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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://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/glue-job-config.md

Glue Job Configuration Guide

Guide for creating Glue jobs, configuring workers, advanced PySpark patterns, and monitoring for external data import pipelines.

Creating the Glue Job

Once you have the PySpark script saved to S3 (e.g., s3://<scripts-bucket>/glue-jobs/external-import-<table-name>.py), create the Glue job.

AWS CLI
aws glue create-job \
  --name "external-import-<source>-<table>" \
  --role "<glue-role-arn>" \
  --command "Name=glueetl,ScriptLocation=s3://<scripts-bucket>/glue-jobs/external-import-<table>.py,PythonVersion=3" \
  --connections "Connections=<glue-connection-name>" \
  --default-arguments '{
    "--datalake-formats": "iceberg",
    "--connection_name": "<glue-connection-name>",
    "--source_table": "<schema>.<table>",
    "--target_table": "<catalog>.<namespace>.<s3-table>",
    "--watermark_column": "<timestamp-column>",
    "--watermark_bucket": "<bucket>",
    "--watermark_key": "watermarks/<table-name>.txt",
    "--conf": "<see iceberg-catalog-config-and-usage.md for S3 Tables or standard Iceberg catalog config>",
    "--enable-glue-datacatalog": "true",
    "--enable-metrics": "true",
    "--enable-continuous-cloudwatch-log": "true"
  }' \
  --glue-version "5.1" \
  --number-of-workers 5 \
  --worker-type "G.1X" \
  --timeout 60 \
  --max-retries 1 \
  --region <region>

Job Configuration Parameters

Worker Types and Sizing

Choose worker type based on workload characteristics:

Worker TypevCPUsMemoryUse Case
G.1X416 GBStandard ETL, small to medium data volumes
G.2X832 GBLarge data volumes, memory-intensive transforms
G.4X1664 GBVery large data volumes, complex joins
G.8X32128 GBMassive data volumes, high parallelism

Number of workers guidance:

  • Small tables (<1M rows, <1 GB): 2-5 workers, G.1X

  • Medium tables (1M-10M rows, 1-10 GB): 5-10 workers, G.1X or G.2X

  • Large tables (10M-100M rows, 10-100 GB): 10-20 workers, G.2X

  • Very large tables (>100M rows, >100 GB): 20-50 workers, G.2X or G.4X

Start conservative and scale up based on job duration and throughput.

Timeout Configuration

Set timeout based on expected job duration:

  • Small incremental loads: 15-30 minutes

  • Medium incremental loads: 30-60 minutes

  • Large incremental loads: 60-120 minutes

  • Full refresh of large tables: 120-480 minutes

Add buffer for source database query time and network latency.

Retry Configuration

Configure retries for transient failures:

'MaxRetries': 1  # Retry once on failure

For production pipelines, consider:

  • Setting MaxRetries to 1-2 for transient network issues

  • Using Glue job bookmarks to avoid duplicate processing

  • Implementing idempotent logic (upsert instead of append)

Important Job Arguments

Required arguments:

  • --datalake-formats iceberg: Required for S3 Tables and standard Iceberg targets

  • --enable-glue-datacatalog: Enable Glue Data Catalog integration for Iceberg

  • --conf: Spark catalog configuration. See iceberg-catalog-config-and-usage.md for the exact keys per target type.

  • --enable-metrics: Publish CloudWatch metrics

  • --enable-continuous-cloudwatch-log: Stream logs to CloudWatch

Optional arguments:

  • --enable-spark-ui: Enable Spark UI for debugging (requires S3 bucket)

  • --spark-event-logs-path: Where to store Spark UI logs

  • --conf spark.sql.adaptive.enabled=true: Enable adaptive query execution

  • --conf spark.sql.adaptive.coalescePartitions.enabled=true: Optimize partition count

Network Configuration

If the source database is in a VPC, ensure the Glue job has network access:

'Connections': {
    'Connections': ['<glue-connection-name>']
}

The connection specifies:

  • VPC

  • Subnet

  • Security groups

  • Availability zone

Glue provisions ENIs in the specified subnet to access the database.

Advanced PySpark Patterns

Parallel Reads with Partitioning

For large tables, read data in parallel using Spark partitioning:

# Read with parallel partitions
source_df = spark.read.format("jdbc").options(
    url=jdbc_url,
    dbtable="large_table",
    numPartitions=10,  # Read with 10 parallel connections
    partitionColumn="id",  # Partition on this column
    lowerBound=1,  # Min value
    upperBound=10000000  # Max value
).load()

This creates 10 parallel queries:

  • Partition 1: WHERE id >= 1 AND id < 1000000

  • Partition 2: WHERE id >= 1000000 AND id < 2000000

  • ...

