Key features of SSB
SQL Stream Builder (SSB) within Cloudera supports out-of-box integration with Flink. Using Flink SQL, you can create tables directly from the Streaming SQL Console window or built-in templates. For integration with Business Intelligence tools you can create Materialized Views.
- Flink SQL
- SQL Stream Builder allows you to use Data Definition Language (DDL), Data Manipulation Language (DML) and Query Language directly from the Streaming SQL Console.
- Change Data Capture
- SQL Stream Builder supports PostgreSQL, Oracle, MySQL, Db2 and SQL Server as Debezium
connectors using Flink SQL. With Change Data Capture (CDC), you can capture changes in
your databases and update your applications with the newly added data.
For more information about Debezium, see the official documentation.
- Session Cluster
- When you start SSB, a session cluster is deployed to share and maintain resources. The submitted SQL jobs are executed as Flink jobs in the same session cluster that share a Job Manager. The properties for the session cluster can be viewed using the Streaming SQL Console, and can be modified with SET statements in the SQL window.
- Built-in Templates
- The Build-in Templates in SSB allows you to quickly and simply create tables for your SQL queries. You only need to provide the connection and job specific information to the template to use it in SSB.
- Streaming SQL Console
- SSB comes with an interactive user interface that allows you to easily create, and manage
your SQL jobs in one place. It allows you to create and iterate on SQL statements with
robust tooling and capabilities. Query parsing is logged to the console, and results are
sampled back to the interface to help with iterating on the SQL statement as required.
- Materialized Views
- SSB has the capability to materialize results from a Streaming SQL query to a persistent view of the data that can be read through REST and over the PG wire protocol. Applications can use this mechanism to query streams of data in a way of high performance without deploying additional database systems. Materialized Views are built into the SQL Stream Builder service, and require no configuration or maintenance. The Materialized Views act like a special kind of sink, and can even be used in place of a sink. They require no indexing, storage allocation, or specific management.
- Input Transform
- In case you are not aware of the incoming data structure or raw data is being collected from for example sensors, you can use the Input Transform to clean up and organize the incoming data before querying. Input transforms also allow access to Kafka header metadata directly in the query itself. Input transforms are written in Javascript and compiled to Java bytecode deployed with the Flink jar.
- User-defined Functions
- You can create customized and complex SQL queries by using User-defined Functions to enrich your data, apply computations or a business logic on it. User defined functions are written in Javascript or Java language.
