Undraleu is CoeurData's enterprise code quality platform for ETL and data pipelines. It analyzes artifacts from platforms such as PowerCenter, IDMC, Azure Data Factory (ADF), AWS Glue, Databricks, Talend, DataStage, SSIS, DBT, and PySpark. It enforces best practices, detects engineering risks, integrates with CI/CD, and generates structured evidence for engineering, governance, and audit teams.
Undraleu supports PowerCenter, IDMC, ADF, AWS Glue, Databricks, Talend, DataStage, SSIS, DBT, and PySpark — with additional platforms added as needed.
No. Undraleu automates repetitive checks so engineers can focus on logic, design, and architecture. It improves consistency and reduces manual effort.
No. Undraleu is not a migration engine. It does not convert code. It provides deep insights into refactoring needs and code quality gaps that help modernization programs run smoothly.
ETL code quality ensures data pipelines are reliable, maintainable, efficient, and compliant. Poor quality increases incidents, delays modernization, and drives up operational cost. Strong engineering discipline is essential as cloud ecosystems become more complex.
Undraleu performs deep static analysis on pipeline artifacts and applies a unified rule set across all supported tools. This lets organizations enforce consistent engineering discipline regardless of platform.
Undraleu evaluates code against ten core categories: Performance, Housekeeping, Tool Abuse, Testability, Functionality Gaps, Maintainability, Impact Analysis, Documentation, Supportability, and Control Evidence.
Excel checklists are slow, manual, and inconsistent. They do not scale across large, multi-cloud architectures and cannot integrate with CI/CD or provide trend analysis. Undraleu replaces them with automated, standardized, repeatable validation.
Yes. By enforcing engineering discipline and catching issues early, Undraleu reduces pipeline failures, defects, and rework.
Yes. Undraleu identifies refactoring needs and enforces standards before and during migration, reducing surprises and improving migration outcomes.
Undraleu can be deployed on-premise or in your cloud environment. It runs centrally and does not require installation on developer machines.
No. Undraleu analyzes code artifacts only and does not connect to production systems.
Yes. Undraleu integrates with GitHub, GitLab, Jenkins, Azure DevOps, Bitbucket, and Azure Repos to run automated shift-left validation.
Undraleu produces structured, repeatable evidence aligned to frameworks like FFIEC, NIST, and HIPAA. It eliminates the need for screenshots and spreadsheets and ensures defensible, consistent quality validation.
Yes. Undraleu benchmarks vendor code quality against internal standards, enabling enforcement of quality-based SLAs.
Undraleu is used by: Developers & data engineers, Engineering leads & platform owners, Internal audit & ITGC teams, Vendor oversight & governance groups, and Modernization & cloud migration teams.
Developers catch issues early, learn best practices faster, and reduce rework. Undraleu acts like an always-available SME that provides immediate feedback.
Leaders get visibility across teams, platforms, vendors, and codebases — helping them identify training needs and measure engineering maturity.
Yes. CoeurData offers tailored evaluations so organizations can measure impact using their own pipelines and platforms.
Pricing depends on the size of your codebase, supported platforms, and deployment model. Enterprise and multi-year contracts are available.
CoeurData provides onboarding, rule customization, upgrades, support, and guidance to help teams adopt engineering discipline efficiently.
Undraleu works like a spell-checker for your data pipelines. It catches issues early and helps engineers build cleaner, safer, more reliable pipelines.
Undraleu is an automated code quality platform for ETL and data pipelines that enforces engineering discipline, integrates with CI/CD, supports governance, and improves quality across PowerCenter, IDMC, ADF, Glue, Databricks, Talend, DataStage, SSIS, DBT, and PySpark.
Undraleu analyzes ETL/ELT pipeline artifacts, applies a unified engineering rule taxonomy, and produces structured, audit-ready insights. It helps organizations reduce technical debt and strengthen engineering maturity at scale.