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This skill provides a structured workflow for importing data from various sources—including JDBC databases, cloud warehouses like Snowflake and BigQuery, DynamoDB, and S3 files—into an AWS data lake. It prioritizes modern S3 Tables or standard Iceberg formats, ensuring data is queryable and well-structured. The skill guides users through connection verification, target configuration, and validation, while delegating complex setup tasks to specialized sub-skills.
What it does
- Imports data from JDBC databases, cloud warehouses, and S3 into a data lake
- Supports one-time loads, recurring ETL pipelines, and catalog migrations
- Configures target tables using S3 Tables or standard Iceberg formats
- Validates data integrity via row counts, null checks, and sample verification
- Automates scheduling for recurring ingestion tasks using Glue triggers
- Provides clear delegation paths for connection setup and table creation
When to use it
- When you need to move data from an external database or cloud service into your AWS data lake
- When setting up a new recurring ETL pipeline for data synchronization
- When migrating existing Hive-style tables to modern Iceberg formats
- When you need to perform a one-time bulk load of files from S3 or local storage
Advantages
- Standardizes ingestion patterns across multiple source types
- Ensures data quality through built-in validation steps
- Promotes modern data lake practices by defaulting to S3 Tables and Iceberg
- Reduces configuration errors by enforcing connection verification before execution
Usage examples
- “Import the customers table from our Oracle database into the data lake.”
- The agent verifies the existing JDBC connection, confirms the target S3 Tables configuration, and initiates a Glue ETL job to migrate the data.
- “Set up a recurring pipeline to pull data from BigQuery into S3.”
- The agent configures a Glue job with the necessary BigQuery connection, sets up the target Iceberg table, and creates a Glue trigger for scheduled execution.
- “Convert our existing Hive tables to Iceberg.”
- The agent performs a catalog migration, updating the table metadata to Iceberg format while maintaining data accessibility.
- “Load these local CSV files into the data lake.”
- The agent guides the user through the local upload process and maps the data into a queryable S3 Tables format.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/aws/ingesting-into-data-lake/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip ingesting-into-data-lake.zip -d .agents/skills
Documentation by AWS
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
1. Verify Dependencies and Context
You MUST check whether AWS MCP tools or AWS CLI are available and inform the user if missing
You MUST confirm target AWS region and verify credentials with
aws sts get-caller-identityFor SageMaker Unified Studio project roles, note that target tables and connections may be scoped to the project. See the caller ARN detection pattern in
querying-data-lake.
2. Classify the Source
| User says... | Source type | Reference |
|---|---|---|
| "upload my file", "local CSV", "move to S3" | Local file | local-upload.md |
| "load from S3", "import CSV/JSON/Parquet from s3://" | S3 files | s3-files.md |
| "import from Oracle/Postgres/MySQL/SQL Server/Redshift/RDS/Aurora" | JDBC | jdbc-ingest.md |
| "pull from Snowflake", "Snowflake table to S3" | Snowflake | snowflake-ingest.md |
| "import from BigQuery", "GCP analytics to S3" | BigQuery | bigquery-ingest.md |
| "export DynamoDB", "DynamoDB to data lake" | DynamoDB | dynamodb-ingest.md |
| "migrate Glue table", "convert Hive to Iceberg" | Catalog migration | catalog-migration.md |
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.
3. Confirm Connection Exists (if applicable)
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.
4. Clarify the Target
You MUST ask the user (or suggest based on catalog inventory) before creating or writing to any table:
Database/namespace: Does a specific target database exist? Or should one be created?
Table: Existing table (append/merge) or new table (delegate to
creating-data-lake-table)?Format: S3 Tables (default), standard Iceberg, or raw Parquet?
Inventory-aware defaults:
If you have already run exploring-data-catalog or can quickly check, use what exists:
Account has an
s3tablescatalogfederated catalog and active table buckets: recommend S3 TablesAccount has general-purpose buckets with Iceberg tables and no S3 Tables usage: recommend standard Iceberg on their existing bucket
Account uses Parquet/ORC on S3 without Iceberg metadata: ask whether to adopt Iceberg now (recommend yes) or continue with raw files
Do not force S3 Tables on customers who haven't adopted it. See iceberg-catalog-config-and-usage.md.
Delegations from this step:
Target table doesn't exist ->
creating-data-lake-tableTarget database named by fuzzy term ->
finding-data-lake-assetsUser doesn't know what exists ->
exploring-data-catalog
5. Execute Source Workflow
Read the source-specific reference and follow its phases. Each is self-contained with job templates, gotchas, and troubleshooting:
Local / S3 / JDBC / Snowflake / BigQuery / DynamoDB / catalog migration -- one reference per source
Common Glue 5.1 or higher job configuration and PySpark templates are shared in glue-job-config.md and glue-job-scripts.md.
6. Validate
Run all three, do not skip:
Row count matches expected (source vs target)
Null check on critical columns
Spot-check 3-5 sample rows
See data-quality-validation.md.
7. Schedule (if recurring)
For recurring pipelines, create a Glue Trigger with a cron schedule. See testing-and-scheduling.md. Simple single-step pipelines use Glue Triggers; multi-step with branching uses MWAA.
