ingesting-into-data-lake

ingesting-into-data-lake is a Data pipelines & ETL skill for AI agents, published by aws in the agent-toolkit-for-aws repository on GitHub. Skillspace rates ingesting-into-data-lake D (69/100) and it passed the safety scan; the rating is based on static analysis only.

Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue catalog tables (migration). Default target is S3 Tables; standard Iceberg on a general purpose bucket is supported where S3 Tables is not adopted. Handles one-time loads, recurring pipelines, migrations. Triggers on: import data, load data, ingest, sync database, migrate table, move data to AWS, set up pipeline, ETL, pull from Snowflake, query BigQuery into S3, export DynamoDB, CTAS, convert to Iceberg. Do NOT use for setting up or troubleshooting Glue connections (use connecting-to-data-source), creating empty tables (use creating-data-lake-table), running queries (use querying-data-lake), finding tables by fuzzy name (use finding-data-lake-assets), catalog audit (use exploring-data-catalog), or SaaS platforms like Salesforce, ServiceNow, SAP, MongoDB, Kafka.

Grade

D

Quality

69 / 100

Safety

Passed

GitHub stars

1,855

Unproven: rated by static analysis only, not yet compared head-to-head.

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