Matillion vs Azure Data Factory: Which Tool Should You Choose?

Do you need a flexible tool that supports both on-premises integration and cloud capabilities? Or are you looking for workflow orchestration features? In that case, Azure Data Factory might be a good fit. On the other hand, if you’re more focused on an intuitive tool that connects various sources with ease, modern SaaS tools like Matillion or Hevo could be the better choice.

In this article, we’ll break down Matillion vs Azure Data Factory to help you figure out which tool is the right fit for your organization. We’ll explore integration options, pricing, monitoring capabilities, AI/ML support, and more.

What is Matillion?

ETL (Extract, Transform, Load) pipelines collect, clean, and load data into a single location for centralized access. Matillion simplifies and automates this process. It offers an intuitive and user-friendly interface for users to easily adopt the tool and build ETL pipelines without writing extensive code.

The best thing about Matillion is that it doesn’t just connect to data warehouses like Snowflake or Redshift. Instead, it offers purpose-built solutions for each platform, including Snowflake, BigQuery, Redshift, and Databricks. It optimizes for each warehouse’s architecture, runs warehouse-specific transformations, and manages resources efficiently (like Snowflake credits or BigQuery slots) to control costs.

Key features of Matillion

Extensive set of pre-built connectors

Matillion includes 150+ pre-built connectors for a wide range of sources, including CRMs, ERPs, marketing platforms, and data warehouses. For anything it doesn’t cover, you can use its custom connector framework to build REST API connections to integrate with any platform.

Low code

When developer resources are limited, Matillion’s low-code interface simplifies building and maintaining ETL pipelines, helping teams use developer time more efficiently. Users can create each pipeline component using dropdowns, checkboxes, and input fields. Once built, the underlying SQL logic is visible, allowing full customization and control.

Error handling

Matillion’s error reporting feature sends real-time alerts to apps like Slack, email, or Google Chat. You can customize logs to include job names, component names, error messages, and more.

Sometimes, when the error comes from a data source rather than the ETL pipeline, the message lacks enough detail to troubleshoot. In that case, Matillion’s Auto Debug feature automatically traces the error and provides more context to simplify troubleshooting.

Reverse ETL

Reverse ETL moves transformed data from the warehouse back into operational tools like CRMs, marketing platforms, and support systems. 

Matillion supports both ETL and Reverse ETL. Its output components send data to external systems, and most pre-built connectors support syncing data back from the warehouse. Moreover, the drag-and-drop interface makes it easy to build Reverse ETL pipelines.

Use Cases

Feature engineering

Matillion automates data cleaning and transformation steps. For example, if you use data from your CRM systems to train churn prediction models, you can build pipelines that collect relevant features, apply transformations, and feed them directly into your machine learning models.

Cloud databases

Matillion offers native integrations for major cloud data platforms like Snowflake, Redshift, BigQuery, and Databricks. It provides custom features for each since it’s purpose-built to optimize performance on those specific platforms.

Live KPI dashboards

Suppose you have critical KPI metrics to track every day or at regular intervals. In that case, you can schedule Matillion jobs to sync your database with source systems as frequently as needed so the downstream KPI dashboards show lively data. 

What is Azure Data Factory?

Azure Data Factory is a cloud-based data integration platform that orchestrates and automates data ingestion and transformation tasks. It enables seamless data movement across different stores and coordinates transformation steps using Azure compute services or on-premises systems. You can monitor and manage workflows through both a user interface and programmatically.

Since it’s tightly integrated with the Azure ecosystem, ADF is a strong choice for orchestrating ETL workflows if your data stack relies on Azure services.

Key features of Azure Data Factory 

Data compression

Azure calls its ETL process a data copy activity. During this activity, ADF can compress data before moving it to the target source. You can choose compression formats like GZip, BZip2, or Deflate directly in the UI for both source and sink datasets. This reduces bandwidth usage and speeds up data transfer.

