Azure Data Factory Reviews & Product Details

Azure Data Factory (ADF) is a fully managed, serverless data integration service designed to simplify the process of ingesting, preparing, and transforming data from diverse sources. It enables organizations to construct and orchestrate Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) workflows in a code-free environment, facilitating seamless data movement and transformation across on-premises and cloud-based systems. Key Features and Functionality: - Extensive Connectivity: ADF offers over 90 built-in connectors, allowing integration with a wide array of data sources, including relational databases, NoSQL systems, SaaS applications, APIs, and cloud storage services. - Code-Free Data Transformation: Utilizing mapping data flows powered by Apache Spark™, ADF enables users to perform complex data transformations without writing code, streamlining the data preparation process. - SSIS Package Rehosting: Organizations can easily migrate and extend their existing SQL Server Integration Services (SSIS) packages to the cloud, achieving significant cost savings and enhanced scalability. - Scalable and Cost-Effective: As a serverless service, ADF automatically scales to meet data integration demands, offering a pay-as-you-go pricing model that eliminates the need for upfront infrastructure investments. - Comprehensive Monitoring and Management: ADF provides robust monitoring tools, allowing users to track pipeline performance, set up alerts, and ensure efficient operation of data workflows. Primary Value and User Solutions: Azure Data Factory addresses the complexities of modern data integration by providing a unified platform that connects disparate data sources, automates data workflows, and facilitates advanced data transformations. This empowers organizations to derive actionable insights from their data, enhance decision-making processes, and accelerate digital transformation initiatives. By offering a scalable, cost-effective, and code-free environment, ADF reduces the operational burden on IT teams and enables data engineers and business analysts to focus on delivering value through data-driven strategies.

Seller

Microsoft

Discussions

Azure Data Factory Community

Value at a Glance

Averages based on real user reviews.

Time to Implement

3 months

Perceived Cost

$$$$$

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Azure Data Factory Integrations

(6)

Integration information sourced from real user reviews.

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Asad A.

Data Engineer

Computer Software

Enterprise (> 1000 emp.)

1/30/2026

"Azure Data Factory: Enterprise-Grade Orchestration with Practical, Reusable Pipelines"

5/5

What do you like best about Azure Data Factory?

What I like best about Azure Data Factory (ADF) is that it hits a really nice spot between enterprise-grade orchestration and day-to-day practicality. A few highlights that genuinely stand out:

Orchestration without friction:

ADF is excellent at coordinating many moving parts: Blob storage, ADLS Gen2, Azure SQL, Synapse, Databricks, Functions, REST APIs, without forcing you to glue everything together manually. Pipelines, triggers, and dependencies feel very natural once you’ve used them a bit.

Clear separation of concerns:

The way ADF separates control flow (pipelines, activities, triggers) from data processing (Spark, SQL, Functions) is a big win. It doesn’t try to be the compute engine; it just orchestrates things cleanly and lets the right service do the heavy lifting.

Parameterization & reusability:

Pipelines, datasets, and linked services are highly parameterizable. This makes it easy to build config-driven ingestion frameworks (single pipeline, many sources), which scales really well in real enterprise setups.

First-class integration with the Azure ecosystem:

Native integration with Synapse, Key Vault, Event Grid, Functions, and Managed Identity is a huge plus. Secure access without secrets flying around is something you really appreciate in production.

Operational visibility:

Monitoring, retry logic, failure paths, and alerts are built in. When something breaks at 3 a.m., you can quickly see what failed, why, and with what input, without digging through logs for hours.

Low barrier to entry, high ceiling:

You can start with simple copy pipelines very quickly but still grow into advanced patterns: metadata-driven pipelines, conditional branching, dynamic execution, and hybrid solutions with Spark or Functions. Review collected by and hosted on G2.com.

What do you dislike about Azure Data Factory?

ADF is an excellent tool at orchestration, but it can feel clunky for debugging, complex logic, and large-team development. Review collected by and hosted on G2.com.

What problems is Azure Data Factory solving and how is that benefiting you?

Azure Data Factory (ADF) mainly solves the problem of coordinating complex data workflows across many systems, and that has been a big productivity win for me.

What problems ADF solves?

Disparate data sources: In real projects, data lives everywhere: cloud storage, databases, APIs, SaaS tools. ADF provides a single, consistent way to connect to and move data between all of them.

