What is ksqlDB?: The Ultimate Guide

• January 20th, 2022


Apache Kafka is an Open-source and Distributed Stream Processing platform that stores and handles real-time messages or data. It has a dedicated set of Kafka Servers or Brokers distributed across Kafka Clusters to store and organize real-time messages.

To get meaningful insights from real-time data or messages stored on Kafka Servers, you should perform several Data Processing operations on those widespread continuous data. Usually, a client library called Kafka Streams is used to perform querying and Data Processing operations for extracting meaningful insights out of those real-time data streams present across various Kafka Servers.

However, to process real-time data using the Kafka Streams library, one should have strong programming and technical knowledge. To mitigate the need for having programming skills and background, Confluent introduced ksqlDB which allows you to execute queries on Kafka data for performing Data Processing operations.

Since ksqlDB is similar to SQL Language in syntax and query statements, it is straightforward even for non-technical folks to seamlessly process the Streaming Data in the Kafka Clusters.

In this article, you will learn about ksqlDB and how it is used to perform data processing operations on Kafka data.

Table of Contents


  • Fundamentals knowledge of Apache Kafka and Data Streaming.

What is ksqlDB?

ksqlDB - ksqlDB logo
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ksqlDB is a SQL engine or interface that enables you to perform stream processing or data analysis operations on messages present across the Kafka environment.

Since ksqlDB is distributed, scalable, and reliable across the Kafka ecosystem, it can seamlessly handle and process trillions of real-time data streaming into Kafka brokers or servers. ksqlDB transforms the continuously streaming real-time events or messages into highly organized Tables and Streams, allowing users to easily write queries on well-arranged data.

The syntax and statements of ksqlDB resemble the syntax of SQL Queries, making it easy for beginners to start working with ksqlDB. ksqlDB can perform Data Processing operations like data cleaning, filtering, joining, aggregation, sessionization, and parsing.

The installation of ksqlDB is very straightforward as you can embed and set up the ksqlDB CLI with your existing Apache Kafka version to perform Data Processing operations. Because of its user-friendly and easy-to-query feature, ksqlDB is being used in various real-time applications like Streaming Analytics, Continuous Monitoring, building ETL Pipelines from Real-time Data, Online Streaming, Data Integration, Developing Event-driven microservices, etc.

How to Implement Streaming Analysis With ksqlDB?

You can run ksqlDB by three methods for implementing queries on data present in Kafka Brokers or Servers. The methods are for real-time data processing via Standalone ksqlDB, Confluent Platform, and Confluent Cloud.

A) Standalone ksqlDB

It runs as a standalone application that natively runs by being a part of the existing Apache Kafka installation. The default docker-compose file runs and starts every prerequisite via Docker to set up the Kafka environment, including Kafka server, Zookeeper instance, and ksqlDB.

If you already have only the Apache Kafka installation without the Docker file, you have to just clone the  Github Repository to embed, access, and work with ksqlDB.

B) Confluent Platform

It is a wholesome package that comprises all the features and functionalities to perform Data Querying Operations on Kafka data. This also acts as a one-stop solution that provides access to several Kafka services like Kafka Servers, Zookeeper, Confluent Schema Registry, and Kafka Connect.

Since ksqlDB comes with the Confluent platform by default, you can effectively perform Stream or Data Processing operations on Kafka data.

C) Confluent Cloud

Confluent Cloud is a fully managed cloud environment that allows you to run ksqlDB to handle and process data present across Kafka clusters. It provides you with a web UI (User Interface) and command-line interface by which you can manage and analyze cluster resources and data present in Kafka servers.  

Simplify Kafka ETL with Hevo’s No-code Data Pipeline

Hevo Data, a No-code Data Pipeline, helps load data from any data source such as Databases, SaaS applications, Cloud Storage, SDK,s, and Streaming Services and simplifies the ETL process. It supports 100+ Data Sources including Apache Kafka, Kafka Confluent Cloud, and other 40+ Free Sources.