Integrated security

Azure data factory offers enterprise-grade security features. It seamlessly integrates with Microsoft Entra ID, allowing users to authenticate with unique credentials before providing data access. It also supports role-based access control and integrates with Azure Key Vault to securely store and access secrets and API keys.

Integration runtime

Integration runtimes (IRs) are the compute environments that handle data movement, transformation, and activity execution within Azure Data Factory. Its Azure IR supports cloud-based data integration tasks. ADF also provides a self-hosted IR for data movement between on-premises systems and the cloud.

Use Cases 

Populate Azure data lake

Azure Data Lake is a scalable storage system that handles structured, semi-structured, and unstructured data. It’s commonly used to store large datasets for analytics. Azure Data Factory lets you load big data from client services or online sources into the data lake for later use.

Since ADF integrates seamlessly with other Azure services, it’s the go-to ETL tool if you already use Azure Data Lake for storage.

Loading data to Azure synapse

If you use Azure Synapse for big data analytics or reporting, integrating it with Azure Data Factory is a wise choice. The tool seamlessly loads required data from various sources into Azure Synapse. Many would argue that Synapse can integrate data on its own, but only to some extent. Azure Data Factory supports various cloud environments and SaaS applications, and offers REST API connections, which Synapse lacks.

Custom triggers

Say you need to sync your database whenever an item goes out of stock in the inventory hub. In this case, event-based triggers are a better choice than time-based ones. Time-based triggers may run pipelines unnecessarily or too often. Azure Data Factory integrates seamlessly with Azure Event Grid, allowing you to set up custom event-based triggers. This way, the pipeline runs only when a specific event (like an item going out of stock) occurs.

Azure Data Factory vs Matillion vs Hevo: Detailed Comparison Table

Hevo is another top alternative tool that offers a user-friendly UI for building and maintaining no-code data pipelines. To better illustrate the differences, we’ve included a comparison table for Azure Data Factory vs. Matillion vs. Hevo below.

Matillion vs Azure Data Factory: In-depth Feature & Use Case Comparison

Orchestration and scheduling

Matillion orchestrates ETL processes by automatically coordinating tasks like data extraction, transformation, and loading. You can build each task as a component and schedule them in sequence or trigger them externally using API endpoints. This makes it easy to schedule jobs and integrate with other services through APIs and webhooks.

Azure Data Factory offers powerful orchestration capabilities that go beyond ETL. It can coordinate a variety of data workflows, including movement, transformation, and analysis. It supports different types of triggers, including scheduled triggers, event-based triggers, and even real-time analytics triggers.

Vendor lock-in

Azure Data Factory (ADF) supports 90+ built-in connectors for on-premises and cloud services, but it runs entirely within Azure. While it integrates with external platforms, it requires Azure-specific configuration and familiarity with the ecosystem.

If your data infrastructure is built around Azure, ADF fits well. It integrates tightly with services like Azure Synapse, Blob Storage, and Key Vault, making data movement and orchestration seamless with no extra connectors or custom setups required.

Additionally, ADF is not a separate SaaS tool; it’s fully managed by Azure, so billing flows through your Azure subscription.

Matillion supports major cloud platforms like Snowflake, Amazon Redshift, Databricks, Google BigQuery, and Azure Synapse. It provides built-in ETL capabilities tailored to each platform, so unlike ADF, you can use Matillion with any of your cloud data platforms.

AI/ML capabilities 

Azure offers a suite of AI services, including Azure OpenAI, AI Search, AI Speech, and Azure AI Foundry, that businesses can easily integrate into their applications. Azure Data Factory (ADF) integrates seamlessly with these services, enabling smooth orchestration across data and AI workflows.

ADF also connects natively with Azure Machine Learning (Azure ML). You can automate end-to-end pipelines. For example, you can ingest and transform data in ADF and then trigger an Azure ML pipeline to retrain a model using the updated data.

Matillion does not include built-in AI or ML capabilities and does not natively integrate with any AI platform. However, you can embed Python scripts in pipelines for tasks like inference or scoring or use REST APIs to connect with external ML platforms.