Argument Routing
S3 path only: Infer one-time load, start Step 2 with S3 files
Connection name: Start Step 3 with the named connection
Table name: Start Step 4, ask whether this is source or target
--targetflag: Pre-fill the target format in Step 4No args: Walk through interactively
Gotchas
S3 Tables requires Glue 5.1 or higher and
--datalake-formats icebergjob argumentAll
spark.sql.catalog.*config MUST go in--confjob arguments, never inspark.conf.set(). Glue 5.x throwsAnalysisException: Cannot modify the value of a static configotherwise. See iceberg-catalog-config-and-usage.md for correct catalog configs.The
warehouseparameter is required in S3 Tables catalog config. Without it Spark fails with "Cannot derive default warehouse location".Table and column names in S3 Tables MUST be all lowercase
overwritePartitions()only replaces partitions present in the DataFrame -- for full refresh with deletes, usecreateOrReplace()Standard Iceberg targets MUST include a LOCATION clause; S3 Tables MUST NOT
DynamoDB does not need a Glue connection -- do not attempt to create one
Connection failures during ingest delegate back to
connecting-to-data-source; do not debug network/credentials in this skillFor target tables in SageMaker Unified Studio projects, ensure the project role has write access to the target namespace before the Glue job runs
Troubleshooting
| 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 for the full catalog.
References
Source-specific
local-upload.md -- Local files
s3-files.md -- S3 files (CSV, JSON, Parquet, Avro, ORC)
jdbc-ingest.md -- Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora, Redshift
snowflake-ingest.md -- Snowflake
bigquery-ingest.md -- BigQuery
dynamodb-ingest.md -- DynamoDB (export and Glue direct read)
catalog-migration.md -- Existing Glue catalog tables (Hive, self-managed Iceberg)
Cross-cutting
iceberg-catalog-config-and-usage.md -- S3 Tables, standard Iceberg, raw files: catalog config, engine access patterns
glue-job-config.md -- Job sizing, monitoring, retry
glue-job-scripts.md -- PySpark templates (append, upsert, custom SQL, full refresh)
incremental-loading.md -- Watermark strategies
testing-and-scheduling.md -- Glue Triggers, MWAA
data-quality-validation.md -- Row counts, null checks, Glue Data Quality
schema-evolution.md -- ALTER TABLE ADD COLUMNS, nested JSON
type-transformations.md -- Type conflict resolution
format-specific-loading.md -- CSV/JSON/Parquet/Avro/ORC specifics
athena-loading.md -- Athena INSERT INTO as simple-load fallback
error-handling.md -- Ingest errors (connection errors delegate to connecting-to-data-source)
upload-options.md -- aws s3 cp vs sync, multipart
Migration-specific
ctas-patterns.md -- Athena CTAS syntax and partition transforms
glue-etl-migration.md -- Large-table migration via Glue 5.1 or higher PySpark
migration-validation.md -- Full validation checklist
migration-troubleshooting.md -- CTAS failures, visibility, partitions
JDBC-specific
jdbc-schema-discovery.md -- Crawler, direct inspection, custom SQL
jdbc-performance.md -- Parallel reads, partitioning
Files
- ATTRIBUTION.mdAdded by AutoGineer928 B
- LICENSELicence file added by AutoGineer9.9 KiB
- NOTICELicence file added by AutoGineer67 B
- SKILL.md10.8 KiB
- references/athena-loading.md2.8 KiB
- references/bigquery-ingest.md3.7 KiB
- references/catalog-migration.md6.9 KiB
- references/ctas-patterns.md3.0 KiB
- references/data-quality-validation.md12.2 KiB
- references/dynamodb-ingest.md7.2 KiB
- references/error-handling.md12.3 KiB
- references/format-specific-loading.md12.8 KiB
- references/glue-etl-migration.md4.5 KiB
- references/glue-job-config.md9.4 KiB
- references/glue-job-scripts.md9.6 KiB
- references/iceberg-catalog-config-and-usage.md7.9 KiB
- references/incremental-loading.md13.1 KiB
- references/jdbc-ingest.md5.7 KiB
- references/jdbc-performance.md10.3 KiB
- references/jdbc-schema-discovery.md12.7 KiB
- references/local-upload.md4.5 KiB
- references/migration-troubleshooting.md2.6 KiB
- references/migration-validation.md2.7 KiB
- references/s3-files.md5.7 KiB
- references/schema-evolution.md9.3 KiB
- references/snowflake-ingest.md3.6 KiB
- references/testing-and-scheduling.md13.6 KiB
- references/type-transformations.md7.8 KiB
- references/upload-options.md1.0 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs an MCP server, Needs a command-line tool
Security
What our scanner noticed· Low risk
- Instructions that use the network
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Published by
- AWS
- Publisher
- AWS (Official)
- Source
- aws/agent-toolkit-for-aws · plugins/aws-data-analytics/skills/ingesting-into-data-lake · commit 0a218f8
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- NOTICE (added by AutoGineer from NOTICE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
- Also distributed in
- aws/agent-toolkit-for-aws
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