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Ingesting Into Data Lake

AWSOfficialPublished by AWS

  • Skill
  • Workflow
  • Data and AI
  • Apache-2.0
  • Low risk
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From the author

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 description by AWS

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

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

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/aws/ingesting-into-data-lake/download

  2. Unzip 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-identity

  • For 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 typeReference
"upload my file", "local CSV", "move to S3"Local filelocal-upload.md
"load from S3", "import CSV/JSON/Parquet from s3://"S3 filess3-files.md
"import from Oracle/Postgres/MySQL/SQL Server/Redshift/RDS/Aurora"JDBCjdbc-ingest.md
"pull from Snowflake", "Snowflake table to S3"Snowflakesnowflake-ingest.md
"import from BigQuery", "GCP analytics to S3"BigQuerybigquery-ingest.md
"export DynamoDB", "DynamoDB to data lake"DynamoDBdynamodb-ingest.md
"migrate Glue table", "convert Hive to Iceberg"Catalog migrationcatalog-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 s3tablescatalog federated catalog and active table buckets: recommend S3 Tables

  • Account 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-table

  • Target database named by fuzzy term -> finding-data-lake-assets

  • User 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:

  1. Row count matches expected (source vs target)

  2. Null check on critical columns

  3. 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

  • --target flag: Pre-fill the target format in Step 4

  • No args: Walk through interactively

Gotchas

  • S3 Tables requires Glue 5.1 or higher and --datalake-formats iceberg job argument

  • All spark.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 for correct catalog configs.

  • The warehouse parameter 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, use createOrReplace()

  • 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 skill

  • For target tables in SageMaker Unified Studio projects, ensure the project role has write access to the target namespace before the Glue job runs

Troubleshooting

ErrorLikely causeAction
Access Denied on S3Missing IAM permissionsCheck Glue role has s3:GetObject, s3:PutObject
Access Denied on S3 TablesMissing s3tables:* permissionsAdd S3 Tables inline policy to Glue role
CTAS timeoutDataset too large for AthenaSwitch to Glue ETL or batch with WHERE filters
JDBC connection timeout/auth failureConnection-level issueDelegate to connecting-to-data-source
Throughput exceeded (DynamoDB)Read percent too highLower read.percent or use native export

See error-handling.md for the full catalog.

References

Source-specific
Cross-cutting
Migration-specific
JDBC-specific

Files

references/catalog-migration.md

Catalog Migration to S3 Tables

Migrate existing Glue Data Catalog tables into Amazon S3 Tables. Source tables can be Hive-format, self-managed Iceberg, or any format Athena can read. The result is a fully managed S3 Table with automatic compaction, snapshot management, and multi-engine access.

Reference Documentation

Why Migrate?

Self-managed Iceberg and Hive tables require manual compaction, snapshot cleanup, and storage optimization. S3 Tables handles all of this automatically. Migration also enables the four-part catalog hierarchy (s3tablescatalog/<bucket>/<namespace>/<table>) for unified access from Athena, EMR, Redshift, and Spark.

Note: The target for catalog migration is always S3 Tables -- that is the purpose of this workflow.

Workflow

Phase 1: Understand the Source

  1. Identify the source table: Get the fully qualified name (database.table or catalog.database.table). If the user gives a fuzzy or business name ("our orders table", "the sales data"), delegate to the finding-data-lake-assets skill to resolve it before continuing -- the rest of this workflow assumes a concrete reference.

  2. Inspect the source:

    • With MCP: Use aws-mcp to get table metadata (format, location, schema, partitions)

    • Without MCP: aws glue get-table --database-name <db> --name <table>

  3. Classify the source format:

    • Hive (CSV, Parquet, ORC, JSON, Avro): Standard external table backed by S3 general purpose bucket

    • Self-managed Iceberg: Iceberg table in general purpose bucket with manual maintenance

    • Other: Any format Athena can query (federated sources, etc.)

  4. Assess size and complexity:

    • Small/medium (under ~100 GB, simple schema): Path A (Athena CTAS) -- single SQL statement

    • Large (over ~100 GB, complex transforms, or needs scheduling): Path B (Glue ETL)

    • Partitioned source: Note partition columns and strategy for conversion

Phase 2: Prepare the Target

  1. Ensure table bucket exists: Check with aws s3tables list-table-buckets. If none, delegate to creating-data-lake-table Phase 2.

