Here’s a comprehensive list of tools and services you can use to build a data pipeline from MySQL to Snowflake using AWS:
- AWS Database Migration Service (DMS) – For initial migration and ongoing CDC from MySQL
- AWS Glue – For scheduled extraction jobs with custom transformations
- MySQL Workbench – For one-time exports and schema analysis
- Debezium – Open-source CDC connector for MySQL that works with AWS services
- AWS Glue ETL – Serverless Spark jobs for transformations
- AWS Lambda – For lightweight transformations and triggering other services
- Amazon EMR – For complex, large-scale data transformations
- AWS Step Functions – For orchestrating complex transformation workflows
- Snowflake Snowpipe – For continuous data loading into Snowflake
- Snowflake COPY command – For batch loading from S3
- Snowflake JDBC/ODBC drivers – For direct connections from AWS services
- Amazon S3 – Essential staging area between MySQL and Snowflake
- Amazon RDS for MySQL – Managed MySQL if migrating from on-premises MySQL
- AWS Lake Formation – For creating a data lake with your MySQL data
- AWS Step Functions – For building complex pipeline workflows
- AWS Managed Workflows for Apache Airflow (MWAA) – For pipeline orchestration
- Amazon EventBridge – For event-driven pipelines
- AWS CloudWatch – For monitoring and alerting
- AWS CloudTrail – For auditing pipeline activities
- AWS Identity and Access Management (IAM) – For access control
- AWS Key Management Service (KMS) – For encryption key management
- AWS Secrets Manager – For storing database credentials
- AWS CloudFormation – For infrastructure as code deployment of pipeline components
- Fivetran – Fully managed ELT pipelines with MySQL and Snowflake connectors
- Matillion – AWS-native ETL tool with strong Snowflake integration
- Stitch Data – Simple ELT service with MySQL and Snowflake support
- Talend – Enterprise data integration platform
- Informatica – Enterprise data integration with AWS and Snowflake connectors
Each of these tools can be combined in different ways depending on your specific requirements, data volumes, transformation complexity, and budget considerations.