Workflow orchestration complexity: Managing dependencies, schedules, retries, and failure handling across multiple jobs is hard. ADF centralizes this logic, so pipelines run in the right order with clear control flow.

Environment and security management: Handling credentials, secrets, and access securely can get messy. ADF’s native integration with Managed Identity and Key Vault simplifies this significantly.

Operational visibility: Without orchestration, it’s difficult to know what ran, what failed, and why. ADF gives built-in monitoring, logging, and alerting for data pipelines.

Scalability and reliability: As data volumes and pipeline counts grow, manual or script-based approaches don’t scale well. ADF provides a managed, scalable service without needing to operate infrastructure.

How this benefits me?

I spend less time on glue code and scheduling logic, and more time on actual data modeling and transformation.

Pipelines are repeatable, auditable, and production-ready, which is critical in enterprise environments.

Failures are easier to detect and recover from, reducing operational overhead.

I can design config-driven, reusable pipelines instead of one-off jobs, which improves maintainability and team collaboration. Review collected by and hosted on G2.com.

Dinesh G.

Lead Digital Cloud Architect - Hybrid Cloud

Small-Business (50 or fewer emp.)

11/7/2025

"Effortless Data Integration and Automation with Azure Data Factory"

4/5

What do you like best about Azure Data Factory?

What’s great about ADF is how easy it makes connecting all my different data sources whether they’re on-prem or in the cloud without having to write a lot of code. I can visually drag and drop to build complex data pipelines, automate repetitive data tasks, and seamlessly move and transform huge volumes of data as my projects grows. I can also monitor and manage everything from one place, which takes a lot of stress out of dealing with data integration and makes life much simpler for everyone involved. Review collected by and hosted on G2.com.

What do you dislike about Azure Data Factory?

The things I dislike most about ADF are usually around its complexity and limitations. Debugging pipelines is often tricky and can be frustrating, especially when something fails, there aren’t many tools for step-by-step troubleshooting. If my workflow is complex, managing and understanding everything can get overwhelming with so many moving pieces and screens to keep track of. Additionally, its integration with non-Azure services is quite limited, so I may run into roadblocks if I need to connect other platforms or work in a Azure cloud setup. And while it handles simple tasks well, customizing things beyond standard connectors or data flows often means writing extra code or finding difficult workarounds. Review collected by and hosted on G2.com.

What problems is Azure Data Factory solving and how is that benefiting you?

ADF helps to solve the headache of moving, combining, and transforming data from all sorts of different sources like databases, spreadsheets and cloud storage into one place where it’s actually useful. Instead of wasting my time setting up manual processes or dealing with multiple tools, I can automate everything and track it from a single dashboard. For me, this means less busywork, fewer errors, and a much easier time getting clean, reliable data for reporting or analysis, so I can focus more on my projects goals and less on chasing down technical hassles. Review collected by and hosted on G2.com.

Abhishek B.

System Engineer

Enterprise (> 1000 emp.)

1/13/2026

"Effortless Data Integration and Pipeline Management with Azure Data Factory"

4.5/5

What do you like best about Azure Data Factory?

A good things about Azure Data Factory is its easy integration with strong integration with veriety of data sources. Building pipelines are the easiest one, even for the complex workflows and monitoring data movement and failure is very convenient for day to day work. Review collected by and hosted on G2.com.

What do you dislike about Azure Data Factory?

The only thing looks difficult about Azure Data Factory is debugging is not very user friendly sometimes. The errors/ issues are not clear always and complex pipeline become too difficult to manage. Ratio of pricing is depends on how optimised the pipeline used. Review collected by and hosted on G2.com.

What problems is Azure Data Factory solving and how is that benefiting you?

Azure Data Factory coviently resolves issue of data integration and problems by moving and transforming data flow between different systems. Also it's benefits me by saving manual works, improving data readability and scheduled pipelines always available on time without any manual intervention for reporting and analytics for business. Review collected by and hosted on G2.com.

Daniel H.

Cloud Enginner

Telecommunications

Small-Business (50 or fewer emp.)

1/9/2026

"Effortless Data Integration with Powerful Monitoring and Intuitive Transformations"

4.5/5

What do you like best about Azure Data Factory?

It enables scalable data integration between on-premises and cloud sources without intensive infrastructure management. Mapping data flows make complex transformations efficient and intuitive. I also appreciate the monitoring and control features, which provide clear visibility into pipeline executions. Review collected by and hosted on G2.com.

