Skip to content
Resolve, revert, replay — without re-architecting
  1. Home /
  2. Teams /
  3. Data

Data teams
monitor every sync in real time

Sync the warehouse to operational systems, monitor every record in flight, and replay any failure — without rebuilding the platform.

Adopted by fast-scaling companies moving mission-critical data in real time

Case study
Migrated from Mulesoft
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
What data teams fight every sprint

Three reasons your data platform spends more time on integrations than analytics.

The hardest part of running a modern data stack isn't the warehouse — it's everything that has to flow into and out of it without breaking the rest of the company.

01 — Pipeline fragility

One schema change upstream breaks five jobs downstream

Every API change at Salesforce, NetSuite, or Stripe ripples through ingestion, transformation, and reverse-ETL. Without contract testing across the chain, breakages show up as missing rows in dashboards on Monday morning.

RELIABILITY
02 — Reverse-ETL drift

Models in the warehouse, decisions in the CRM

You ship a churn-risk model — and it lands in the warehouse, not the seller's view. Bolting on a reverse-ETL tool adds cost, latency, and another point of failure to monitor.

DATA QUALITY
03 — Incident response

Sync failures are invisible until someone complains

When a Stripe-to-Snowflake job silently drops a day of records, you find out from finance, not from your monitoring. Replaying the missed window means a custom backfill script and crossing your fingers.

OBSERVABILITY
PLATFORM

Six products. One Platform.
Replace many legacy vendors.

Every tool Stacksync replaces is one fewer vendor, one fewer bill, one fewer integration to maintain.

Start building now
Start building now
Connectors

Every system that produces or consumes operational data.

Stacksync covers ingestion, sync, reverse-ETL, and event streaming on a single set of connectors — with end-to-end observability per record.

Warehouses & lakes
05
  • Snowflake
  • BigQuery
  • Databricks
  • Redshift
  • ClickHouse
Operational databases
05
  • PostgreSQL
  • MySQL
  • MongoDB
  • DynamoDB
  • Cosmos DB
Streaming & events
05
  • Kafka
  • Kinesis
  • Pub/Sub
  • Pulsar
  • Webhook
Source systems
05
  • Salesforce
  • HubSpot
  • Stripe
  • NetSuite
  • Shopify
Every sync emits OpenTelemetry traces — drop them into Datadog, Honeycomb, or Grafana.
Browse all 1,500+ connectors
SECURITY

Security teams love Stacksync

As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer.

SOC 2 type II
ISO 27001
HIPAA BAA
GDPR
CCPA
DPF US, EU, UK, CH
CSA STAR
→ SECURITY WITH BENEFITS

SSO & SCIM

Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.

Alerts

Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.

Secure connection options

Securely connects to your systems with:

How does Stacksync compare to Fivetran + Hightouch?

Fivetran handles ELT in; Hightouch handles reverse-ETL out — Stacksync is one platform doing both, plus event-driven sync, on the same connectors. One contract, one observability surface, one place to debug.

Can we replay a failed sync window?

Yes. Every sync window is durable — Stacksync stores a manifest of every record processed and the outcome. Failed windows replay with one click; partial replays (only the failed records) are also supported.

What about CDC from Postgres to the warehouse?

Stacksync supports logical replication (Postgres, MySQL) and change-feed APIs (MongoDB, DynamoDB, Cosmos DB) natively. Latency is sub-second for in-region targets.

How does observability work?

Every sync emits OpenTelemetry traces (span per record), metrics (sync rate, failure rate, lag), and structured logs. Drop them into your existing observability stack — Datadog, Honeycomb, Grafana, New Relic — via OTLP.

Coworkers laughing in front of a laptop in a casual office setting

Your last integration took months.
Your next one takes a prompt.