Airflow Data Pipelines

Apache Airflow Data Integration

Enterprise clients move large volumes of data between Salesforce and the systems surrounding it — ERP, data warehouses, databases, REST APIs, and flat files. Airflow gives you a programmatic, observable, retry-capable orchestration layer. We design and build the DAGs that make it work reliably at scale.

Why Airflow for Salesforce Integration

Salesforce’s API limits make naive sync approaches brittle at volume. Airflow solves the key challenges:

Bulk API 2.0 — Asynchronous batch jobs that process millions of records without hitting REST API governor limits
DAG-level dependency management — Extract from three systems in parallel, then converge into a single validated, transformed load
Automatic retry with backoff — Transient Salesforce API timeouts and rate limits handled gracefully without manual intervention
Full audit trail — Every task run, input record count, error, and duration logged and visible in the Airflow web UI
Idempotent design — DAGs designed to re-run safely — no duplicate records, no orphaned data on retry

Medallion Architecture for Salesforce Data

We implement the Bronze → Silver → Gold data model to give you clean, query-ready Salesforce data:

Bronze layer — Raw extraction from Salesforce via Bulk API 2.0, stored in S3 or PostgreSQL. No transformations, full fidelity, timestamped. Enables point-in-time replay.

Silver layer — Cleansed, deduped, and typed records. Salesforce IDs resolved to business keys. Null handling, picklist normalization, and field-level validation applied.

Gold layer — Business-ready aggregations: opportunity pipeline by account, case resolution metrics, lead conversion funnels, custom KPIs. Loaded into Snowflake or Redshift for BI tools.

Integration Patterns We Build

Salesforce → Data Warehouse (One-way sync) Full + incremental loads from Salesforce objects (Account, Contact, Opportunity, Case, custom objects) into Snowflake/Redshift, scheduled daily or hourly.

ERP → Salesforce (Master data sync) Account, product catalog, and order data flowing from SAP, NetSuite, or Oracle into Salesforce with field mapping, deduplication, and upsert via External ID.

Bi-directional sync Two-way sync with conflict resolution logic — last-writer-wins, Salesforce-master, or custom merge strategies based on SystemModstamp delta tracking.

API aggregation pipelines Pull from multiple REST endpoints (marketing platforms, payment processors, logistics APIs), normalize, and load into Salesforce as custom objects or activity records.

Event-driven triggers Airflow sensors that watch S3 prefix drops, Salesforce Platform Event queues, or database table watermarks before kicking off downstream tasks.

Deployment Options

Heroku — Airflow on Heroku with a PostgreSQL metadata database and worker dynos. Quick to deploy, scales horizontally, close to Heroku Connect workflows.

AWS MWAA (Managed Airflow) — Fully managed Airflow on AWS. Pairs well with S3, Glue, Lambda, and Redshift in the same VPC. Recommended for teams already on AWS.

Self-hosted — Docker Compose or Kubernetes-based Airflow for teams with existing infrastructure. We handle the CeleryExecutor or KubernetesExecutor setup.

What You Get

DAG codebase in Git with CI/CD pipeline for testing and deployment
Airflow web UI configured with RBAC for your team
Monitoring alerts for task failures, SLA misses, and queue backlog
Runbook documentation covering common failure scenarios
Handoff training so your team can extend and maintain the pipelines

Typical Scale

We’ve built pipelines handling:

50M+ records/day from multi-org Salesforce environments
Sub-30 minute incremental sync latency for time-sensitive data
200+ object types across complex Salesforce data models
5+ source systems feeding a single Salesforce target org

Ready to work together?

Let's discuss how we can help your team.

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