In our experience, organizations talk about modernizing their data stack for one of 3 reasons:
- They need to understand their data better
- They want to learn how to make their data work for them (aka increase efficiency and automate tasks)
- They want to reduce costs
But before anything can happen, their data needs to be moving smoothly behind the scenes. And that is where an ETL (Extract, Transform, Load) tool and data pipeline orchestration can help. As the name suggests, an ETL essentially takes all the data from different places – whether that be Excel sheets, Salesforce, SQL databases, on-premise servers, you name it – and delivers it where it needs to go. Usually, that’s a data lake or a data warehouse where analytics and reporting takes place. (Read more about that in our blog post about Databricks!)
What is ADF? How Does it Fit into Data Pipeline Orchestration?
An ETL tool that has been proven to be reliable is Azure Data Factory (ADF). That comes with two caveats:
First, it’s a cloud platform that only runs in Microsoft Azure and not a standalone tool you can deploy on AWS.
And secondly, while it is agnostic in terms of the type of data sources that it connects to, the fact that it can move easily between on-premises and cloud systems, as well as, that it supports multiple file formats – it plays best in a Microsoft ecosystem.
In short, ADF is not just an ETL tool – it’s a data pipeline orchestration service that ensures your data movement, integration, and monitoring are streamlined across multiple systems.
Assuming you know all of that, these are the questions to ask yourself to try and find out if ADF is the right tool for you… It’s not so much about the features as they stand in this moment in time, but about how they fit into a longer company strategy.
Key Considerations for Data Pipeline Orchestration
When evaluating ADF or any other data pipeline orchestration tool, companies often weigh:
- Connectivity – Will it talk to ALL our systems: SaaS apps, databases, APIs, and on-prem?
- Scalability – Will it handle our data today and well into the future?
- Ease of use – Can IT or BI staff run it? Do the right people know how to use it?
- Governance & monitoring – Can we track jobs, handle failures, and pass audits without a lot of effort?
- Total cost of ownership – What’s cheaper long term: licensing one tool, or duct-taping five together?
Now that we have the ‘Key considerations when choosing the right ETL tool’ covered, let’s talk about the signs that indicate you may be ready for ADF.
A quick checklist: How Do You Know You’re Ready for ADF and Data Pipeline Orchestration?
- Data is coming in from too many sources, and it is proving difficult to manually track and make sense of it.
- Hours are being wasted importing data, exporting data and making spreadsheets of it.
- Azure Cloud is on your mind – either your organization has already made the move or is serious about making the move.
- Your on-premises ETL tools are breaking, and unable to handle the data volume anymore. A good example of this is SSIS – a legacy SQL Server Integration System that is great at certain jobs, but not the best as you grow your business and outgrow how much your server alone can handle.
- You have a small to medium size data and information technology team to design the data pipelines and monitor that the ETL is working properly.
Speaking of SSIS – it is OKAY if you still use it. Lots of companies use it (and for good reason!)
This is in no way, shape or form trying to take you away from the comfort of what you know, and what your business needs now.
SSIS (SQL Server Integration Services) is still very common in industries where an SQL Server has been the backbone for almost 3 decades, and where mission-critical ETL jobs have been running reliably for years. And this is for several reasons:
- SQL Server ubiquity – Wherever SQL Server is dominant, SSIS tags along.
- Don’t break what you already know – It works just fine. So, many organizations see no reason to rip out what isn’t broken.
- Cost sensitivity – SSIS comes with SQL Server, so there are no extra licensing fees, which isn’t the case with ADF.
- Compliance in the industry itself – Heavily regulated industries (finance, healthcare, government) have to stay on on-premises solutions because they handle sensitive data. For e.g., Electronic Health Records (EHR) that deal with private health information (PHI) for compliance purposes (HIPAA).
- Risk aversion – A tendency to play safe and stick with what you know.
ADF vs. Legacy Tools in Data Pipeline Orchestration
But we’re again faced with the same challenge: SSIS is on-premises and doesn’t play well with modern cloud strategies. This is where ADF as a data pipeline orchestration platform makes sense. You can lift-and-shift existing SSIS packages into ADF, run them in the cloud, and modernize at your own pace.
