We have a customer with transactional data in PostgreSQL. They want to run analytical use cases in Snowflake and publish incremental data daily. How would you design the solution to ingest only incremental data each day?
💡 Model Answer
Design a pipeline that extracts only incremental changes from PostgreSQL and loads them into Snowflake daily. 1) Capture changes: Use PostgreSQL logical replication or Debezium to stream changes to Kafka. 2) Ingest to S3: Write change events to S3 in Parquet/Avro, partitioned by date. 3) Transform: Use AWS Glue jobs to read the incremental files, apply schema evolution, and write to a staging table in Snowflake. 4) Load: Use Snowpipe or bulk COPY to load the transformed data into the target Snowflake table, using a MERGE statement to upsert based on primary key. 5) Orchestrate: Use AWS Step Functions or Airflow to schedule daily runs, maintain checkpoints, and handle failures. 6) Monitoring: Use CloudWatch and Snowflake query history. This architecture ensures only new rows are processed each day, reduces load, and keeps the analytics layer fresh.
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