Monitoring and logging

Azure Data Factory provides pipeline and activity monitoring capabilities within the tool. Pipelines monitor the flow of data and orchestration of workflows, while activities monitor individual data movements or transformation steps. That means, along with transformation steps, it also monitors the flow and infrastructure of pipelines. 

ADF integrates natively with Azure Monitor and Log Analytics, allowing you to query logs, set up alerts, and analyze error logs at scale. It also offers a centralized dashboard for real-time visibility into pipeline health and performance.

Azure data factory provides pipelines and activities monitoring capabilities within the tool. Pipelines means it monitors the flow of data and orchestration of workflows while activity means it monitors individual data movements or transformation steps. That means along with transformation steps, it also monitors the flow and infrastructure of pipelines. 

Matillion monitors at the job and task levels. Jobs represent the entire ETL or ELT process, and tasks refer to individual components within that job. Monitoring focuses primarily on whether each transformation or SQL component succeeds. Matillion does not offer a separate, centralized logging service. Instead, it uses inline loggers at each step, which you must inspect manually when a failure occurs.

If you need broad, cloud-scale observability and integration with enterprise monitoring tools, ADF is the stronger option. If your focus is limited to ETL logic and step-level visibility, Matillion’s built-in monitoring is sufficient.

Pricing

ADF offers pay-as-you-go pricing, similar to cloud storage and compute models. It charges per activity for orchestration runs and activity execution, per minute for compute clusters (like vCore) used during transformations, and per read/write Data Factory operation. Since ADF integrates closely with other Azure services, costs can accumulate quickly depending on usage.

Matillion ETL offers four subscription tiers: Developer, Basic, Advanced, and Enterprise. It calculates based on credits. For example, one hour of Virtual Core usage = 1 credit. The Developer tier is a free tier but comes with limited capabilities. 

While Matillion does not publish official pricing, user reviews suggest that the Basic plan costs around $1,000 per month with 500 credits, and the Advanced plan costs about $2,000 per month with 750 credits. The Enterprise edition is custom-priced based on business needs.

Overall, Matillion is cost-efficient for high-volume, predictable workloads, while ADF is affordable when your workflows are deeply integrated within the Azure ecosystem.

Hybrid

Though Azure Data Factory is a cloud-native solution, its self-hosted integration runtime offers hybrid capabilities. It’s a lightweight agent that you install on your private network or on-prem. This agent acts as a bridge between your ADF instance and on-prem data, allowing seamless and secure data flow between your cloud and on-prem servers.

ADF’s pre-built connectors also include connections to on-prem servers like Oracle, SQL Server, PostgreSQL, DB2, file systems, and SAP.

On the other hand, Matillion is a cloud-native solution. It offers hybrid support but has more limitations than ADF. For example, Matillion can connect to on-prem data sources to load data but cannot be deployed on on-premises servers.

When to Choose Matillion?

Choose Matillion when you’re building cloud-native ETL pipelines and want broad support for multiple cloud platforms like Snowflake, Redshift, BigQuery, and Azure Synapse. It’s a strong fit for teams that prefer diverse integration options and value a visual, low-code interface. Matillion also appeals to organizations looking for predictable subscription-based pricing. 

When to Choose Azure Data Factory?

Choose Azure Data Factory if you are already invested in Microsoft Azure services. ADF offers seamless integration with Azure-native tools like Synapse, Blob Storage, Key Vault, and Azure Machine Learning. It’s ideal when you need hybrid support, tight security integration, and enterprise-grade orchestration for complex workflows. 

Why does Hevo stand out?

Hevo is built for ease. It offers an extensive set of pre-built connectors and intuitive drag-and-drop features to integrate various data sources. For teams that want simplicity without sacrificing flexibility in customizing transformation steps, Hevo stands out.

Another highlight is Hevo’s transparent pricing. It follows a tier-based model with some of the most affordable rates, making it especially appealing for mid-sized businesses.

Sign up for Hevo’s 14 day free trial to experience effortless data integration.

FAQs on Matillion vs Azure Data Factory