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Adele
Migrate your SQL ETL jobs to any cloud platform in seconds

What is Adele?

ADELE is an advanced AI platform designed to streamline the migration of SQL ETL (Extract, Transform, Load) jobs to cloud environments. It automates the extraction of metadata from legacy systems, including enterprise ETL tools and hard-coded PL/SQL procedures, and generates optimized data pipelines for target cloud platforms. The platform supports SQL-like languages parsing, enabling users to write queries that are automatically converted into cloud-native data pipelines.

The tool offers extensive data lineage and impact analysis capabilities, comparable to specialized solutions, with navigation from high-level entity mapping down to attribute-level transformations. It serves as a comprehensive metadata and documentation resource, even after migration, and provides guided migration accelerators that are target-platform agnostic, with strong support for Azure Data Factory. ADELE eliminates human errors through unified pipeline generation and can automate 80–90% of legacy data application logic harvesting and migration.

Features

  • Reliable metadata harvesting: Extracts knowledge from enterprise ETL tools and hard-coded PL/SQL procedures
  • Automated generation of target data pipelines: Reimplements data pipelines in target platforms to save effort and reduce errors
  • SQL-like languages parsing: Converts SQL-based queries into cloud-native data pipelines
  • Extensive data lineage and impact analysis: Provides navigation from entity-level to attribute-level mapping with business-user-friendly visualization
  • Target platform agnostic migration accelerator: Supports migration to various cloud platforms with strong Azure Data Factory capabilities
  • Automated harvesting of metadata: Covers legacy platforms and hard-coded data pipelines like PL/SQL and T-SQL
  • Elimination of human error: Uses generalized and unified generation of data pipelines
  • Automated documentation: Serves as a metadata and documentation tool post-migration

Use Cases

  • Migrating legacy SQL ETL jobs to cloud platforms like Azure
  • Automating metadata extraction from enterprise ETL tools
  • Generating data pipelines for greenfield data applications development
  • Conducting data lineage analysis and impact assessment
  • Documenting transformation logic for data solutions
  • Optimizing data migration processes for data warehouses
  • Reducing manual effort in re-architecting legacy data systems

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