A ready-to-use, declarative, metadata-driven ETL framework for Azure Databricks. This unified solution supports both batch and streaming data processing, and is designed to reduce data engineering effort by up to 50% through built-in automation, consistency, and reusability.

Key Features:

• Declarative Configuration: Define ETL pipelines using metadata, not code.
• Dual-Mode Support: Seamlessly handles both batch and streaming workloads.
• Automation Built-In: Handles schema evolution, error handling, upserts, auditing, data validation, data lineage tracking and more.
• Reusable Components: Common transformations and validations abstracted for consistency.
• Optimized for Databricks: Leverages Delta Lake, Spark Structured Streaming, and Azure integrations.