5-minute tutorial

Migrate Amazon Redshift to AWS Athena in 60 Seconds

Learn how to copy your Amazon Redshift data to AWS Athena with a single command using ingestr - no code required.

One command Zero code Production ready

What you'll learn

How to install and set up ingestr in seconds

Connect to Amazon Redshift and AWS Athena with proper authentication

Copy entire tables or specific data with a single command

Set up incremental loading for continuous data synchronization

Prerequisites

Python 3.8 or higher installed
Redshift cluster running and accessible
Security group allows inbound connections
Database user with appropriate permissions
VPC and subnet properly configured
AWS credentials
S3 bucket access
AWS Glue Catalog permissions

Step 1: Install ingestr

Install ingestr in seconds using pip. Choose the method that works best for you:

Recommended: Using uv (fastest)

# Install uv first if you haven't already
pip install uv

# Run ingestr using uvx
uvx ingestr

Alternative: Global installation

# Install globally using uv
uv pip install --system ingestr

# Or using standard pip
pip install ingestr

Verify installation: Run ingestr --version to confirm it's installed correctly.

Step 2: Your First Migration

Let's copy a table from Amazon Redshift to AWS Athena. This example shows a complete, working command you can adapt to your needs.

Set up your connections

Amazon Redshift connection format:

redshift://username:password@host:port/database

Parameters:

• username: Master username or IAM user
• password: User password
• host: Cluster endpoint URL
• port: Port number (default 5439)
• database: Database name

AWS Athena connection format:

athena://?bucket=<your-destination-bucket>&access_key_id=<your-aws-access-key-id>&secret_access_key=<your-aws-secret-access-key>&region_name=<your-aws-region>

Parameters:

• bucket: S3 bucket name for storing Parquet files
• access_key_id: AWS access key ID for authentication
• secret_access_key: AWS secret access key for authentication
• region_name: AWS region for Athena service and S3 buckets
• workgroup: Athena workgroup name
• profile: AWS profile name to use

BigQuery Setup Required

Before running the command:

Create a service account in Google Cloud Console
Grant it BigQuery Data Editor and Job User roles
Download the JSON key file
Use the path to this file in your connection string

Run your first copy

Copy the entire users table from Amazon Redshift to AWS Athena:

ingestr ingest \
    --source-uri 'redshift://admin:[email protected]:5439/mydb' \
    --source-table 'staging.raw_data' \
    --dest-uri 'athena://?bucket=bucket_123&access_key_id=access_123&secret_access_key=secret_123&region_name=eu-central-1' \
    --dest-table 'raw.raw_data'

What this does:

• Connects to your Amazon Redshift database
• Reads all data from the specified table
• Creates the table in AWS Athena if needed
• Copies all rows to the destination

Command breakdown:

--source-uri Your source database
--source-table Table to copy from
--dest-uri Your destination
--dest-table Where to write data

Step 3: Verify your data

After the migration completes, verify your data was copied correctly:

Check row count in AWS Athena:

-- Run this in AWS Athena
SELECT COUNT(*) as row_count 
FROM raw.raw_data;

-- Check a sample of the data
SELECT * 
FROM raw.raw_data 
LIMIT 10;

Advanced Patterns

Once you've mastered the basics, use these patterns for production workloads.

Only copy new or updated records since the last sync. Perfect for daily updates.

ingestr ingest \
    --source-uri 'redshift://admin:[email protected]:5439/mydb' \
    --source-table 'public.orders' \
    --dest-uri 'athena://?bucket=bucket_123&access_key_id=access_123&secret_access_key=secret_123&region_name=eu-central-1' \
    --dest-table 'raw.orders' \
    --incremental-strategy merge \
    --incremental-key updated_at \
    --primary-key order_id

How it works: The merge strategy updates existing rows and inserts new ones based on the primary key. Only rows where updated_at has changed will be processed.

Common Use Cases

Ready-to-use commands for typical Amazon Redshift to AWS Athena scenarios.

Daily Customer Data Sync

Keep your analytics warehouse updated with the latest customer information every night.

# Add this to your cron job or scheduler
ingestr ingest \
    --source-uri 'redshift://admin:[email protected]:5439/mydb' \
    --source-table 'public.customers' \
    --dest-uri 'athena://?bucket=bucket_123&access_key_id=access_123&secret_access_key=secret_123&region_name=eu-central-1' \
    --dest-table 'analytics.customers' \
    --incremental-strategy merge \
    --incremental-key updated_at \
    --primary-key customer_id

Historical Data Migration

One-time migration of all historical records to your data warehouse.

# One-time full table copy
ingestr ingest \
    --source-uri 'redshift://admin:[email protected]:5439/mydb' \
    --source-table 'public.transactions' \
    --dest-uri 'athena://?bucket=bucket_123&access_key_id=access_123&secret_access_key=secret_123&region_name=eu-central-1' \
    --dest-table 'warehouse.transactions_historical'

Development Environment Sync

Copy production data to your development AWS Athena instance (with sensitive data excluded).

# Copy sample data to development
ingestr ingest \
    --source-uri 'redshift://admin:[email protected]:5439/mydb' \
    --source-table 'public.products' \
    --dest-uri 'athena://?bucket=bucket_123&access_key_id=access_123&secret_access_key=secret_123&region_name=eu-central-1' \
    --dest-table 'dev.products' \
    --limit 1000  # Only copy 1000 rows for testing

Troubleshooting Guide

Solutions to common issues when migrating from Amazon Redshift to AWS Athena.

Connection refused or timeout errors

Check your connection details:

Verify cluster is not paused
Check VPC security group rules
Ensure cluster is publicly accessible if connecting from outside VPC
Validate IAM roles if using temporary credentials

Authentication failures

Common authentication issues:

Verify cluster is not paused
Check VPC security group rules
Ensure cluster is publicly accessible if connecting from outside VPC
Validate IAM roles if using temporary credentials

Schema or data type mismatches

Handling data type differences:

ingestr automatically handles most type conversions
Amazon Redshift: SUPER type for semi-structured data
Amazon Redshift: SORTKEY and DISTKEY affect performance
Amazon Redshift: VARCHAR length limits are enforced
Amazon Redshift: Compression encodings impact storage

Performance issues with large tables

Optimize large data transfers:

Use incremental loading to process data in chunks
Run migrations during off-peak hours
Split very large tables by date ranges using interval parameters

Ready to scale your data pipeline?

You've learned how to migrate data from Amazon Redshift to AWS Athena with ingestr. For production workloads with monitoring, scheduling, and data quality checks, explore Bruin Cloud.

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