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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/type-transformations.md

Type Transformation and Conflict Resolution Reference

This document describes expected approaches for handling type conflicts and transformations during data import.

Type Conflict Detection

What is a Type Conflict?

A type conflict occurs when:

  1. Target table exists with a defined schema

  2. Source data has a column with a different type

  3. Direct load would fail without transformation

Common Type Conflicts
Source TypeTarget TypeExample Conflict
STRINGINT/DECIMAL"$29.99" → 29.99
STRINGDATE/TIMESTAMP"2024-01-15" → DATE
INTSTRING12345 → "12345"
STRINGBOOLEAN"true"/"false" → TRUE/FALSE
DECIMALINT29.99 → 29 (loses precision)

Expected User Interaction

When a type conflict is detected, the skill should:

1. Clearly Identify the Conflict
[!] Type Conflict Detected:

Column: price
Source Type: STRING (contains values like "$29.99", "$149.50")
Target Type: DECIMAL(10,2)

This conflict must be resolved before import can proceed.
2. Present Clear Options
How would you like to handle this?

Option 1: Transform/Cast - Remove $ symbol and cast STRING to DECIMAL
  - Pros: Preserves all valid data
  - Cons: Invalid values may cause import to fail
  - Example: "$29.99" → 29.99

Option 2: Skip Invalid Rows - Skip rows where transformation fails
  - Pros: Import continues even with bad data
  - Cons: May lose some rows
  - Example: "$29.99" → 29.99, "N/A" → skipped

Option 3: Fail Import - Stop if any invalid values found
  - Pros: Ensures data quality
  - Cons: Requires fixing source data first
  - Example: Stops immediately on first invalid value

Which option do you prefer?
3. Wait for User Decision

Do NOT silently apply a transformation without user confirmation.

Transformation Patterns

STRING → Numeric (INT/DECIMAL)

PySpark:

from pyspark.sql.functions import regexp_replace, col

# Remove non-numeric characters except decimal point
transformed_df = source_df.withColumn(
    "price",
    regexp_replace(col("price"), "[^0-9.]", "").cast("decimal(10,2)")
)

# With validation (skip invalid)
from pyspark.sql.functions import when

transformed_df = source_df.withColumn(
    "price",
    when(
        regexp_replace(col("price"), "[^0-9.]", "").rlike("^[0-9.]+$"),
        regexp_replace(col("price"), "[^0-9.]", "").cast("decimal(10,2)")
    ).otherwise(None)
).filter(col("price").isNotNull())

Athena SQL:

SELECT
  CAST(regexp_replace(price, '[^0-9.]', '') AS DECIMAL(10,2)) AS price
FROM source_table
WHERE regexp_replace(price, '[^0-9.]', '') <> ''
STRING → DATE/TIMESTAMP

PySpark:

from pyspark.sql.functions import to_date, to_timestamp

# Simple date parsing
transformed_df = source_df.withColumn(
    "signup_date",
    to_date(col("signup_date"), "yyyy-MM-dd")
)

# Timestamp with timezone
transformed_df = source_df.withColumn(
    "event_timestamp",
    to_timestamp(col("event_timestamp"), "yyyy-MM-dd HH:mm:ss")
)

# Multiple format attempts
from pyspark.sql.functions import coalesce

transformed_df = source_df.withColumn(
    "date_field",
    coalesce(
        to_date(col("date_field"), "yyyy-MM-dd"),
        to_date(col("date_field"), "MM/dd/yyyy"),
        to_date(col("date_field"), "dd-MMM-yyyy")
    )
)

Athena SQL:

SELECT
  DATE_PARSE(date_string, '%Y-%m-%d') AS parsed_date,
  FROM_ISO8601_TIMESTAMP(timestamp_string) AS parsed_timestamp
FROM source_table
STRING → BOOLEAN

