Ingestion: dlt + lambda

Overview

This example demonstrates a serverless data ingestion pipeline that:

1. Fetches chess data from an external source and processes it using dlt

2. Writes the data to S3

The pipeline runs as an AWS Lambda function packaged in a Docker container.

How It Works

Infrastructure Components

• AWS Lambda: Executes the ingestion code on demand

• Amazon ECR: Stores the Docker container image

• Amazon S3: Temporary storage for data files before loading to Snowflake

• AWS Secrets Manager: Stores credentials and configuration

• Terraform: Provisions and manages all infrastructure

Code Structure

pipelines/
├── chess_lambda.tf           # Terraform creating the lambda function and ECR repository
└── ingest/
    ├── chess_source_schema.yml   # Snowflake table schema definitions in YAML format
    └── chess-ingestion/          # Lambda function code
        ├── Dockerfile
        ├── lambda_handler.py # Lambda code with DLT pipeline
        └── ...

Data Flow Process

The pipeline follows these steps:

1. Extraction: DLT extracts data from the source

2. Transformation: DLT performs basic transformations (typing, normalization)

3. Loading: DLT loads the data directly to S3

4. Schema Management: Table schemas are defined in YAML files and managed by the pipeline

Development Guide

1. Local Development with DuckDB

For rapid iteration without AWS resources, use DuckDB as the destination:

1. Create a .env.local file with:

   DESTINATION=duckdb
   # Add any source-specific credentials here

2. Run the pipeline locally:

   make run-local

3. Examine results in the local .duckdb database

2. Local Development with S3

To run the lambda with S3 as a temporary destination:

1. Configure .env.local with:

   DESTINATION=filesystem
   AWS_REGION=<your-aws-region>
   S3_BUCKET_NAME=<your-s3-bucket-name>
   AWS_PROFILE=<your-aws-profile>
   # Add any source-specific credentials here

2. Run with the same command:

   make run-local

3. Testing on AWS

Once your code is deployed to AWS you can run the lambda with:

export AWS_PROFILE=<your_profile>
make run-lambda env=<your_environment>

4. VSCode Debugging

For interactive debugging, add this to .vscode/launch.json:

{
    "name": "Debug chess lambda",
    "type": "debugpy",
    "request": "launch",
    "program": "${workspaceFolder}/pipelines/ingest/chess-ingestion/lambda_handler.py",
    "console": "integratedTerminal",
    "cwd": "${workspaceFolder}/pipelines/ingest/chess-ingestion",
    "justMyCode": false
}

Schema Management

Snowflake landing table schemas are defined in YAML files in the pipelines/ingest/<source-name>_source_schema.yml file.

After running the pipeline locally, generate a source schema definition:

cd pipelines/
uvx boringdata dlt get-schema chess

This will generate a schema file chess_source_schema.yml in the pipelines folder to define the Snowflake tables.

Manual Deployment

For manual deployment:

# Set required environment variables
export AWS_PROFILE=<your_profile>

# Build and deploy
make deploy env=<your_environment>

This process:

1. Builds the Docker image locally

2. Pushes it to ECR

3. Updates the Lambda to use the new image

Common Commands

# Development
make run-local                        # Run locally with settings from .env.local
make run-lambda env=<environment>     # Execute on AWS Lambda

# Deployment
make build env=<environment>          # Build Docker image
make deploy env=<environment>         # Build and deploy to ECR

# Utilities
make help                             # Show all available commands

Resources

• DLT Documentation

• Snowflake SQL API Documentation