  2. Ensure analytics integration is enabled: Verify s3tablescatalog exists. Delegate to creating-data-lake-table Phase 2, step 4 if not set up.

  3. Create or select namespace: Use existing or create new via aws s3tables create-namespace.

  4. Plan partition strategy: Iceberg supports hidden partition transforms (day(), month(), year(), hour(), bucket()). Recommend converting Hive-style explicit partition columns to Iceberg transforms where possible.

Phase 3: Migrate the Data
Path A: Athena CTAS (default for small/medium tables)

Single SQL statement that creates the S3 Table and populates it in one step. See ctas-patterns.md for full syntax and examples.

Key points:

  • Target path: "s3tablescatalog/<table_bucket_name>"."<namespace>"."<new_table_name>"

  • Default format: PARQUET. Also supports AVRO, ORC.

  • Use Iceberg partition transforms (day(), month(), bucket()) instead of Hive-style explicit partition columns.

  • No LOCATION clause -- S3 Tables manages storage.

  • Table and column names must be all lowercase.

  • Source catalog for default GDC tables is awsdatacatalog.

  • Add WHERE filters to migrate subsets or batch large migrations.

Path B: Glue ETL (for large tables or complex transforms)

Use when CTAS would time out, when transforms are complex, or when the migration needs to be scheduled/repeatable.

  1. Create PySpark script that reads from source and writes to S3 Table

  2. Create Glue 5.1 or higher job with --datalake-formats iceberg and --conf catalog config

  3. Run and monitor the job

See glue-etl-migration.md for job configuration, PySpark script template, and catalog setup.

Phase 4: Validate the Migration

Run all of these checks -- do not skip any:

  1. Row count comparison:

    SELECT 'source' AS tbl, COUNT(*) AS cnt FROM "<source_catalog>"."<source_db>"."<source_table>"
UNION ALL
SELECT 'target' AS tbl, COUNT(*) AS cnt FROM "s3tablescatalog/<bucket>"."<namespace>"."<new_table>"

  2. Schema comparison: Verify column names, types, and order match expectations. Minor type promotions (e.g., int to bigint) are acceptable.

  3. Spot-check data: Compare a sample of rows between source and target, focusing on:

    • Boundary values (min/max of numeric and date columns)

    • Null counts per column

    • Distinct counts on key columns

  4. Partition verification (if partitioned):

    SELECT <partition_column>, COUNT(*) FROM "s3tablescatalog/<bucket>"."<namespace>"."<new_table>"
GROUP BY 1 ORDER BY 1

See migration-validation.md for the full checklist.

Phase 5: Post-Migration Guidance

After validation passes:

  1. Update downstream consumers: Provide the new table path for queries, dashboards, and ETL jobs.

  2. Recommend keeping the source table temporarily as a rollback option. Suggest a retention period (e.g., 30 days).

  3. Do NOT drop the source table. Warn the user and let them decide when to clean up.

  4. Evaluate table lineage: If the source table has lineage present, use it to recommend next-steps for producers and consumers.

Gotchas

  • Athena CTAS has a 100-partition limit per statement. For sources with more than 100 partitions, either migrate in batches with WHERE filters or use Glue ETL (Path B).

  • CTAS creates a new table -- it does not do an in-place conversion. The source table remains unchanged.

  • Column names with uppercase letters will cause the target table to be invisible to analytics services. Always lowercase column names in the SELECT: SELECT upper_Col AS upper_col.

  • Self-managed Iceberg tables may have schema evolution history (added/renamed columns). CTAS captures the current schema only -- historical evolution is not preserved.

  • Hive tables with complex SerDe configurations (custom delimiters, regex SerDe) should be tested with a small CTAS first to verify Athena can read them correctly. Glue will often read things Athena cannot. Try Glue if Athena fails.

  • Time travel on the source Iceberg table is lost after migration. The S3 Table starts fresh with its own snapshot history.

Troubleshooting

See migration-troubleshooting.md for common errors and fixes covering CTAS failures, validation mismatches, visibility issues, and partition problems.

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)
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aws/agent-toolkit-for-aws

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