TL;DR

Managing real-time data without schemas can lead to inconsistencies and governance risks. Pulsar schemas offer a structured way to enforce data consistency, support schema evolution, and ensure compliance. By implementing these schemas, organizations can achieve reliable and manageable data streaming operations.

Opening

Imagine a development team confidently deploying a new feature, only to find that a minor change breaks downstream applications, leading to costly data inconsistencies and delayed reports. This scenario is all too common when schemas are neglected in data streaming environments. As organizations increasingly rely on real-time data for decision-making, the need for robust schema management has never been more critical. Enter Pulsar schemas—a tool designed to transform how data is structured and governed in complex streaming systems.

What You'll Learn (Key Takeaways)

• Importance of Schemas – Schemas are critical for maintaining data consistency and preventing breaking changes in streaming systems, ensuring that data-driven applications remain reliable and efficient.
• Pulsar Schema Management – Learn how Pulsar schemas, which are integrated at the broker level, differ from external schema registries like Kafka’s and how they streamline schema registration and validation.
• Schema Compatibility Best Practices – Implement backward compatibility to enable safe schema evolution and prevent disruptions, ensuring seamless data flow in applications.
• Governance and Compliance Strategy – Utilize Pulsar schemas to enforce data governance policies, ensuring compliance and maintaining data quality across real-time applications.

Q&A Highlights

Q: Is it viable to use Pulsar schemas to validate data outside of Pulsar, such as historical data in a data lake?
A: While Pulsar schemas are designed to enforce structure during data transmission, it's prudent to validate data within each system to ensure consistency and reliability.

Q: What strategy do you recommend for scenarios with existing topics where consumers and producers are not using schemas?
A: If you have a clear understanding of the historical messages, applying a schema to the existing topic can work. However, for development setups or uncertain historical data, starting with a new topic might be advisable to avoid compatibility issues.