Apache Griffin

Apache Griffin is an open-source data quality solution for big data environments, supporting both batch and streaming modes. It provides a unified framework for defining, measuring, and reporting data quality metrics across diverse data sources. Griffin enables organizations to build trusted data assets by offering a domain-driven model and a flexible DSL for expressing quality rules. It is designed to integrate with modern big data stacks, including Spark, Hive, Kafka, and Hadoop.

Features

• Supports batch and streaming data quality measurement.
• Domain-specific language (DSL) for defining custom quality rules.
• Built-in models for accuracy, completeness, timeliness, and profiling.
• Integration with Spark, Hive, Kafka, Hadoop, and Elasticsearch.
• Configurable sinks for outputting metrics to console, HDFS, or Elasticsearch.
• Front-end interface for onboarding new data quality requirements.
• Extensible architecture for custom rule definitions and connectors.
• Real-time and scheduled execution of quality checks.

Capabilities

• Ingests data from various sources and applies quality checks dynamically.
• Measures data quality based on user-defined rules and outputs metrics.
• Supports checkpointing and persistence for streaming data validation.
• Enables comparison between source and target datasets in batch and stream.
• Provides APIs and configuration files for flexible deployment.
• Operates in distributed environments using Spark and YARN.
• Handles structured data in Hive tables and Kafka topics.

Benefits

• Improves trust in data assets through continuous quality monitoring.
• Reduces manual effort in validating data across systems.
• Enhances decision-making with reliable and timely data metrics.
• Scales with enterprise data infrastructure and supports real-time use cases.
• Encourages collaboration between data engineers and analysts.
• Open-source and governed by the Apache Software Foundation.