You can use Hevo’s Data Pipelines to replicate the data from your Apache Kafka Source or Kafka Confluent Cloud to the Destination system. It loads the data onto the desired Data Warehouse/Destination and transforms it into an analysis-ready form without having to write a single line of code.

Hevo’s fault-tolerant and scalable architecture ensures that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. Hevo supports two variations of Apache Kafka as a Source. Both these variants offer the same functionality, with Confluent Cloud being the fully-managed version of Apache Kafka.


Check out why Hevo is the Best:

  • Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled securely and consistently with zero data loss.
  • Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
  • Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
  • Hevo Is Built to Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
  • Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
  • Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
  • Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.

Stream Processing Operations Using ksqlDB on Confluent Cloud

Since it is a fully managed service with an interactive UI, you can flexibly perform Stream Processing operations using ksqlDB instead of locally setting up the Kafka environment to run queries on Kafka data.

This eliminates the essential prerequisite of setting up a typical Kafka infrastructure to run queries on Kafka data, and you can focus more on analyzing and processing the Real-time Streaming Data, thereby boosting the overall productivity. 

ksqlDB - Confluent Cloud Sign-in page
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Step 1: Initially, you need to sign up with the Confluent Cloud platform. Follow this link and click on “Sign up and try it for free.” You will be redirected to a new page to fill in essential user information, including name, email ID, and password, for setting up the Confluent Cloud account.

After filling in the essential details, click on the “Start Free” button. Now, you are successfully signed up with the Confluent cloud platform. 

Step 2: Open the respective email address you provided while signing up. Click on the verification link to start working with the Confluent Cloud platform. 

Following the verification link sent to your email address, you will be asked to sign in with the Confluent Cloud’s login id and password.  Then, you will be redirected to the welcome page of Confluent Cloud, where you will be prompted to fill in some user information. 

Step 3: In the next step, you will create Kafka Clusters where the Kafka Cluster consists of a dedicated set of servers or brokers running across the Kafka Environment to produce, store and consume data. 

Click on the “Create Cluster” button as shown in the image below:

ksqlDB - Creating Kafka Cluster
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Step 4: In the next step, you will be prompted to select the type of Kafka Cluster you want to create for storing messages.

On selecting the Cluster Type, you can determine the Kafka cluster’s features, usage limits, and price according to your liking. As of now, select the “Basic” Cluster type. However, you can upgrade to the Standard Cluster type at any time. Click on the “Begin Configuration” button. 

ksqlDB - Creating Kafka Cluster type
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Step 5: In the next step, you will be asked to select your preferred cloud providers, such as GCP, AWS, and Azure, in which you will create your Kafka clusters. Select the Respective Cluster to provide the region and availability details. 

Step 6: Now, you will be redirected to the payments page. Provide the payment details, name the cluster with a unique name, review the payment method and click on the “Launch Cluster” button. 

ksqlDB - Configuring Kafka Cluster
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On executing the above steps, you successfully created a Kafka cluster for storing messages.

ksqlDB - Naming Kafka Cluster
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The newly created cluster will be displayed on the Cluster overview page.

Step 7: Now, you are all set to create a Kafka Topic, which stores and organizes messages present in a Kafka Server. With an interactive UI, you can easily create Kafka Topics in minutes instead of writing and executing commands from a command-line tool.

You can refer to the process of Kafka Topic creation, producing sample messages using the Datagen Source Connector, consuming Kafka messages, inspecting data streams in the official documentation by Confluent. With their respective documentation link, you learned to create a Kafka topic with the name “users.”

Now, create another Kafka topic with the name “pageviews” by following the steps as given in the official documentation. Now, you have two Kafka topics, namely “users” and “pageviews.” In the further steps, you will be writing queries using ksqlDB to process the data present within those two topics.

Step 8: Initially, you have to register both “pageview” and “users” topics as a Stream and Table, respectively, because ksqlDB will not let you directly query or work on Kafka Topics to perform Stream Processing operations. 

