Individual skill
ingesting-into-data-lake
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.
skills/specialized-skills/analytics-skills/ingesting-into-data-lake/SKILL.md
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- data Derived category
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- data-databases Derived category
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SKILL.mdcore · 11,074 bytesSKILL.mdcore · 11,074 bytesSKILL.mdcore · 11,074 bytesSKILL.mdcore · 11,074 bytesSKILL.mdcore · 11,074 bytesSKILL.mdcore · 11,074 bytesSKILL.mdcore · 11,074 bytesSKILL.mdcore · 11,074 bytesreferences/athena-loading.mddoc · 2,831 bytesreferences/bigquery-ingest.mddoc · 3,794 bytesreferences/catalog-migration.mddoc · 7,038 bytesreferences/ctas-patterns.mddoc · 3,026 bytesreferences/data-quality-validation.mddoc · 12,451 bytesreferences/dynamodb-ingest.mddoc · 7,333 bytesreferences/error-handling.mddoc · 12,626 bytesreferences/format-specific-loading.mddoc · 13,153 bytesreferences/glue-etl-migration.mddoc · 4,645 bytesreferences/glue-job-config.mddoc · 9,625 bytesreferences/glue-job-scripts.mddoc · 9,813 bytesreferences/iceberg-catalog-config-and-usage.mddoc · 8,049 bytesreferences/incremental-loading.mddoc · 13,381 bytesreferences/jdbc-ingest.mddoc · 5,790 bytesreferences/jdbc-performance.mddoc · 10,546 bytesreferences/jdbc-schema-discovery.mddoc · 13,045 bytesreferences/local-upload.mddoc · 4,625 bytesreferences/migration-troubleshooting.mddoc · 2,640 bytesreferences/migration-validation.mddoc · 2,772 bytesreferences/s3-files.mddoc · 5,879 bytesreferences/schema-evolution.mddoc · 9,497 bytesreferences/snowflake-ingest.mddoc · 3,729 bytesreferences/testing-and-scheduling.mddoc · 13,961 bytesreferences/type-transformations.mddoc · 7,979 bytesreferences/upload-options.mddoc · 1,053 bytes
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# Ingest into Data Lake
Move data from a source into a queryable table in the data lake. This skill assumes the source connection (if one is needed) already exists. For Glue connection setup or troubleshooting, delegate to `connecting-to-data-source`.
## Philosophy
**Default to S3 Tables unless the environment says otherwise.** S3 Tables is the recommended target for new data lake work. If the user's catalog inventory shows they haven't adopted S3 Tables, recommend standard Iceberg on their existing general-purpose bucket instead of forcing them to change posture.
## Common Tasks
You MUST execute commands using AWS MCP server tools when connected -- they provide validation, sandboxed execution, and audit logging. Fall back to AWS CLI only if MCP is unavailable. You MUST explain each step before executing.
## Workflow
