Data Pipelines is where you move data from one system into another without writing integration code by hand. You pick a source, pick a destination, decide how the data should be shaped along the way, and let UnifyApps handle the ongoing sync. This page covers what a pipeline is, the stages every pipeline moves through, and where to go next depending on what you're trying to do.
Overview
A pipeline connects a source system, such as a database, SaaS application, file store, or API, to a destination where that data should end up, such as a warehouse, another application, or an internal object. Once configured, it keeps that data flowing on its own — either continuously or on a schedule you define — so you don't need to re-run anything manually.
Every pipeline is built from a small set of decisions: which objects to bring across, how their fields map to the destination, and how often the sync should run. Once those are set, the pipeline handles execution, retries, and keeping you informed if something goes wrong.
What Is a Data Pipeline?
Think of a pipeline as a standing instruction: "take this data, from here, and keep it updated over there." Instead of a one-time export or import, a pipeline is a configured, repeatable data flow that knows where to read from, what to read, how to transform it, and where to write it — and it does this on its own each time it runs.
Pipelines are useful whenever one system needs to reliably reflect data from another: syncing customer records into a warehouse for reporting, pulling support tickets into an internal object for automation, or feeding product usage events into an analytics store. Because the configuration lives in one place, you can see exactly what's flowing where and monitor its health over time.
The Pipeline Lifecycle
Every pipeline moves through the same five stages, whether it's your first one or your fiftieth:
Create — Start a new pipeline and choose the source and destination systems you want to connect.
Configure — Select the objects to sync, map their fields to the destination, and set up any transformations, filters, or upsert keys you need.
Deploy — Publish your configuration so the pipeline goes live, turning your draft setup into an active data flow.
Monitor — Track throughput, latency, and record counts, and investigate any records that failed to sync.
Manage — Pause, resume, restart, or update the pipeline as your needs evolve, and roll back to an earlier version if needed.
You'll revisit these stages often. A pipeline is rarely "set and forget" — sources add new fields, destinations change, and schedules need tuning, so expect to move back into Configure or Manage as things evolve.
Find Your Way Around Data Pipelines
The Data Pipelines section is organized around the lifecycle above. Here's where to go depending on what you're doing:
Pipelines Manager — Your starting point. See every pipeline, its status, and options to create, clone, share, delete, pause, or resume.
Source & Destination — Connect the systems your pipeline reads from and writes to, test the connection, and see why options might be disabled until a connection succeeds.
Pipeline Objects — Choose which source objects to bring in, set file formats, toggle syncs on or off, and apply cursors or record filters.
Schema Mapping & Transformations — Map fields manually or replicate the source schema automatically, handle schema evolution, and apply transformations or upsert keys.
Pipeline Settings — Configure auto-retry, choose real-time versus scheduled syncing, set cron intervals, and control historic versus live ingestion.
Deploy, Restart & Pause — Understand deploy states, the difference between resuming and restarting, and what happens to draft changes when you pause.
Logs & Monitoring — Review latency and throughput, see migration counts per object, and inspect or retry failed records.
Versions — Look back through deploy history, restore an earlier version, or export and import a pipeline's configuration.
Notes
A pipeline must be deployed before it moves data — saving a configuration alone doesn't activate it.
Pausing a pipeline stops new syncs but keeps your configuration intact, so you can resume later without reconfiguring anything.
If a connection looks unavailable, check Source & Destination first — many setup options stay disabled until a connection test succeeds.