--- title: "Scale data pipelines - Stacksync" description: "Pipelines that scale with your data, not your headcount, sub-second latency, exactly-once delivery, observability per record." canonical: https://www.stacksync.com/use-case/scale-data-pipelines last_modified: 2026-07-05 --- Pipelines that scale with your data, not your headcount # Scale data pipelines without rebuilding them Move billions of records across systems with sub-second latency, exactly-once delivery, and per-record observability, without rewriting your stack. [Book a demo](https://www.stacksync.com/book-a-demo) [Talk to a solutions architect](https://www.stacksync.com/book-a-demo) ## Adopted by fast-scaling companies moving mission-critical data in real time - Gladia Case study - IDEXX - MedPro Migrated from MuleSoft - Eko Case study - Ubicloud - Vimeo - Codility Migrated from Celigo - ACERTUS Migrated from Heroku Connect - Syringa Migrated from Matillion - Truora - Streaam Case study - SEAL SQ - Rinsed Migrated from Fivetran - IA Capital Group Case study - Meter - Golden Pear Funding Migrated from Celigo Where pipelines break at scale ## Three reasons your data pipeline breaks at the next 10x. The pipeline that worked at 1M records/day starts dying at 100M. Most rebuilds happen for the same three reasons. 01 - Backpressure ### Source systems can't keep up with downstream processing Your warehouse can ingest at 100k records/sec; your CRM API caps at 100/sec. Without intelligent backpressure, queues balloon and the slowest connector starves the rest. THROUGHPUT 02 - Replay & lineage ### When a pipeline drops a window, you lose three days finding the gap Most pipelines log success or failure but not what was processed. Recovering from a partial outage means writing a one-off backfill script, and crossing your fingers that it doesn't double-write. RELIABILITY 03 - Schema evolution ### Every upstream change is a downstream incident When Salesforce adds a field or Stripe deprecates an endpoint, your pipeline silently drops the new data and you find out a week later from a confused dashboard. COUPLING 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](https://www.stacksync.com/book-a-demo) ### [Two-way sync](https://www.stacksync.com/two-way-sync) Changes made in one platform automatically update across all connected systems in real time, eliminating data silos and reducing errors. Replaces: Heroku Connect and Salesforce Data 360 Heroku Connect Salesforce Data 360 ### [Workflow automation](https://www.stacksync.com/workflow-automation) Stop building brittle API scripts. With Stacksync, you can trigger complex automated workflows using simple SQL commands. Replaces: Workato and Boomi ### [AI Agents](https://www.stacksync.com/ai-agents) Expose every enterprise system to your agents through a single MCP layer. Claude, ChatGPT and Gemini get production-grade tools without custom glue code. Natively available in: Claude and ChatGPT and Gemini Claude ChatGPT Gemini ### [Event queues](https://www.stacksync.com/event-queues) Handle massive traffic spikes without losing a single event. Queues buffer your data during surges, ensuring strict ordering and reliable delivery. Replaces: Kafka and Amazon SQS and Confluent Kafka Amazon SQS ### [Database hosting](https://www.stacksync.com/database-hosting) Interact with your CRM, ERP, and payment tools as if they were just another table in your database. Say goodbye to rate limits and complex API documentation. Replaces: AWS RDS and Self-hosted databases AWS RDS Self-hosted databases ### [EDI](https://www.stacksync.com/edi) Transform legacy EDI complexity into simple database interactions. Stacksync automatically parses incoming EDI documents directly into your database tables. Replaces: SPS Commerce and TrueCommerce [Start building now](https://www.stacksync.com/book-a-demo) Architecture ## Built for the operational data plane. Stacksync runs on a streaming-first architecture (Kafka under the hood) with exactly-once semantics, durable retries, and end-to-end OpenTelemetry tracing per record. Sources 05 - Salesforce - HubSpot - Stripe - Postgres - MongoDB Sinks 05 - Snowflake - BigQuery - Databricks - Redshift - S3 Streaming 05 - Kafka - Kinesis - Pub/Sub - Pulsar - Webhook Observability 05 - Datadog - Honeycomb - Grafana - New Relic - OpenTelemetry Every record is traced end-to-end. Replay any window with one click, deduped by exactly-once semantics. [Browse all 1,000+ connectors](https://www.stacksync.com/connectors) SECURITY ## Security teams trust 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, and is DPF-certified for US, EU, UK and CH data transfers. [Learn more about security](https://www.stacksync.com/security) | | | | --- | --- | | | SOC 2 Type II | | | ISO 27001 | | | HIPAA BAA | | | GDPR | | | CCPA | | | DPF US-EU-UK-CH | → 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: [OAuth 2](https://www.stacksync.com/security) [SSH Tunnelling](https://docs.stacksync.com/two-way-sync/connectors/setup-options/ssh-tunneling) [SSL certificates](https://docs.stacksync.com/two-way-sync/connectors/postgres/authorize-postgres/amazon-rds/ensuring-secure-rds-connections-with-ssl-certificate) [IP Whitelisting](https://docs.stacksync.com/two-way-sync/connectors/setup-options/ip-whitelisting) [VPN gateway](https://docs.stacksync.com/two-way-sync/legal/service-consumption-tables#:~:text=%E2%9C%93-,VPN%20gateway,-%2D) [VPC peering](https://www.stacksync.com/security) and more Common questions ## Common questions about scaling data pipelines. Pipeline scale questions are about throughput limits, exactly-once semantics, schema-evolution handling, and replay mechanics. [Talk to a solutions architect](https://www.stacksync.com/book-a-demo) ### What throughput can Stacksync sustain? Production deployments routinely sustain 100k+ records/second per pipeline, with horizontal scaling for higher loads. The platform is tested at 1M+ records/second on stress fixtures. ### How is exactly-once delivery guaranteed? Stacksync uses idempotency keys per record and transactional outbox patterns at sinks. Replays of any window are safe, duplicates are dropped at the sink. ### What happens when an upstream schema changes? Schema changes are detected on every sync. New fields appear automatically; removed or renamed fields trigger a Slack/email alert with the affected mappings, before the next sync runs. ### How do we replay a missed window? Every sync run is durable with a per-record manifest. Click 'Replay' on a window in the UI; Stacksync re-runs the failed records (deduped by idempotency key) without touching the records that succeeded. ## Your last integration took months. Your next one takes a prompt. [Book a demo](https://www.stacksync.com/book-a-demo) [Get started](https://app.stacksync.com/)