Ingesting Into Data Lake logo

Ingesting Into Data Lake

OrganizationPopular
aws
ingesting-into-data-lake

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.

Overview

Publisheraws
Repositoryagent-toolkit-for-aws
Skill nameingesting-into-data-lake
Stars
2.7K
Forks
311
Bundled files
25
LicenseApache-2.0
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 25 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by aws on GitHub. Read the source before you install it.

Installation

Install the Ingesting Into Data Lake AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/aws/agent-toolkit-for-aws.git /tmp/agent-toolkit-for-aws
mkdir -p .claude/skills
cp -r /tmp/agent-toolkit-for-aws/plugins/aws-data-analytics/skills/ingesting-into-data-lake .claude/skills/ingesting-into-data-lake
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ingesting Into Data Lake in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ingesting Into Data Lake on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ingesting Into Data Lake is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

bash
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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ingesting Into Data Lake AI skill do?

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...

Why use Ingesting Into Data Lake on TypingMind?

Because you install it once and use it with any model. Ingesting Into Data Lake is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ingesting Into Data Lake in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-data-analytics/skills/ingesting-into-data-lake. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ingesting Into Data Lake?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ingesting Into Data Lake?

As many as you like. As long as a model supports skills, you can use Ingesting Into Data Lake with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ingesting Into Data Lake AI skill free?

Yes. It is published on GitHub by aws under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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