Move data on a schedule that recovers itself.

Salesforce API limits make naive syncs brittle the moment volume arrives. Airflow gives the job a dependency graph, retry with backoff, and an audit trail of every run. We write the DAGs idempotent — a re-run is always safe — and hand you the repository with the CI that deploys it.

Book a source audit →All eight engagements
Shape
Fixed-scope project
Typical length
6–10 weeks
Priced
Fixed scope, per pipeline
Starts with
A source audit
You keep
DAG repository, CI/CD, alerts, runbook
Proven by
AlgoBridge

Ten weeks, bronze to gold.

Drawn to scale
01Source auditWeek 1Which systems, which objects, what volume, and how fresh the data actually has to be.
02Bronze — raw extractWeeks 2–4Bulk API 2.0 into S3 or PostgreSQL. No transformation, full fidelity, timestamped for point-in-time replay.
03Silver — cleansedWeeks 5–6Deduped and typed. Salesforce IDs resolved to business keys, picklists normalised, field validation applied.
04Gold — business-readyWeeks 7–9Pipeline by account, case resolution, conversion funnels — loaded into Snowflake or Redshift for the BI tools you already have.
05HandoverWeek 10DAG repository with CI/CD, RBAC configured, failure and SLA alerts wired, runbook written.
Drawn at the long end: ten weeks. Six is the same five stages with fewer objects in each layer — the audit and the handover do not shrink. Every layer is idempotent, so a re-run is always safe.

What the pipeline work covers.

Six areas

Most pipelines need the first few of these. The last ones are about how it runs once it is yours, which is the half that decides whether you call us again.

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

The handover

What you are left holding.

A pipeline is only as good as the morning somebody else has to fix it. These four are what make that possible without us.

See what every engagement ends with
  1. The DAG repositoryEvery DAG, every operator, every test, with the CI that deploys them — in your git.
  2. The access modelAirflow RBAC configured per team, and every credential rotated out of the code into a secrets backend.
  3. The alertsFailure and SLA-miss alerts wired to wherever your team already looks, not to a dashboard nobody opens.
  4. The runbookHow to re-run a day, how to backfill, and why every task is safe to run twice.

Tell us what your org is doing wrong.

We will tell you whether it needs a project, a retainer, one of our products, or nothing at all. The call costs you half an hour.

Book a consultation →