Data streaming solutions help organizations generate timely insights and take relevant actions to enhance their business growth. Apache Kafka and Google Cloud Pub/Sub are popular data streaming solutions. However, they have different architectures, latency ranges, data retention capacities, and scalability.
Let us understand these data systems through a detailed Apache Kafka vs Pub/Sub comparison. This will help you make an informed decision about which streaming service is suitable for your work objectives.
What is Apache Kafka?
Apache Kafka is an open-source and distributed event streaming service used for stream processing data pipelines in real-time. It allows you to capture data from sources such as databases or software applications and store and stream it on other data systems or analytics platforms. As a result, Kafka helps you ensure a continuous flow of data or information.
Kafka contains clients and servers that help you publish (write) and subscribe (read) a stream of events using the TCP network protocol. The client applications that allow you to publish events are called producers, and those that receive these events are called consumers. These components are decoupled from each other, imparting efficient streaming performance.
All the events are arranged and stored in topics like folders that store files. The servers forming the storage layer are called brokers. The topics are partitioned across multiple buckets in different Kafka brokers.
Key Features of Apache Kafka
- Data Replication: In Kafka, every topic is replicated to avoid data loss. This way, multiple brokers have copies of your data. You can also replicate data across multiple regions or data centers using Kafka.
- Low Latency: The platform facilitates low-latent operations through batching, partitioning, and compression capabilities. It helps speed up the delivery of event streams in high-performing data pipelines.
- Data Security: Kafka ensures data security through encryption, SSL or SASL authentication, and authorization using the access control lists mechanism. These security measures enable you to protect sensitive data during event streaming.
What is Pub/Sub?
Google Cloud Pub/Sub is a fully managed asynchronous communication service. It enables the exchange of information by decoupling message-producing services from message-receiving services. As a result, the functioning of one message sender is not affected by the functioning of the receiver. This facilitates isolation, resulting in high performance and scalability.
Publishers are client applications that send messages to Pub/Sub which then delivers them to the subscribers. These messages can contain various data types, including text, JSON objects, or binary data. After the publisher sends a message, an acknowledgment is sent to it by the Pub/Sub that it has received the message, assuring its delivery. The Pub/Sub then sends this message to the subscriber, who acknowledges that the message has been processed.
The messages sent by publishers are stored in Google Cloud storage and are deleted if any one of the subscribers acknowledges this message.
Key Features of Pub/Sub
- Push and Pull Delivery: Pub/Sub has a push and pull message delivery model. In the pull model, subscribers request messages. Alternatively, in the push model, the Pub/Sub server sends messages to subscribers as an HTTP request.
- Multiple Delivery Protocols: You can connect Pub/Sub topics to several endpoints, including message queues, HTTP servers, serverless functions, and email addresses. It ensures flexible and reliable communication.
- Filtering: It allows subscribers to deploy filters to receive only relevant messages that meet specific criteria, preventing unnecessary message processing.
Key Differences Between Apache Kafka vs Pub/Sub
You should keep in mind the following key differences between Apache Kafka vs Google Pub/Sub before using them:
Architecture
Kafka has a distributed server/client architecture with topics, brokers, producers, and consumers as its important components. The Kafka cluster can be made up of several servers that span across different regions or data centers. Producers, which are client applications, help you to create and send events in Kafka. These events are stored in topics that are partitioned to distribute the data across multiple brokers. You can then read them using the client applications called consumers.
On the other hand, the Pub/Sub’s architectural framework is divided into two parts: the data plane and the control plane. The data plane helps you manage messages moving between publishers and subscribers. The control plane enables you to assign publishers and subscribers to servers in the data plane.
The servers in the data plane are called forwarders, while those in the control plane are called routers. When you connect a client application to Pub/Sub, the router decides the data centers to which the client should connect based on the latency range. The routers help you distribute data load across the set of available forwarders.
Latency
Latency in Kafka is the time lag between an event created by a producer and consumed by a consumer. Kafka facilitates low latency and is suitable for building real-time data pipelines. Techniques such as reducing batch size, compression, and network configuration can further reduce the latency. To speed up the data processing, you can also increase the number of consumer instances and partitions.