You can create Streams and Tables from Kafka topics using the CREATE STREAM and CREATE TABLE statements. Such statements or queries will be written and executed in the ksqlDB editor for Data Processing operations. Now, navigate to the ksqlDB in the left side panel.

Click on the respective ksqlDB application that you launched for creating Kafka Topics. You will be redirected to the ksqlDB editor tab, where you can write and execute queries.

ksqlDB Application- ksqlDB
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Step 9: Firstly, you will create streams in the name of pageviews_original with the parameters, such as view time, userid, and pageid.

Create Stream Pageviews- ksqlDB
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You will get the output as shown below.

ksqlDB Application Editor - ksqlDB
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Then, you can inspect the newly created stream by executing the following command.

ksqlDB Running Queries - ksqlDB
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  • The select * (Select all) command displays all the information about message fields and messages present within the  “pageviews_original” Kafka stream.

Step 10: In the next step, you will create tables with the parameters, such as userid, register time, gender, and regionid.

Create Table Users - ksqlDB
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Execute the following commons to inspect the newly created table.


The output of the above command resembles the following image.

ksqlDB - Executing Command
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Since the data is streaming in real-time, your query will continuously fetch messages flowing into the respective Kafka Topics. Click on the “Stop” button to end the execution. On creating streams and tables, data will get updated inside the Streams and Tables tab continuously.

ksqlDB - ksqlDB Tables
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  • Step 11: Click on the Tables option.
ksqlDB Application Table - ksqlDB
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You can see all the metadata about the newly created table, like table name, topic reference, schema, and data type description. With ksqlDB, you can also write persistent queries that connect one or more tables or streams. 

CREATE STREAM pageviews_enriched AS
SELECT users.userid AS userid, pageid, regionid, gender
FROM pageviews_original
ON pageviews_original.userid = users.userid
ksqlDB - Running Query
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You will get the output as shown above. The Persistent Query creates a new stream with the name “pageviews_enriched” that includes parameters retrieved from pageviews_original. Then, the JOINS Clause further merges the “users” table and pageviews_original streams by keeping “userid” as the common parameter.

Step 11: To inspect the newly created Persistent Query, navigate to the “Persistent Queries” tab.

ksqlDB app1 - Query
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Click on the “Explain Query” button to see the scheme and query properties of the newly created Persistent Query.

ksqlDB app1 - ksqlDB Flow
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Step 12: Navigate to the Flow tab to visualize the overall Data Flow of ksqlDB.

In the Flow tab, you can view the entire topology of the ksqlDB workflows, including streams and tables created with ksqlDB. It displays the details of the streams and tables along with their unique name. You can also view the Query Statements used to create the respective Streams and Tables using ksqlDB.

ksqlDB app1 - Creating Streams
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On clicking on the “CREATE-STREAM” node from the overall topology, you can view the persistent query or statement used while creating the new stream called “PAGEVIEWS_ENRICHED.”

ksqlDB app1 - Pageviews Enriched
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You can further investigate the new stream by clicking on the “PAGEVIEWS_ENRICHED” node. You can view the real-time streaming messages into the respective stream and its schema.

On executing all the above-mentioned steps, you have successfully created Kafka Clusters and Topics on the Confluent Cloud platform to execute ksqlDB queries on the Kafka data. 


In this article, you have learned about ksqlDB and how to execute ksqlDB Queries on the Confluent Cloud Platform for processing real-time streaming data present across the Apache Kafka Ecosystem.

However, you can also execute the ksqlDB Queries on Kafka data using the Confluent platform CLI and standalone ksqlDB server. On trying ksqlDB via different mediums, you can analyze and compare the Data Processing speed, accuracy, and request-response rates of queries according to each Kafka Distribution or Environment. 

Extracting complicated data from Apache Kafka, on the other hand, can be Difficult and Time-Consuming. If you’re having trouble with these and want to find a solution, Hevo Data is a good place to start!


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Share your experience of learning about ksqlDB in the comments section below!

No-code Data Pipeline For Apache Kafka