Comparison

ADF vs Polysync: When ADF Stops Being Enough

A pragmatic comparison for teams already invested in Azure Data Factory.

Start With What ADF Does Well

Azure Data Factory is a strong data integration service. Within a single factory, it gives you visual pipelines, a large library of connectors, parameterised datasets and linked services, tumbling-window and schedule triggers, and an integration runtime that can reach hybrid sources. For a team whose data lives inside Azure, ADF is often the right primary tool and is not something Polysync attempts to replace.

The conversation gets interesting when ADF stops being the only thing in the room.

Where ADF's Orchestration Model Runs Out

ADF's scheduling and dependency primitives are designed around the assumption that the work being coordinated is also in ADF. Once the assumption breaks, three gaps appear quickly.

Dependencies stop at the factory boundary. ADF activity dependencies (dependsOn) only resolve between activities inside the same pipeline. Triggering one pipeline from another is possible via the Execute Pipeline activity, but a Databricks job kicked off elsewhere, a Cloud Function on GCP, or an external arrival event cannot participate as a first-class node in an ADF dependency graph.
Triggers fire from a clock, not from a graph. Schedule and tumbling-window triggers know when to run, but they do not natively express "run this pipeline only after that unrelated workload finishes successfully on another platform." Teams typically bridge that gap with custom code, a queue, or a third system that polls.
Concurrency control is per-pipeline, not per-platform. ADF lets you cap a single pipeline's concurrent runs. It does not give you a global budget that says "no more than 8 things running against this Databricks workspace, across all sources." When the same Databricks cluster is being driven by ADF, a notebook job, and an ad-hoc workflow, that budget has to live somewhere outside ADF.

What Polysync Adds Around ADF

Polysync is a control plane that treats ADF pipelines as first-class nodes in a larger dependency graph. It does not run the transformations itself; ADF still does that. Polysync decides when ADF runs, what runs alongside it, and what waits for it.

Cross-platform DAG. An ADF pipeline can depend on a Databricks job, which can depend on a Synapse pipeline, which can depend on a Google Cloud Function. Trigger conditions (on success, on failure, on completion, on skipped) are resolved recursively across the whole graph.
Auto-discovery of pipelines. Polysync connects to your factory and discovers existing pipelines, exposing them as Jobs you can configure into Tasks. You are not retyping pipeline names or parameters.
Parameter mapping across platforms. The output of an ADF pipeline (a row count, a file path, a status) can be mapped into the parameters of a downstream task on a different platform without writing a custom passthrough.
Unified concurrency and rate limits. Concurrency profiles use leaky-bucket and rolling-window controls scoped per platform connection. An ADF run, a Databricks job, and a Logic App invocation can each contribute to the same budget when they target the same downstream resource.
One monitoring view. Every run across every connected platform shows up in a single dashboard with status, duration, and history; you do not bounce between ADF Monitor, Databricks job runs, and Logic Apps run history to assemble the picture.
Standard cron with a visual builder. Schedules attach to root tasks and are expressed in standard cron syntax with timezone awareness. Multiple schedules per task are supported; tumbling windows are not the only available shape.

Side by Side

ADF on its own

Dependencies inside a pipeline, not across platforms
Per-pipeline concurrency, no global budget
Triggers driven by clock or window, not graph state
Monitoring scoped to the factory
Hand-rolled glue when work spans GCP or other services

ADF with Polysync

ADF pipelines as nodes in a cross-platform DAG
Concurrency budgets that span all connected platforms
Triggers driven by upstream task state, with trigger conditions
One dashboard across Azure, Google Cloud, and AWS services
Parameters mapped between tasks without bespoke code

Will I Still Need ADF?

Yes. Polysync does not move bytes. If you use ADF today to copy from on-prem SQL into ADLS, that activity is still in ADF and still uses your integration runtime. Polysync calls the ADF pipeline, waits for it, captures its result, and decides what runs next.

When ADF Alone Is Still the Right Answer

If every pipeline you operate lives inside one ADF instance, your dependencies are simple, and you have no plans to add Databricks, Functions, Logic Apps, Synapse, Fabric, or Google Cloud services into the same flow, ADF's built-in scheduling is fine. Adding a separate orchestrator before you need one is overhead, not insurance.

The point at which most teams cross the line is the second platform: the first Databricks job, the first Cloud Function, the first AKS workload that needs to coordinate with an ADF pipeline. That is the moment a dedicated orchestrator starts paying for itself.