From raw data to insights: How to ingest data from Eventstream into a KQL database for powerful analytics
Synapse Real-Time Analytics in Microsoft Fabric can help you capture, transform, route, analyze and visualize huge amounts of real-time data from different sources in a matter of seconds. In this blog post, we’ll learn about the integration between Eventstream and a KQL database, both of which are a part of the Real-Time Analytics experience.
The Eventstream feature allows you to capture real-time events from a variety of sources such as Azure Event Hubs, custom applications, and even a sample dataset of taxi rides data and stock rates. It also offers a drag and drop experience that enables you to create event data processing, transforming, and routing logic without requiring any coding experience. The end-to-end data flow diagram in Eventstream provides a comprehensive understanding of the data flow and organization.
A KQL database is a fast, powerful, scalable, and highly available tool that is designed to handle large volumes of data. The Kusto Query Language (KQL) provides a simple yet powerful syntax that allows anyone to query, analyze, and visualize data to get insights in real-time.
KQL allows data to be queried as it’s ingested, and can quickly analyze structured, semi-structured, and unstructured data, including free text, using built-in operators and functions for trend analysis, pattern recognition, anomaly detection, forecasting, and machine learning capabilities.
This allows, for example, businesses to quickly respond to changes in their operations and make informed decisions based on the latest data. KQL Databases can ingest data from a wide range of sources, including Eventstream, making it easy to get data into the service and start analyzing it.
The integration between Eventstream and your KQL database is straightforward. You can add a KQL database as a destination to your eventstream and route the event data to your KQL database in real-time, using a friendly wizard. If desired, Eventstream provides the option to transform and filter the data according to your needs.
Screenshot of Eventstream Data Preview showing successful ingestion of Taxi data into a KQL database
Once your data is ingested into your KQL database, you can utilize KQL queries to gain valuable insights into your data.
Screenshot of a KQL queryset showing an example query analyzing Yellow Taxi sample data received from Eventstream
Example of using KQL queries to analyze sample Yellow Taxi sample data received from Eventstream
The video below provides an end-to-end example, which includes several KQL queries for analyzing the sample Yellow Taxi sample data stream.
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