PySpark:

from pyspark.sql.functions import when, upper

transformed_df = source_df.withColumn(
    "is_active",
    when(upper(col("is_active")).isin("TRUE", "T", "YES", "Y", "1"), True)
    .when(upper(col("is_active")).isin("FALSE", "F", "NO", "N", "0"), False)
    .otherwise(None)
)

Athena SQL:

SELECT
  CASE
    WHEN UPPER(is_active) IN ('TRUE', 'T', 'YES', 'Y', '1') THEN TRUE
    WHEN UPPER(is_active) IN ('FALSE', 'F', 'NO', 'N', '0') THEN FALSE
    ELSE NULL
  END AS is_active
FROM source_table
Numeric → STRING

PySpark:

# Simple cast
transformed_df = source_df.withColumn(
    "id_as_string",
    col("id").cast("string")
)

# With formatting
from pyspark.sql.functions import format_string

transformed_df = source_df.withColumn(
    "price_formatted",
    format_string("$%.2f", col("price"))
)
Handling NULL Values

PySpark:

from pyspark.sql.functions import coalesce, lit

# Provide default for nulls
transformed_df = source_df.withColumn(
    "quantity",
    coalesce(col("quantity"), lit(0))
)

# Filter out nulls in critical columns
transformed_df = source_df.filter(
    col("customer_id").isNotNull() &
    col("order_date").isNotNull()
)

Complete Transformation Example

Scenario

Source CSV has:

  • price as STRING with "$" prefix

  • signup_date as STRING "YYYY-MM-DD"

  • is_active as STRING "true"/"false"

Target table expects:

  • price as DECIMAL(10,2)

  • signup_date as DATE

  • is_active as BOOLEAN

Glue ETL Script
import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
from pyspark.sql.functions import regexp_replace, to_date, when, upper, col

args = getResolvedOptions(sys.argv, ['JOB_NAME', 'source_path', 'target_table'])
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)

# Read source CSV
source_df = spark.read.format("csv") \
    .option("header", "true") \
    .load(args['source_path'])

# Apply transformations
transformed_df = source_df \
    .withColumn(
        "price",
        regexp_replace(col("price"), "[^0-9.]", "").cast("decimal(10,2)")
    ) \
    .withColumn(
        "signup_date",
        to_date(col("signup_date"), "yyyy-MM-dd")
    ) \
    .withColumn(
        "is_active",
        when(upper(col("is_active")) == "TRUE", True)
        .when(upper(col("is_active")) == "FALSE", False)
        .otherwise(None)
    )

# Filter out rows with failed transformations
clean_df = transformed_df.filter(
    col("price").isNotNull() &
    col("signup_date").isNotNull() &
    col("is_active").isNotNull()
)

# Log filtered count
original_count = source_df.count()
clean_count = clean_df.count()
print(f"Original rows: {original_count}")
print(f"Clean rows: {clean_count}")
print(f"Filtered out: {original_count - clean_count}")

# Write to Iceberg table
clean_df.writeTo(args['target_table']).append()

job.commit()

Evaluation Criteria

When evaluating type conflict resolution:

Detection:

  • Skill compares source schema to target schema

  • Identifies specific columns with type mismatches

  • Clearly communicates the conflict to user

User Interaction:

  • Presents at least 2-3 options for handling the conflict

  • Explains pros/cons of each option

  • Waits for user decision before proceeding

  • Does NOT silently transform without confirmation

Transformation Code:

  • Provides complete PySpark or SQL code for transformation

  • Handles edge cases (null values, invalid formats)

  • Includes data quality filters if "skip invalid" chosen

  • Logs row counts (original vs transformed)

Validation:

  • Tests transformation on sample data first

  • Validates that transformed types match target schema

  • Reports success/failure clearly

Common Mistakes to Avoid

Silently applying transformations without user consent Not detecting type conflicts before attempting import Incomplete transformation code (missing null handling) Not logging how many rows were filtered out Assuming all source data is valid without validation Not providing fallback for invalid values Generic "cast to type" without cleaning data first (e.g., "$29.99" → cast fails)

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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