Kafka Best Practices in Confluent Cloud

Confluent Cloud is a fully managed Apache Kafka as a Service in the public cloud. Using Confluent Cloud allows developers to focus on building applications, microservices, and data pipelines for real-time streaming data integration and analytics rather than managing the underlying infrastructure.

But, just because we run Kafka for you, doesn’t mean your job is done!

In this white paper, we offer a complete guide to Kafka best practices to ensure you get the most out of Kafka by developing applications in a way that works seamlessly with Confluent Cloud.

• Best practices building Kafka applications in Confluent Cloud

• Architecture considerations for cloud success

• Fundamentals for developing client applications

• Enterprise-grade Kafka security, networking, monitoring, and metrics

We describe how to monitor your application performance, consumer lag, and throttling, using JMX and the Metrics API. You can optimize your Kafka applications for throughput, latency, durability, and availability, and to meet each of these goals, you will learn how to set producer configuration parameters such as:

Producer:

• replication.factor
• batch.size
• linger.ms
• compression.type
• acks
• buffer.memory
• enable.idempotence
• max.in.flight.requests.per.connection
• session.timeout.ms

And how to set consumer configuration parameters such as:

• fetch.min.bytes
• enable.auto.commit
• isolation.level

In addition to discussing integration with Confluent Cloud itself, we tackle the challenges and benefits of running software in Cloud environments. We'll also offer guidance to help you plan your way to a reliable, performant, and cost-effective deployment.

You can also use the promo code C50INTEG to get an additional $50 of free Confluent Cloud usage.*

Author

Yeva Byzek, Integration Architect, Confluent

Yeva Byzek is an integration architect at Confluent designing solutions and building demos for developers and operators of Apache Kafka. She has many years of experience validating and optimizing end-to-end solutions for distributed software systems and networks.