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Today, businesses generate more data than ever before. Thanks to IoT devices, real-time user interaction, and telematics, among others, we’re experiencing an explosion of high-volume, high-velocity data, often referred to as “Big Data.” Central to making sense of these vast data sets efficiently is the use of streaming analytics. In this context, we’ll explore how to handle high-volume data in Databricks using SQL streaming analytics.
What is SQL Streaming Analytics?
Streaming analytics involves analyzing data in real-time as it arrives. SQL’s real-time streaming analytics considering unbounded and continuous data as a stream. When using Databricks, this typically involves Structured Streaming, a scalable and fault-tolerant stream processing engine built on the Spark SQL engine.
Setting up the Data Stream
The first step is to set up a data stream for analysis. Below is a typical example of creating a streaming DataFrame that reads data from a Kafka source. Kafka is a distributed streaming platform suitable for handling real-time data feeds.
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-- Define the input stream val inputStream = spark.readStream .format("kafka") .option("kafka.bootstrap.servers", "host1:port1,host2:port2") .option("subscribe", "topic1") .load() |
Running SQL on Streaming Data
1. Aggregations
Commonly, you’ll need to perform aggregations on the streaming data. You can express computations as you would on static data. The system will continuously update the result as streaming data continues to arrive.
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-- Convert columns to appropriate types and alias them val data = inputStream .selectExpr("cast (value as string)", "cast (timestamp as timestamp)") .as[(String, Timestamp)] -- Group data by window and word and compute counts val windowedCounts = data .groupBy(window($"timestamp", "10 minutes"), $"value") .count() |
2. Join Operations
You can also join streaming data with static datasets. Consider the case where you have user activity data streaming in real-time, and you have a static dataset of user profile data, which you want to join to do real-time user profiling.
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-- Define static DataFrame val staticDf = spark.read .format("parquet") .load("/path/to/static/data") -- Perform join operation val joinedData = data.join(staticDf, "userID") |
Summary
SQL in Databricks is a powerful tool for handling high-volume data in real-time. It provides a familiar SQL interface for real-time streaming analytics. By using the built-in functionality like aggregations and joins, you can effectively make sense of your streaming data.
Streaming analytics is an infinitely wide topic, and there’s still so much left. In the upcoming blogs, I’ll cover more advanced concepts including watermarks and handling late data. Stay tuned!