Autonomy is key in building a sustainable and motivated team, and this core principle also applies to DevOps. Building self-serve Apache Kafka® and Confluent Platform deployments require a streamlined process with unrestricted tools—a centralized processing tool that allows teams in large or mid-sized organizations to automate infrastructure changes while ensuring shared standards are met. With more than 15 years of engineering and technology consulting experience, Pere Urbón-Bayes (Senior Solution Architect, Professional Services, Confluent) built an open source solution—JulieOps—to enable a self-serve Kafka platform as a service with data governance.
JulieOps is one of the first solutions available to realize self-service for Kafka and Confluent with automation. Development, operations, security teams often face hurdles when deploying Kafka. How can a user request the topics that they need for their applications? How can the operations team ensure compliance and role-based access controls? How can schemas be standardized and structured across environments? Manual processes can be cumbersome with long cycle times. Automation reduces unnecessary interactions and shortens processing time, enabling teams to be more agile and autonomous in solving problems from a localized team level.
Similar to Terraform, JulieOps is declarative. It's a centralized agent that uses the GitOps philosophy, focusing on a developer-centric experience with tools that developers are already familiar with, to provide abstractions to each product personas. All changes are documented and approved within the change management process to streamline deployments with timely and effective audits, as well as ensure security and compliance across environments.
The implementation of a central software agent, such as JulieOps, helps you automate the management of topics, configuration, access controls, Confluent Schema Registry, and more within Kafka. It’s multi tenant out of the box and supports on-premises clusters and the cloud with CI/CD practices.
Tim and Pere also discuss the steps necessary to build a self-service Kafka with an automatic Jenkins process that will empower development teams to be autonomous.
EPISODE LINKS
Apache Kafka 3.5 - Kafka Core, Connect, Streams, & Client Updates
A Special Announcement from Streaming Audio
How to use Data Contracts for Long-Term Schema Management
How to use Python with Apache Kafka
Next-Gen Data Modeling, Integrity, and Governance with YODA
Migrate Your Kafka Cluster with Minimal Downtime
Real-Time Data Transformation and Analytics with dbt Labs
What is the Future of Streaming Data?
What can Apache Kafka Developers learn from Online Gaming?
Apache Kafka 3.4 - New Features & Improvements
How to use OpenTelemetry to Trace and Monitor Apache Kafka Systems
What is Data Democratization and Why is it Important?
Git for Data: Managing Data like Code with lakeFS
Using Kafka-Leader-Election to Improve Scalability and Performance
Real-Time Machine Learning and Smarter AI with Data Streaming
The Present and Future of Stream Processing
Top 6 Worst Apache Kafka JIRA Bugs
Learn How Stream-Processing Works The Simplest Way Possible
Building and Designing Events and Event Streams with Apache Kafka
Rethinking Apache Kafka Security and Account Management
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