It’s really a multi-protocol world for many of us in the API space. Our 2023 State of Software Quality revealed while RESTful APIs are still dominating, many API practitioners are using other protocols like GraphQL (23%), Apache Kafka (20%), and gRPC (9%). Additionally, while web APIs remain top experience (88%), we are seeing an increase in year-over-year support of events. As thought leaders, it’s important we continue to provide the tools you need to get the job done.
In this blog, we’ll dive head-first into the world of Apache Kafka, its many benefits, use cases and how to explore topics/channels. Hold on tight!
What is Apache Kafka and How Does it Work?
Apache Kafka is an open-source distributed platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integrations, mission-critical applications, and event-driven applications.
What does that mean? In layman terms, countless data sources generate continuous event streams, which are digital records of actions and their timestamps. These events can be actions triggering other processes, such as customer orders, flight seat selections, or form submissions.
Even non-human activities, like a connected thermostat reporting a temperature, qualify as events. These data streams present opportunities for real-time data-driven applications. Streaming platforms allow developers to build high-speed, accurate applications that process these streams while preserving event order.
Kafka offers three core capabilities:
- Publishing and subscribing to data streams.
- Preserving recording in their original order in a fault-tolerant manner.
- Real-time record processing.
Developers leverage these Kafka capabilities through four APIs:
- Producer API: Allows applications to publish streams to Kafka topics. Topics are log files storing records in the order they occur, and records remain in topics for a predefined duration or until storage space is exhausted.
- Consumer API: Enables applications to subscribe to topics, ingest, and process the stored stream, either in real-time or for historical data.
- Streams API: Extends Producer and Consumer capabilities, enabling sophisticated continuous stream processing. It allows consumption, analysis, aggregation, and transformation of records from multiple topics, with the option to publish resulting streams to the same or different topics.
- Connector API: Empowers developers to create reusable connectors that simplify and automate the integration of data sources into a Kafka cluster.
What Are the Benefits of Using Apache Kafka?
Apache Kafka offers several key benefits for data streaming and processing in various applications:
- Real-Time Data Streaming: Kafka allows real-time data processing, making it ideal for applications requiring instant data analysis.
- Scalability: Easily scale Kafka to handle large data volumes, ensuring system performance as your data needs grow.
- Fault Tolerance: Kafka is designed for fault tolerance, ensuring data integrity and availability, even in the event of failures.
- Durability: Data stored in Kafka topics is durable and can be retained for a specified period, making it suitable for long-term data storage and analysis.
- Data Integration: Kafka acts as a central hub for efficient data integration, facilitating communication between systems and applications.
- Decoupling: Kafka decouples data producers from consumers, allowing independent operation and easy component additions or modifications.
- High Throughput: Kafka handles high message throughput with low latency, making it perfect for processing millions of messages per second.
- Compatibility: Kafka provides client libraries for various programming languages, ensuring compatibility with a wide range of technologies.
- Ecosystem: It integrates seamlessly with tools like Apache ZooKeeper, Apache Flink, and Apache Spark for versatile data processing and analysis.
- Reliability: Trusted by large organizations and tech giants, Kafka offers reliability and strong support.
- Versatility: Kafka is adaptable to various industries and use cases, including log aggregation, event sourcing, and data pipelines.
- Community Support: As an open-source platform, Kafka benefits from an active community, ensuring ongoing development and support.
These benefits have propelled Kafka to popularity in industries such as finance, e-commerce, and social media, making it a go-to solution for real-time data processing needs.
Real World Use Cases for Apache Kafka
The modern consumer is accustomed to real-time global updates; from checking the score and commentary of a football game to refreshing the browser to get live traffic updates – these quick and seamless transfers of data is only possible due to streaming platforms like Kafka.
Companies use Kafka in various ways, many of which we, as consumers, use regularly:
Activity tracking: Websites with millions of users generate thousands of data points every second and that data is logged whenever you click on a page or a link. Companies use Apache Kafka to record and store events like user registration, page clicks, page views and items purchased. Some popular companies using Kafka include LinkedIn, Uber, and Netflix.
LinkedIn uses Kafka for message exchange, activity tracking, and logging metrics. With over 100 Kafka clusters, they can process 7 trillion messages daily. Uber is one of the largest deployments of Apache Kafka in the world. It uses the streaming platform for exchanging data between a user and a drive.
Real-time processing: Real-time data processing refers to the capturing and storing of event data in real-time. Conventional data pipelines run in scheduled batches and process all aggregated information during a specified time, but Apache Kafka allows organizations to process data on the fly. Business leaders leverage Kafka for revenue generation, customer satisfaction, and business growth. Popular financial services like ING, PayPal, and JPMorgan Chase leverage Kafka to ensure customers get a seamless experience.
ING used Kafka at first for powering a fraud-detection system but expanded to multiple customer centric use cases. PayPal uses Kafka to handle about 1 trillion messages per day. JPMorgan Chase uses Kafka to power monitoring and administrative tools, allowing real-time customer handling and decision-making.
How to Explore your Kafka Channels with SwaggerHub Explore
Alright, on to the good stuff. API exploration is an API testing practice that is taking the industry by storm. It refers to the process of discovering and familiarizing oneself with an API interface. It involves understanding the features, capabilities, and functionalities an API provides by interacting with it and exploring its endpoints, methods, parameters and responses. To learn more about API exploration check out our previous blogs.
You can easily interact with Kafka Channels using SwaggerHub Explore.
To explore Kafka Channels using SwaggerHub Explore, you can follow these steps:
- Go to SwaggerHub and log in to your account.
- Click on the Explore tab.
- In the Select a protocol dropdown menu, select Kafka.
- Choose Operation (Subscribe or Publish a Kafka message).
- Enter the following information:
- Kafka Server: Server for the Kafka service you want to explore.
- Topic/channel name: The name of the Kafka topic/channel.
- Authentication type: The authentication type used to connect to the Kafka broker.
- Username: The username for authentication (if required).
- Password: The password for authentication (if required).
- Custom Kafka broker or Kafka properties can be entered under “Connection Settings & Properties”
- Ensure all necessary and required data and metadata are added under “Headers and Parameters”
- Click on Publish/Subscribe button depending on what operation you are using.
- If you Publish to a topic/channel you will get a confirmation message validating that the Publish was successfully. If you have Subscribed to a topic/channel you will start receiving responses as they are sent to the channel.
Here are some additional tips for exploring Kafka Channels using SwaggerHub Explore:
- You can use the Parameters tab to view and edit the parameters for an operation.
- You can use the Headers tab to view and edit the headers for an operation.
- You can use the Body tab to view and edit the body Published messages being sent.
- You can use the History tab to view a history of all the Published and messages from a Subscriptions that have been sent and received.
SwaggerHub Explore is a powerful tool useful for easily exploring and interacting with Kafka Services. So, what are you waiting for? Get exploring today!