PostgreSQL

Sortment does not directly support PostgreSQL as a data source. In case you don't have a data warehouse, you will need to setup a sync between your PostgreSQL to a data warehouse of your choice. This document outlines different approaches and the best practices for setting up an automated ETL pipeline.

Choosing the right method for ETL

There are several ways to setup data pipeline from PostgreSQL, but we've covered the two most common ones here:

Option 1: Using Managed ETL Tools (No-Code/Low-Code)

If you prefer a managed solution with minimal engineering effort, this is the preferred approach. If your data volume is low, this is a cost effective way to get your pipeline up and running quickly. You can use any of the following tools:

Tool	Pricing Model	Pros	Cons
Airbyte (Self-hosted)	Free (infra costs apply)	Open-source, customizable	Requires engineering effort
Stitch	$100/month (10M rows)	Easy setup, cost-effective for small-scale	Limited connectors, batch only
Hevo	$249/month	Real-time sync, no maintenance	Costs rise with volume
Fivetran	$500+/month	Fully managed, enterprise-grade	Most expensive option

These tools handle incremental syncs, schema changes, and monitoring automatically, but can be costly if the data volume is huge.

Best Practices

• Use Incremental Syncs: Avoid full data loads to optimize costs and performance.

• Monitor Data Pipeline: Set up logging and alerts for failures.

• Optimize Storage Costs: Use compressed formats like Parquet.

• Schema Evolution: Ensure the ETL tool can handle schema changes automatically.

Option 2: Cloud-native Solution (Engineering required)

For a fully managed and scalable data pipeline, cloud-native ETL solutions offer a reliable way to migrate PostgreSQL data to Redshift or BigQuery. We have linked respective documentations for migrating data from PostgreSQL to cloud data warehouses.

• AWS Database Migration Service into Redshift (Guide)

• BigQuery Data Transfer Service (Guide)

Best Practices

• Use Incremental Loading: Reduce costs by syncing only changed data.

• Monitor & Alert: Set up monitoring via CloudWatch (AWS) or Stackdriver (GCP).

• Optimize Storage: Use Parquet or Avro for efficient processing.

• Automate Schema Evolution: Ensure schema changes don’t break the pipeline.

Support

If you need help with choosing what is the best option for your company, feel free to reach out to [e-mail]