Data Governance Overview¶
Confluent is incrementally adding Data Governance capabilities to Confluent Cloud, starting with Early Access Programs for:
Data Governance aims to manage the availability, integrity, and security of data used across the organization. Given the extraordinary explosion in terms of volume, variety, and speed of data to power the modern enterprise, it’s no surprise that the management of that data becomes paramount to every single company across the globe. Data Governance is now becoming mainstream, mandatory, and a main force behind the data everywhere movement.
What is Data Governance?¶
Data governance is the management of the quality, availability, usability, integrity, and security of data used throughout your organization. It provides a framework to manage information as an asset and guide data management activities across the organization. An effective data governance solution integrated with Kafka can provide discoverability, tracking, and security of your data throughout its lifecycle.
At its core, a data governance consists of three key areas:
Confluent is committed to help our customers with technology to help achieve their data governance goals for data in motion.
Why Data Governance at Confluent?¶
Use cases for governance of data in motion vary one organization or company to another, but normally fall under one or more of these categories:
- Compliance with data regulations
- Frictionless onboarding for users and teams employing Confluent solutions
- Knowledge sharing of data-in-motion
- Self-service streaming platform
How is Confluent addressing Data Governance?¶
Confluent is building a complete data governance solution to democratize data in motion in a “governed” way. This fully managed service by Confluent is focused on making data available to everyone across the organization, under the proper controls and restrictions.
|Data Quality||Schemas management and enforcement|
|Data standardization and integrity|
|Data Catalog||Metadata ingestion for all Confluent and Apache Kafka® artifacts|
|Data classifications through tagging|
|Metadata enrichment through key value pairs|
|Search and discovery of data through UI and API|
|Data Flow / Lineage||Visualize the data origin, transformations, and destination|