That’s exactly why ADF isn’t a competitor to SSIS. It’s the bridge that helps you protect past investments while stepping into the future.
Common questions about ADF and Data Pipeline Orchestration
We’ve put together some questions we’re frequently asked about ADF – ranging from business-focused, technical/operations, migration and integration challenges, and strategic input.
We hope you find them useful. If you find yourself wanting to learn more, reach out to us!
1. Do I need ADF if I already have Power BI / Synapse / Databricks?
Yes. Power BI is for visualization, Synapse is for analytics and warehousing, and Databricks is for data engineering and AI. None of them specialize in data pipeline orchestration. That’s where ADF fits – it makes sure the right data gets into those systems reliably.
2. Will ADF save us money compared to our current ETL/ELT tools?
Often, yes. ADF is pay-as-you-go, so you only pay for what you use. Compared to heavy enterprise tools, it’s usually cheaper, especially if you’re already on Azure. The bigger savings often come from reduced manual effort; no more fragile scripts or endless troubleshooting.
3. Can ADF connect to all our current systems (on-prem + SaaS)?
We’d like to say that it will in most cases. ADF has 100+ built-in connectors for common systems like Salesforce, SAP, Dynamics, Oracle, and SQL databases. It also works with REST APIs for custom integrations. For on-prem systems, ADF uses a self-hosted integration runtime to move data securely between your data center and Azure.
4. What’s the difference between using ADF for transformations vs. Databricks?
ADF transformations: Visual, low code “mapping data flows.” Good for simpler, rule-based transformations (e.g., reformatting dates, merging tables). Use ADF when you want quick, no-code data prep.
Databricks transformations: Code-first, high-performance, distributed across clusters. Needed for heavy data engineering, real-time streaming, or advanced ML/AI. Use Databricks when you need a sophisticated set up.
Together, they complement each other under a data pipeline orchestration strategy.
5. Do we need a team of data engineers, or can BI/IT staff manage ADF?
One of ADF’s strengths is that it’s approachable. BI or IT staff can build and manage pipelines through its visual interface. For more complex pipelines, a data engineer helps – but you don’t need a big engineering team to get value from ADF.
6. Does ADF integrate with Microsoft Fabric, Synapse, or Databricks?
Yes. ADF pipelines can load into Synapse for analytics, trigger Databricks notebooks for transformations, and now integrate directly into Microsoft Fabric, so you can manage everything in one workspace.
7. If we adopt ADF now, what does it set us up for in the future (AI, real-time analytics)?
Reliable pipelines are the foundation for everything else. With ADF in place, you’re ready to:
- Feed real-time data into dashboards
- Prepare data for Databricks ML/AI models
- Power Microsoft Fabric-based analytics
- And scale to hundreds of data sources without breaking your workflows
Future-Proofing with Data Pipeline Orchestration
Reliable pipelines are the foundation for AI, real-time analytics, and innovation. With ADF as your data pipeline orchestration solution, you’re set up to:
- Feed real-time data into dashboards
- Prepare data for AI/ML in Databricks
- Power Fabric or Synapse analytics
- Scale across hundreds of data sources
In conclusion…
Azure Data Factory (ADF) is more than an ETL tool — it’s a data pipeline orchestration service that keeps data movement reliable, secure, and scalable. It helps to get these basics right, as it sets organizations up for bigger ticket items like AI, and advanced analytics.
It’s natural to be worried about running on SSIS, but it’s not a make or break. We’ve worked with organizations that were hesitant about making the move to the cloud and were comfortable about staying on their SQL server. If that is you, we are happy to be a sounding board for you and your team. If you are worried about ADF integrating with your visualization tools, or how it will fit into your larger tech stack that you’re trying to build, we can talk that through with you, too.
Bottom line: We know it can be pretty overwhelming to not know where or even IF to start moving to ADF. But, when you get the foundation in place, everything else – analytics, AI, and innovation – becomes that much easier.