/ingesting-into-data-lake

name: ingesting-into-data-lake
description: >-
  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.
metadata:
  version: "1"
  argument-hint: "'[source-path|connection-name|table-name] [--target s3-tables|iceberg|parquet]'"

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 type | Reference | |---|---|---| | "upload my file", "local CSV", "move to S3" | Local file | [local-upload.md](references/local-upload.md) | | "load from S3", "import CSV/JSON/Parquet from s3://" | S3 files | [s3-files.md](references/s3-files.md) | | "import from Oracle/Postgres/MySQL/SQL Server/Redshift/RDS/Aurora" | JDBC | [jdbc-ingest.md](references/jdbc-ingest.md) | | "pull from Snowflake", "Snowflake table to S3" | Snowflake | [snowflake-ingest.md](references/snowflake-ingest.md) | | "import from BigQuery", "GCP analytics to S3" | BigQuery | [bigquery-ingest.md](references/bigquery-ingest.md) | | "export DynamoDB", "DynamoDB to data lake" | DynamoDB | [dynamodb-ingest.md](references/dynamodb-ingest.md) | | "migrate Glue table", "convert Hive to Iceberg" | Catalog migration | [catalog-migration.md](references/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 `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](references/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](references/glue-job-config.md) and [glue-job-scripts.md](references/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 r