Streaming SQL has become an essential part of effective real-time data processing solutions. It enables data-driven organizations to analyze and act on streaming data and to quickly identify the value in new data. Using streaming SQL, organizations can quickly adopt a modern data architecture and create streaming data pipelines to support their business needs.
0:00 Intro
0:07 What is Streaming SQL
0:35 Difference from SQL
2:05 Use Cases
2:32 Tutorial Scenario
2:50 Flow Tutorial Begins
3:22 GitPod
6:35 Validation
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In recent years, the enormous growth in data volumes and the increasing need for real-time data analysis have made SQL a crucial component of data management and business analytics.
However, traditional SQL solutions that operate on stored data in databases cannot effectively support real-time data processing requirements.
As a result, there is a need for a new type of SQL that can process continuous data streams. This is where streaming SQL comes in.
Before we dive deeper, let us examine what exactly we mean by “streaming” in this context.
"Streaming" refers to the handling of data as a continuous flow of events or messages through message brokers such as Kafka, Gazette, Amazon Kinesis, or Apache Pulsar.
These event streams can include various types of data, from user actions on websites or mobile apps, IoT sensor data, and server metrics to traditional database activities captured using change data capture (CDC).
Traditional SQL runs on databases while streaming SQL runs on streams of live data. Running SQL on databases returns a static set of results from a single point in time. On the other hand, with streaming SQL you could run the exact same SQL query on a stream of real-time data and get a point-in-time answer.
In short, streaming SQL is designed to process subsets of data quickly and deliver results in real time.
Streaming SQL can transform, filter, aggregate, and enrich data in flight, making it a powerful tool for organizations to extract maximum value from constantly streaming data.
Also, streaming SQL can work with a wide range of data sources and environments, from public cloud platforms to Kafka, Hadoop, NoSQL, and relational databases.
#sql #data #dataengineering #datapipeline