The Streaming Data Quality Guidebook

Learn Stream Governance best practices to protect downstream systems and applications from poor data quality with this comprehensive guide.

Maintaining high data quality has always been essential but now with the rise of AI/ML, the stakes are higher than ever. Poor data quality leads to “garbage in, garbage out”, with disastrous downstream impact, from costly outages and system failures to inaccurate reports and poor decision-making.

Using Apache Kafka® and other data streaming technologies can amplify data quality issues, as bad data can spread faster and into more places. With event-driven systems, bad data can easily cause an entire ecosystem of applications and services to break. So how can your organization take advantage of the benefits of streaming architectures while giving data teams a sustainable and scalable way to maintain high data quality?

This Data Quality Guidebook introduces best practices for data streaming with governance in mind. You’ll learn about common categories of bad data to watch out for and how to put preventative measures in place. The guide includes actionable steps you can take ensure high data quality and covers topics like establishing data contracts, creating data products, and embracing a decentralized data mesh approach to data ownership and management across an organization. Whether you’re new to data streaming or a seasoned guru, we’ll show you how Confluent’s Stream Governance suite makes this all seamless and easy to implement.

Download now to learn how to:

• Identify common schema issues and causes for invalid values
• Define and share data contracts with Schema Registry
• Discover, understand, and trust your data streams in a decentralized model