  • Partition 10: WHERE id >= 9000000 AND id <= 10000000

Best practices:

  • Use a numeric column with even distribution

  • Set numPartitions = number of workers × cores per worker

  • Choose lowerBound and upperBound based on actual data range

Deduplication Logic

If there's risk of duplicate records (job retries, late arrivals):

from pyspark.sql.window import Window
from pyspark.sql.functions import row_number

# Deduplicate by primary key, keeping latest by watermark
window = Window.partitionBy("primary_key").orderBy(col(watermark_column).desc())
deduplicated_df = source_df.withColumn("row_num", row_number().over(window)) \
    .filter(col("row_num") == 1) \
    .drop("row_num")
Type Conversion and Validation

Add data quality checks and type conversions:

from pyspark.sql.functions import col, when

transformed_df = source_df.select(
    # Safe type casting with null handling
    when(col("amount").cast("double").isNotNull(), col("amount").cast("double"))
        .otherwise(0.0).alias("amount"),

    # String trimming and validation
    when(col("email").rlike(r"^[\w\.-]+@[\w\.-]+\.\w+$"), col("email"))
        .otherwise(None).alias("email"),

    # Date parsing with fallback
    when(col("order_date").isNotNull(),
         to_date(col("order_date"), "yyyy-MM-dd"))
        .otherwise(None).alias("order_date")
)
Watermark with Buffer for Late Arrivals

If source data can arrive late (event timestamp < updated timestamp):

from datetime import timedelta

# Load data from 1 day before last watermark to catch late arrivals
buffer_watermark = (datetime.strptime(last_watermark, '%Y-%m-%d %H:%M:%S')
                    - timedelta(days=1)).strftime('%Y-%m-%d %H:%M:%S')

filtered_df = source_df.filter(
    f"{args['watermark_column']} > '{buffer_watermark}'"
)

# Then use upsert to avoid duplicates

Monitoring and Observability

CloudWatch Logs

Glue streams job logs to CloudWatch Logs under:

  • Log group: /aws-glue/jobs/output

  • Log stream: <job-name>-<job-run-id>

Key log patterns to monitor:

  • Last watermark: <value> - Starting point for incremental load

  • Loading X new/updated records - How many records found

  • Updated watermark to: <value> - New watermark after load

  • ERROR - Any errors during execution

CloudWatch Metrics

With --enable-metrics, Glue publishes:

  • glue.driver.aggregate.numCompletedTasks - Tasks completed

  • glue.driver.aggregate.elapsedTime - Job duration

  • glue.driver.aggregate.recordsRead - Records read from source

  • glue.driver.aggregate.bytesRead - Bytes read from source

Set up CloudWatch alarms for:

  • Job failures (state = FAILED)

  • Long-running jobs (duration > threshold)

  • No records loaded (might indicate source issue)

Spark UI

Enable Spark UI for detailed execution metrics:

'DefaultArguments': {
    '--enable-spark-ui': 'true',
    '--spark-event-logs-path': 's3://<logs-bucket>/spark-logs/'
}

Access via Glue console → Job runs → View Spark UI

Use Spark UI to:

  • Identify slow stages (data skew, shuffle issues)

  • Analyze task distribution across workers

  • Debug memory issues (GC time, spills to disk)

Script Storage and Versioning

Best practices for script management:

  1. Store scripts in S3: s3://<scripts-bucket>/glue-jobs/<job-name>.py

  2. Version scripts: Use S3 versioning or include version in filename

  3. Separate environments: Different buckets for dev/staging/prod

  4. Use Git: Maintain scripts in Git, deploy to S3 via CI/CD

Example structure:

s3://my-glue-scripts/
  prod/
    external-import-customers.py
    external-import-orders.py
  dev/
    external-import-customers.py
    external-import-orders.py

Testing Scripts Locally

Test PySpark scripts locally before deploying to Glue:

# Install dependencies
pip install pyspark boto3

# Run script locally (modify to use local Spark)
python external-import-customers.py \
  --JOB_NAME test-run \
  --connection_name test-connection \
  --source_table customers \
  --target_table local.test.customers \
  --watermark_column updated_at \
  --watermark_bucket test-bucket \
  --watermark_key watermarks/customers.txt

For full local testing, use AWS Glue Docker images:

docker pull amazon/aws-glue-libs:glue_libs_5.0.0_image_01

Summary

Glue ETL job creation workflow:

  1. Choose template - Append, Upsert, Custom SQL, or Full Refresh

  2. Customize script - Add transformations, validation, error handling

  3. Save to S3 - Store script in versioned S3 location

  4. Create job - Use MCP or CLI with appropriate configuration

  5. Size workers - Choose worker type and count based on data volume

  6. Configure monitoring - Enable CloudWatch logs and metrics

  7. Test locally - Validate logic before deploying (optional)

With a well-configured Glue job, external database data flows continuously into S3 Tables with minimal operational overhead.

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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