Contrarily, latency in Pub/Sub is the amount of time required to deliver a message created by the publisher to the subscriber. The Pub/Sub has higher latency than Kafka as it runs on Google Cloud’s infrastructure, and delays may arise due to slow network speed. To reduce latency, you can optimize the network settings or send multiple messages in chunks from publishers to subscribers.
Data Replication and Durability
Apache Kafka offers a data replication feature that ensures data availability even during system failure. It allows you to replicate partitions across multiple brokers, and each partition consists of one leader and several replicas.
In addition, there are in-sync replicas (ISR) that are in sync with the leader and can take over when the leader fails. This ensures high availability and fault tolerance. You can also configure Kafka retention duration in order to replay or process past events.
Conversely, Pub/Sub supports message replication by facilitating data replication across multiple zones, ensuring data availability and durability. However, after you acknowledge messages in Pub/Sub, they become inaccessible for subscriber applications.
Acknowledging the message in Pub/Sub implies that the subscription has finished processing and should not be delivered to the subscriber again. To overcome this limitation, Pub/Sub offers the seek feature to change the acknowledgment status of messages in bulk. It allows you to replay the messages that have already been acknowledged.
Integration
You can integrate Kafka with various data systems such as Postgres, AWS S3, or Elasticsearch using Kafka Connect.
In contrast, you can easily integrate Pub/Sub with Google Cloud Services such as Dataflow or BigQuery. It can also be integrated with non-GCP data systems using APIs.
Costs
Kafka is open-source software and is used freely for event streaming purposes.
Alternatively, Pub/Sub has a pay-as-you-go pricing model with different charges for using throughput, storage, data transfer, and filtered message services.
Here is a tabular summary of Pub/Sub vs Kafka:
Factors to Consider When Choosing Apache Kafka or Pub/Sub
You should compare the following Kafka vs Pub/Sub factors before choosing any of them for data streaming:
Deployment
Apache Kafka is deployed on the cloud or locally on Windows, Linux, and macOS. Contrarily, Pub/Sub can be deployed only in the cloud.
Scalability
You can scale Kafka horizontally by adding more brokers in clusters to accommodate increased data load.
On the other hand, Pub/Sub is scaled horizontally by increasing the number of servers. You can also use a load-balancing mechanism in Pub/Sub to distribute network traffic to the nearest Google Cloud data center. This will help you store and manage increased data volume.
Use Cases
According to Paul Mac Farland, the senior vice president of Partner and Innovation Ecosystem at Confluent, 120,000 organizations globally use Kafka. It is widely used for stream processing, data integration, analytics, microservice communication, real-time fraud detection, and edge computing in IoT devices and social media applications.
Alternatively, Pub/Sub is used for real-time data streaming, asynchronous task processing, log aggregation, system monitoring, and alerting applications.
Streamlining Data Integration With Apache Kafka and Pub/Sub Using Airbyte
After evaluating Kafka and Pub/Sub features, you must integrate data from different sources into your chosen streaming system. Airbyte, a data movement platform, can help you collect and consolidate data from various sources in Kafka or Pub/Sub. It offers an extensive library of 400+ connectors to facilitate data integration. You can also load data from Kafka to Pub/Sub using Airbyte.
With suitable tools, you can cleanse and transform the integrated data. This standardized data can be used in various streaming or messaging applications.
Some important features of Airbyte are as follows:
- Multiple Options to Build Data Pipeline: Airbyte offers UI, API, Terraform Provider, and PyAirbyte to help you build highly functional data pipelines.
- Deployment Flexibility: Depending on your requirements, you can deploy Airbyte locally using the self-managed option. Alternatively, you can use the cloud-hosted or hybrid option.
- Log Monitoring: With Airbyte, you can monitor your ELT pipelines using various methods, such as Connection Logging, Airbyte Datadog Integration, and Airbyte Open Telemetry Integration.
- RAG Transformations: You can integrate Airbyte with LLM frameworks like LangChain or LlamaIndex to perform RAG transformations such as chunking. This enables you to improve the accuracy of LLM outcomes.
Conclusion
Apache Kafka and Pub/Sub are high-performing data streaming and messaging software. Whether you choose to use one of them depends on your specific use case and infrastructure.
This blog comprehensively explains Kafka vs Pub/Sub comparison to help you select a suitable data system. You can use Kafka if you prefer high-throughput and low latency, while you can choose Pub/Sub to utilize a fully managed Google Cloud ecosystem.