Setting the skill. One moment.
Analytics Skills
Skill 54 of 130
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…
5 minutes · 1,037 words · 19 sections
Install
npx skills add aws/agent-toolkit-for-aws --skill ingesting-into-data-lakenpx skills add aws/agent-toolkit-for-aws/plugin marketplace add aws/agent-toolkit-for-awsThe first command installs just this skill, by the name in its SKILL.md; the second installs the whole repository.
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.
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.
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.
aws sts get-caller-identityquerying-data-lake.| User says… | Source type | Reference |
|---|---|---|
| “upload my file”, “local CSV”, “move to S3” | Local file | local-upload.md (opens in a new tab) |
| “load from S3”, “import CSV/JSON/Parquet from s3://” | S3 files | s3-files.md (opens in a new tab) |
| “import from Oracle/Postgres/MySQL/SQL Server/Redshift/RDS/Aurora” | JDBC | jdbc-ingest.md (opens in a new tab) |
| “pull from Snowflake”, “Snowflake table to S3” | Snowflake | snowflake-ingest.md (opens in a new tab) |
| “import from BigQuery”, “GCP analytics to S3” | BigQuery | bigquery-ingest.md (opens in a new tab) |
| “export DynamoDB”, “DynamoDB to data lake” | DynamoDB | dynamodb-ingest.md (opens in a new tab) |
| “migrate Glue table”, “convert Hive to Iceberg” | Catalog migration | catalog-migration.md (opens in a new tab) |
If the user names Salesforce, ServiceNow, SAP, MongoDB, Kafka, or another SaaS/streaming source, decline – these are not supported in this release.
If the source table is referenced by a fuzzy or business name (“migrate our orders table”, “pull from the sales warehouse”), delegate to finding-data-lake-assets to resolve before proceeding.
For JDBC, Snowflake, and BigQuery sources, a Glue connection is required. Check:
aws glue get-connection --name <CONNECTION_NAME> --region <REGION>If the connection does not exist, stop and delegate to connecting-to-data-source to create and test it. Do not proceed with ingest until the connection is verified.
Local files, S3 files, DynamoDB, and catalog migration do not need a Glue connection.
You MUST ask the user (or suggest based on catalog inventory) before creating or writing to any table:
creating-data-lake-table)?Inventory-aware defaults:
If you have already run exploring-data-catalog or can quickly check, use what exists:
s3tablescatalog federated catalog and active table buckets: recommend S3 TablesDo not force S3 Tables on customers who haven’t adopted it. See iceberg-catalog-config-and-usage.md (opens in a new tab).
Delegations from this step:
creating-data-lake-tablefinding-data-lake-assetsexploring-data-catalogRead the source-specific reference and follow its phases. Each is self-contained with job templates, gotchas, and troubleshooting:
Common Glue 5.1 or higher job configuration and PySpark templates are shared in glue-job-config.md (opens in a new tab) and glue-job-scripts.md (opens in a new tab).
Run all three, do not skip:
See data-quality-validation.md (opens in a new tab).
For recurring pipelines, create a Glue Trigger with a cron schedule. See testing-and-scheduling.md (opens in a new tab). Simple single-step pipelines use Glue Triggers; multi-step with branching uses MWAA.
--target flag: Pre-fill the target format in Step 4--datalake-formats iceberg job argumentspark.sql.catalog.* config MUST go in --conf job arguments, never in spark.conf.set(). Glue 5.x throws AnalysisException: Cannot modify the value of a static config otherwise. See iceberg-catalog-config-and-usage.md (opens in a new tab) for correct catalog configs.warehouse parameter is required in S3 Tables catalog config. Without it Spark fails with “Cannot derive default warehouse location”.overwritePartitions() only replaces partitions present in the DataFrame – for full refresh with deletes, use createOrReplace()connecting-to-data-source; do not debug network/credentials in this skill| Error | Likely cause | Action |
|---|---|---|
| Access Denied on S3 | Missing IAM permissions | Check Glue role has s3:GetObject, s3:PutObject |
| Access Denied on S3 Tables | Missing s3tables:* permissions | Add S3 Tables inline policy to Glue role |
| CTAS timeout | Dataset too large for Athena | Switch to Glue ETL or batch with WHERE filters |
| JDBC connection timeout/auth failure | Connection-level issue | Delegate to connecting-to-data-source |
| Throughput exceeded (DynamoDB) | Read percent too high | Lower read.percent or use native export |
See error-handling.md (opens in a new tab) for the full catalog.
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.
The verbatim description from this skill’s front matter — the string an agent matches on to decide whether to load it.
skills/specialized-skills/analytics-skills/ingesting-into-data-lake/SKILL.mdmain, last pushed 24 September 2026.SKILL.md, not by matching a directory convention. 19 distinct layouts observed: plugins/aws-agents-for-devsecops/skills/*/SKILL.md, plugins/aws-agents/skills/*/SKILL.md, plugins/aws-core/skills/*/SKILL.md, skills/core-skills/*/SKILL.md, skills/specialized-skills/analytics-skills/*/SKILL.md, skills/specialized-skills/database-skills/*/SKILL.md, skills/specialized-skills/ec2-skills/*/SKILL.md, skills/specialized-skills/end-user-computing-skills/*/SKILL.md, skills/specialized-skills/messaging-and-streaming-skills/*/SKILL.md, skills/specialized-skills/migration-and-modernization-skills/*/SKILL.md, skills/specialized-skills/networking-and-content-delivery-skills/*/SKILL.md, skills/specialized-skills/operations-skills/*/SKILL.md, skills/specialized-skills/quantum-computing-skills/*/SKILL.md, skills/specialized-skills/resilience-skills/*/SKILL.md, skills/specialized-skills/security-and-identity-skills/*/SKILL.md, skills/specialized-skills/serverless-skills/*/SKILL.md, skills/specialized-skills/storage-skills/*/SKILL.md, skills/specialized-skills/system-table-skills/*/SKILL.md, skills/specialized-skills/web-and-mobile-development/*/SKILL.md.h1 and no skipped levels:.claude-plugin/marketplace.json by Amazon Web Services, declaring 4 plugins. It is read for editorial metadata only — never as the skill index, which is always the repository tree./aws/agent-toolkit-for-aws.md, and each skill at its own .md URL.25 files · 185 KB
Everything this skill ships beside its prose. All of it is set here, as subchapters of skill 54.
Documentation the agent loads on demand, rather than up front.