I'm building a reporting stack with Postgres, Salesforce, and S3, what pipeline approach should I use?

For a stack with Postgres + Salesforce + S3, the best default approach is usually a modern ELT pipeline with a central warehouse and light orchestration.

Recommended pattern

1) Ingest data from each source into a warehouse

• Postgres: use CDC or incremental replication if possible
• Salesforce: API-based extraction on a schedule, ideally incremental
• S3: land raw files directly or sync them into curated tables

2) Keep raw data first, transform second

A good pattern is:

• Raw layer: store data as-is from each source
• Staging layer: clean/normalize fields
• Mart layer: business-ready reporting tables

This makes debugging and reprocessing much easier.

3) Use ELT instead of ETL

Since you’re building a reporting stack, it’s usually better to:

• Extract from Postgres/Salesforce/S3
• Load into warehouse
• Transform in SQL/dbt

This is especially good when:

• you want faster iteration
• reporting logic changes often
• analysts need visibility into transformations

Typical architecture

• Sources: Postgres, Salesforce, S3
• Ingestion: Airbyte/Fivetran/custom jobs
• Orchestration: Dagster, Airflow, Prefect, or managed scheduler
• Warehouse: Snowflake, BigQuery, Redshift, or Postgres if small
• Transforms: dbt
• BI layer: Looker, Metabase, Tableau, Power BI

Source-specific recommendations

Postgres

• If it’s an operational DB, avoid heavy queries on it
• Prefer:
  • logical replication / CDC
  • incremental extracts using updated_at
• If volume is small, scheduled full refresh may be fine initially

Salesforce

• Use a connector rather than building custom API logic unless necessary
• Watch out for:
  • API limits
  • soft deletes
  • record updates vs creates
  • nested objects and denormalization

S3

• Treat S3 as either:
  • a source of files to ingest, or
  • a data lake/raw landing zone
• Standardize file formats if possible:
  • Parquet > CSV/JSON for analytics
• Partition by date/source if the data is large

Pipeline approach I’d choose

If you want the most practical setup:

Airbyte or Fivetran → Warehouse → dbt → BI

With:

• Dagster/Airflow/Prefect only if you need orchestration beyond the connector schedules
• S3 as either a source feed or backup/raw archive

When to choose ETL instead

Use ETL only if:

• you need heavy data cleansing before landing
• compliance/security requires filtering before the warehouse
• transformations are expensive and better handled upstream

Small-team vs larger-team guidance

Small team / fast start

• Airbyte/Fivetran
• Warehouse
• dbt
• Metabase/Looker Studio

Larger team / more control

• Custom ingestion where needed
• Orchestrator (Dagster/Airflow)
• dbt
• Warehouse with strong governance
• BI tool

Practical advice

• Model everything around a single source of truth
• Build incremental pipelines
• Keep idempotent loads
• Track data freshness and row counts
• Add data quality checks early

If you want, I can sketch a concrete architecture diagram or recommend a stack based on your team size, budget, and data volume.