Systems | Development | Analytics | API | Testing

Operate the data streaming platform efficiently by focusing on prevention, monitoring, and mitigation for maximum uptime. Handle potential data loss risks like software bugs, operator errors, and misconfigurations proactively. Leverage GitOps for real-time alerts and remediation. Adjust capacity to meet demand and monitor costs with Confluent Cloud's pay-as-you-go model. Prepare for growth with documentation and minimal governance.

Early automation saves time and money. GitOps improves CI/CD pipeline, enhancing operations & traceability. Learn to use GitOps for data streaming platforms & streaming applications with Apache Kafka and Confluent Cloud.

Explore the resilience of Kafka, understand the implications of datacenter disruptions, and mitigate data loss impacts. Learn to scale with Confluent Cloud, cluster and schema linking, and how to use an active/passive disaster recovery pattern for business continuity.

Streaming platforms need key capabilities for smooth operations: data ingestion, development experience, management, security, performance, and maintenance. Self-managed platforms like Apache Kafka can meet these needs, but can be costly and require intensive maintenance. On the other hand, Confluent Cloud offers fully-managed services with features like scalable performance, auto-balancing, tiered storage, and enhanced security and resiliency. It provides systematic updates and maintenance, freeing users from infrastructure concerns. Confluent Cloud streamlines creation of a global, well-governed data streaming platform.

"Over the last few years, DISH Wireless has turned to AWS partners like Confluent to build an entirely new type of telecommunication infrastructure—a cloud-native network built to empower developers. Discover how data streaming allows DISH Wireless to:— Deliver data products that turn network data into business value for customers— Harness massive volumes of data to facilitate the future of app communications— Seamlessly connect apps and devices across hybrid cloud environments.

Learn about the data requirements including data quality, modeling, security, and more, as well as which to prioritize when building real-time data streaming platform and applications.

Getting started with Apache Flink® can be challenging. In this short video, Wade Waldron will walk you through a complete example of how to consume Apache Kafka® messages using Apache Flink and Java.

Learn to transition stream processing system from POC to production. Secure app and platform, handle spikes, outages, errors. Automate deployment to focus on value-add app building.

But their batch-based processing systems and lack of access to self-service data was slowing them down, making it difficult to harness real-time data and create the targeted marketing campaigns they needed to reach their customers..