Real-Time SQL Streaming Data Processing in Databricks: Hands-On Exercises

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Real-time data streaming has become a crucial element in every business, and SQL streaming on Databricks allows for near-instant processing and a surge in throughput levels. Here, we provide some hands-on exercises with SQL code examples to shine a beam on the fortitude of Databricks’ streaming capabilities.

Setup

Firstly, it’s important to understand that Databricks is built upon Apache Spark SQL. This gives it the ability to support a wide range of data types, functions, and analytics that are familiar to SQL users.
To begin, ensure you have a Databricks account and an active workspace. Here’s an SQL query example that deals with retrieving data from a table. This is the most basic form of an SQL query:

Reading Streams

Databricks allows for the reading of streams through its Structured Streaming API. This way, you can deal with real-time data. To read a stream, we use the readStream format as indicated below with an input source (file in this case):

Streaming with SQL

Structured Streaming allows for SQL queries right on the data streams. Here is an example which carries out a simple aggregation on a stream:

Handling Streaming Data

You should note that with Databricks, handling streams is a bit different. Here, output is managed in ‘micro-batches.’ Following is an example to illustrate the same:

This code starts the streaming computation and returns a StreamingQuery which can then be used in conjunction with awaitTermination() to wait for the termination signal.

Databricks SQL streaming underpins the capabilities of an organization to deal with real-time data and glean insights almost instantly. Once you get the hang of it, the possibilities are limitless.

This post just scrapes the surface of real-time SQL streaming data on Databricks. For a much more detailed view and hands-on exercises, head over to the Databricks documentation and dive in.