Apache Griffin

Apache Griffin is an open-source data quality solution (data quality management) for big data environments, developed under The Apache Software Foundation.

• End-to-end data quality solution for big data platforms (data quality management).
• Supports batch and streaming data quality measurements with unified model and interfaces (data processing).
• Defines data quality metrics through a declarative DSL and configurable rules (data quality rules engine).
• Provides measurement, reporting, and dashboarding for data quality statistics (data observability).
• Integrates with big data ecosystems such as Hadoop and Spark-based environments (big data ecosystem integration).

More About Apache Griffin

Apache Griffin is an open-source data quality solution (data quality management) designed for big data environments where enterprises need to measure, monitor, and manage the quality of data stored and processed on distributed platforms. It addresses the problem of defining data quality rules in a reusable way, executing them at scale on batch and streaming pipelines, and surfacing the resulting metrics through reporting and dashboards so that technical and business teams can track data reliability over time.

The project provides a unified process to define and compute data quality metrics (data quality rules engine). Users describe quality requirements using a declarative model, often through domain-specific rule definitions that can express checks such as completeness, accuracy, timeliness, and consistency. Apache Griffin then translates these rules into executable jobs that run on big data processing engines. This separation between rule definition and execution allows organizations to standardize data quality policies while adapting execution to their existing infrastructure.

Apache Griffin supports both batch and streaming measurements (data processing). In batch scenarios, data quality jobs run against large datasets stored in data lakes, warehouses, or Hadoop-based storage. In streaming scenarios, Griffin evaluates data quality on continuous data flows, enabling monitoring of real-time pipelines. The framework computes metrics and persists results, which can be queried and visualized to understand quality levels, trends, and rule violations across datasets and time windows.

The project provides components for measurement, reporting, and dashboarding (data observability). After executing quality checks, Apache Griffin stores metrics and detailed records for passed and failed checks. Dashboards and visual tools consume this information to present data quality statistics to stakeholders such as data engineers, data stewards, and platform operators. These capabilities support activities like Root Cause Analysis (RCA) of data issues, auditing of pipeline behavior, and validation of upstream changes.

Apache Griffin is built to integrate with big data ecosystems (big data ecosystem integration), including environments based on Apache Hadoop and Apache Spark that are common in enterprise data platforms. It can run on cluster computing frameworks to process large-scale datasets and streaming sources, leveraging existing storage and compute resources rather than requiring a separate proprietary engine. Configuration-driven integration enables organizations to connect Griffin to their data sources, sinks, and metadata systems according to their architecture.

From an enterprise architecture perspective, Apache Griffin fits into the data governance and data platform stack as a data quality and observability layer (data governance). It supports the implementation of standardized data quality policies across domains, helps enforce compliance requirements related to data accuracy and completeness, and gives platform teams operational visibility into data pipeline behavior. In directory and taxonomy terms, it can be categorized under data quality management, big data processing tools, and data observability for analytics and data platforms.