TR Raveendra
Community@TRRaveendra · Hyderabad
TR Raveendra. Big Data Cloud Data Engineer
Agent Skills by TR Raveendra
Showing 19 vetted skills indexed across 1 GitHub repositories.
TRRaveendrainformatica-to-pyspark-migration
Convert Informatica PowerCenter and IDMC mappings into PySpark on Databricks.
TRRaveendramssql-to-pyspark-migration
Convert MSSQL T-SQL stored procedures into Databricks PySpark and Spark SQL code.
TRRaveendradatabricks-data-engineering
Implements Databricks data engineering pipelines using medallion architecture, Delta Lake, and Unity Catalog patterns.
TRRaveendrasdlc-agent-02-requirement-validation
Validates requirement packages and generates clarification questions as structured JSON artifacts on Databricks.
TRRaveendrasdlc-agent-03-business-understanding
Generates a Business Understanding Document from validated requirements in a Databricks SDLC pipeline.
TRRaveendrasdlc-agent-06-data-model
Generates Bronze/Silver/Gold data models and Delta DDL scripts from mapping documents on Databricks.
TRRaveendrasdlc-agent-11-unit-test
Generates and executes PyTest suites with coverage reports for reviewed PySpark and SQL code.
TRRaveendrasdlc-agent-08-sql
Generates SQL views and procedures for Databricks Gold-layer consumption as pipeline stage 8.
TRRaveendrasdlc-agent-04-metadata-discovery
Discovers Unity Catalog metadata and builds data dictionaries for Databricks SDLC pipelines.
TRRaveendrasdlc-agent-10-code-review
Reviews PySpark and SQL code against Databricks standards and produces review reports with revised code.
TRRaveendrasdlc-agent-15-production-monitoring
Generates Databricks monitoring dashboards and alert rules from deployment packages.
TRRaveendrasdlc-agent-09-data-quality
Generates data quality rules, expectations, and reports from Databricks pipeline artifacts.
TRRaveendrasdlc-agent-13-documentation
Generates README, architecture, deployment, and runbook documents from upstream Databricks pipeline artifacts.
TRRaveendrasdlc-agent-05-mapping-document
Generates source-to-target mapping documents from metadata discovery packages on Databricks.
TRRaveendrasdlc-agent-07-pyspark-development
Generates production PySpark notebooks from data models and DDL on Databricks.
TRRaveendrasdlc-agent-12-testing
Runs integration, regression, and performance tests on Databricks and writes a test report artifact.
TRRaveendrasdlc-agent-01-jira-requirement
Fetches Jira tickets and produces validated requirement artifacts for a Databricks SDLC pipeline.
TRRaveendrasdlc-agent-14-deployment
Generates Databricks Asset Bundles and CI/CD deployment packages from reviewed pipeline code.
TRRaveendramssql-to-databricks-migration
Convert T-SQL stored procedures and scripts into Databricks PySpark and Spark SQL notebooks.
Frequently Asked Questions About TR Raveendra
FAQPage SchemaWhat tasks can I accomplish with TR Raveendra's skills?▼
You can migrate Informatica PowerCenter/IDMC mappings and MSSQL T-SQL stored procedures to PySpark and Spark SQL, build medallion bronze/silver/gold pipelines with CDC, SCD, and Delta Lake features, and execute a 15-agent SDLC chain from Jira requirement through deployment and production monitoring.
Who are these skills designed for?▼
Big data and cloud data engineers modernizing legacy ETL platforms onto Databricks. They suit teams converting Informatica or SQL Server workloads, implementing Unity Catalog governance, and organizations wanting an artifact-driven multi-agent SDLC pipeline on the lakehouse.
How do the SDLC agent skills operate at runtime?▼
Each agent reads its upstream input artifact from /Volumes/{catalog}/state/artifacts/{ticket}/, calls the Databricks coding backend to perform the work, validates output against its JSON contract, and writes the resulting artifact back to the lakehouse for the next agent in the chain.
What Databricks engineering capabilities are covered?▼
The databricks-data-engineering skill covers medallion architecture, Auto Loader and structured streaming, Delta Lake time travel, MERGE, CDF, OPTIMIZE and VACUUM, Unity Catalog volumes and external locations, plus performance tuning via AQE, Photon, and liquid clustering.
What inputs are required for the migration skills?▼
The Informatica skill needs a PowerCenter XML export, IDMC asset JSON, or mapping description referencing constructs like Source Qualifier, Lookup, Router, or Update Strategy. The MSSQL skills need T-SQL stored procedures, scripts, or queries to convert into PySpark or Spark SQL equivalents.