Precisely Data Quality for Kafka

As the volume and velocity of data continues to grow, streaming data represents a paradigm shift that introduces new data quality challenges.

The speed of business today demands that organizations enable access to real-time data. This has given rise to event-driven architectures. This fundamental shift from batch processing to streaming data provides new opportunities for increased data delivery reliability, more nimble reactivity
and faster business insights. But it also presents new risks to data integrity as streaming sources, data volumes and architectural complexity continue to grow, affecting data quality Among the many streaming data options, distributed streaming platform Apache Kafka has become a top choice
for real-time data communication.

Kafka is an agile, high through put, low latency option for managing data in motion, but it cannot ensure the reliability and accuracy of real-time
data streams The options that exist to ensure data quality of data in Kafka streams is often limited to very technical options that require a robust skill set and are limited in their ability to apply business rules to the data. Business teams expect the same logic to be applied to real-time data that would be applied in a batch mode.

Read more about Precisely Data Quality for Kafka and how it delivers trust in streaming data with producer-to-consumer validation and reduces enterprise risk by ensuring that streaming data is validated, reconciled, and timely, to produce meaningful and reliable data insights.