What do you dislike about Azure Data Factory?

Debugging and error messages are not always clear, which can make troubleshooting time-consuming. Pricing can also be difficult to estimate, as the serverless model relies on activity execution and data movement. Review collected by and hosted on G2.com.

parth p.

Senior Cloud Engineer

Computer Software

Enterprise (> 1000 emp.)

1/28/2026

"Powerful, Scalable Data Integration with Azure Data Factory"

4.5/5

What do you like best about Azure Data Factory?

Azure data factory is very powerful when it comes to data integration at scale from data sources like Azure blob storage and Azure SQL databases. The ability to create and run pipeline from SDK is also a very good feature to have implementation. Review collected by and hosted on G2.com.

What do you dislike about Azure Data Factory?

The private networking setup is bit convoluted from administration standpoint. we had issues initially making Azure data factory working privately with rest of our Azure native resources. Review collected by and hosted on G2.com.

Verified User in Information Technology and Services

Small-Business (50 or fewer emp.)

1/11/2026

"Effortless Data Integration with Powerful Automation and Scalability"

4.5/5

What do you like best about Azure Data Factory?

Its ability to orchestrate and automate complex data integration workflows at scale. Its low-code, visual interface makes it easy to build and monitor pipelines, while its seamless integration with a wide range of data sources—both on-premises and cloud—enables reliable end-to-end data movement. Additionally, features like built-in scheduling, monitoring, error handling, and integration with other Azure services make it a robust and scalable solution for enterprise data engineering needs. Review collected by and hosted on G2.com.

What do you dislike about Azure Data Factory?

While Azure Data Factory is a powerful orchestration tool, some limitations include limited debugging capabilities for complex pipelines and the lack of advanced transformation features compared to dedicated data processing engines like Spark. Version control and CI/CD integration can also feel less intuitive, especially for large teams, and troubleshooting performance or runtime issues can sometimes require switching between multiple monitoring views. Additionally, costs can increase as pipelines grow in complexity and frequency, requiring careful optimization and governance. Review collected by and hosted on G2.com.

What problems is Azure Data Factory solving and how is that benefiting you?

Azure Data Factory solves the problem of integrating data from diverse sources and orchestrating complex data workflows in a reliable and scalable way. It simplifies data ingestion, scheduling, and dependency management across cloud and on-premises systems, reducing manual effort and operational overhead. For me, this means faster pipeline development, consistent and repeatable data loads, better visibility into data movement through built-in monitoring, and the ability to focus more on data modeling and analytics rather than managing infrastructure or custom integration logic. Review collected by and hosted on G2.com.

Nishant A.

Cloud Architect

Civil Engineering

Enterprise (> 1000 emp.)

1/7/2026

"Essential for ETL and Data Ingestion with Seamless Integration"

5/5

What do you like best about Azure Data Factory?

Azure Data Factories have been heavily implemented in our Organization and most of them are used for ETL/ELT, parsing data, etc

I use the connectors for storage i.e. Blob and ADLS connector for data ingestion and data lake architecture. Review collected by and hosted on G2.com.

What do you dislike about Azure Data Factory?

ADFs have latency hence real time streaming is an issue. Review collected by and hosted on G2.com.

AMRUTRAJ H.

Data Engineer

Enterprise (> 1000 emp.)

10/31/2025

"Flexible Scheduling, But Monitoring View Needs Improvement"

5/5

What do you like best about Azure Data Factory?

Schedules and various types of schedules available in ADF. Review collected by and hosted on G2.com.

What do you dislike about Azure Data Factory?

We can’t see all our pipeline activities in single page while monitoring. Also data gets erased after 50 days Review collected by and hosted on G2.com.

Shirisha D.

Infra transformation sr analyst

Enterprise (> 1000 emp.)

10/7/2025

"Powerful , cloud native ETL made simple"

4.5/5

What do you like best about Azure Data Factory?

It's low code interface and integration flexibility is what I like best about azure data factory. You can connect to a wide range of data sources and create complex data pipelines visually without writing heavy code Review collected by and hosted on G2.com.

What do you dislike about Azure Data Factory?

The debugging experience on the portal can be slow especially for large data flows. Also data flow mapping UI could be more responsive. Review collected by and hosted on G2.com.