Ingesting Into Data Lake
aws/agent-toolkit-for-awsMoving data into an AWS data lake is now straightforward. You can import from many sources like S3 files, local uploads, or JDBC databases such as Oracle, SQL Server, and PostgreSQL. Cloud data warehouses like Redshift, Snowflake, and BigQuery are also supported.
The default target for your data is S3 Tables. If your environment has not adopted S3 Tables, you can use standard Iceberg on an existing bucket. The skill handles one-time loads, recurring pipelines, and migrations. It also triggers on many common commands like import, sync, migrate, and ETL.
This tool is built for data engineers who need a simple way to move data into a queryable data lake without manual scripting. It checks for existing connections and guides you step by step.
Add Ingesting Into Data Lake skill to your workflow
Global
mkdir -p ~/.claude/skills/ingesting-into-data-lakeProject
mkdir -p .claude/skills/ingesting-into-data-lakeSource Repository
More Data Science & ML Claude Code Skills
Explore Runlllllllama/rigorpilot-skills
Plan and rank quick deep learning exploration runs with clear boundaries
Run Trainlllllllama/rigorpilot-skills
Run deep learning training conservatively with structured logs and metrics
Env And Assets Bootstraplllllllama/rigorpilot-skills
Safe and careful setup of conda environments and asset paths for deep learning reproduction
Env And Assets Bootstraplllllllama/ai-paper-reproduction-skill
Prepare conda-first environment and asset paths for deep learning reproduction
Data Visualizationanthropics/knowledge-work-plugins
Pick the right chart, write clean Python code, and design for everyone
Powerbi Modelinggithub/awesome-copilot
Expert guidance for building optimized Power BI semantic models with star schemas and DAX
Power Bi Dax Optimizationgithub/awesome-copilot
Make your Power BI DAX formulas faster and easier to maintain
Bigquery Basicsgoogle/skills
Easily analyze massive datasets with SQL and built-in machine learning