being they were created using SQL vs something like Java, Scala, or Python. Data being emitted via Continuous SQL are the continuous results—there is a beginning but no end.
Companies are doubling down on streaming data, and that reliance is only going to become more prevalent and, in fact, necessary to survival. In order to compete and capitalize on streaming data as well as scale to meet growing business demands, organizations are looking to Continuous SQL to simplify, accelerate, and make possible streaming data, and business, success.