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Azure Data Factory

Azure Data Factory

Overview

What is Azure Data Factory?

Microsoft's Azure Data Factory is a service built for all data integration needs and skill levels. It is designed to allow the user to easily construct ETL and ELT processes code-free within the intuitive visual environment, or write one's own…

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Learn from top reviewers

The go-to ETL tool for most situations

Rating: 9 out of 10
September 14, 2023
IncentivizedVetted ReviewVerified User
Data Integration: We harness Azure Data Factory's capabilities to move data from various sources – both on-premises databases and cloud storage – into our Azure data storage s...
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Verified user
C-Level Executive
Azure Data Factory3 years of experience

One of the best and reliable ETL & ELT platforms for pulling data from multiple sources

Rating: 10 out of 10
November 12, 2022
IncentivizedVetted ReviewVerified User
One of the best Data Integration tools for both ETL and ELT. I have been using ADF for the last 6+ years and it helped me in extracting several data feeds within our organizat...
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Rajarshi Maitra PMI™- ASM®, ACP® and CAPM®
Director - Information Technology
Director/Client Engagement Leader- P&C Insurance (Digital Transformation)
TAPsDIGITal
Azure Data Factory7 years of experience

Azure Databricks

Rating: 9 out of 10
June 3, 2022
IncentivizedVetted ReviewVerified User
Orchestration platform for the Databricks notebooks. Have used an ETL for loading csv files into SQL server based database.
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Gorthy Rohith
Professional - Information Technology
Big Data Developer
Suncor
Azure Data Factory2 years of experience

Database management and ETL tool for big data that is smart and reliable

Rating: 9 out of 10
June 5, 2021
IncentivizedVetted ReviewVerified User
Get full data integration at scale with Microsoft Azure Data Factory's management service for serverless data integration,‌ hybrid data integration is easily and agilely possi...
Continue reading
Niloofar Keshvari Nia
Administrative Assistant - Information Technology
IT Operations and Support Specialist
AZEREX LLC.
Azure Data Factory1 year of experience

ADF is awesome!

Rating: 10 out of 10
January 20, 2021
IncentivizedVetted ReviewVerified User
I've used it to perform PoC's and work with data transformation processes that interact with other applications or tools.
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Marco Urrea
Engineer - Information Technology
DevOps Engineer
DigitalOnUs
Azure Data Factory3 years of experience
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Awards

Products that are considered exceptional by their customers based on a variety of criteria win TrustRadius awards. Learn more about the types of TrustRadius awards to make the best purchase decision. More about TrustRadius Awards

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Pricing

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Azure Data Factory

Azure Data Factory

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What is Azure Data Factory?

Microsoft's Azure Data Factory is a service built for all data integration needs and skill levels. It is designed to allow the user to easily construct ETL and ELT processes code-free within the intuitive visual environment, or write one's own code. Visually integrate data sources using more than…

Entry-level set up fee?

  • No setup fee

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services

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Features

Data Source Connection

Ability to connect to multiple data sources

9
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Data Transformations

Data transformations include calculations, search and replace, data normalization and data parsing

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Data Modeling

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Product Details

What is Azure Data Factory?

A fully managed, serverless data integration service that enables users to visually integrate data sources with more than 90 built-in, maintenance-free connectors. Users can construct ETL (extract, transform, and load) and ELT (extract, load, and transform) processes, code-free or optionally with code. Integrated data can then be sent to Azure Synapse Analytics to unlock business insights.

Azure Data Factory Technical Details

Operating SystemsUnspecified
Mobile ApplicationNo

Frequently Asked Questions

Microsoft's Azure Data Factory is a service built for all data integration needs and skill levels. It is designed to allow the user to easily construct ETL and ELT processes code-free within the intuitive visual environment, or write one's own code. Visually integrate data sources using more than 80 natively built and maintenance-free connectors at no added cost. Focus on data—the serverless integration service does the rest.

Reviewers rate Connect to traditional data sources and Connecto to Big Data and NoSQL and Simple transformations highest, with a score of 9.

The most common users of Azure Data Factory are from Enterprises (1,001+ employees).
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Comparisons

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Reviews From Top Reviewers

View all reviews
(1-5 of 7)

Simple Solution to Data Migration

Rating: 8 out of 10
Incentivized
June 22, 2021
Verified User
Professional in Information Technology
Information Technology & Services Company, 201-500 employees
Vetted Review
Verified User
Azure Data Factory
5 years of experience
Use Cases and Deployment Scope
We use Azure Data Factory to orchestrate ETL and ELT pipelines in our projects and it has a wide range of data connectors right from classic(FTP) and modern data lake storage like ADLS and AWS S3. Using the Dataflow and SSIS integration runtime server, it can perform complex transformations without the need for another tool. Executing and Monitoring ETL loads the data factory is very simple and user-friendly.
Pros
  • Orchestration
  • On premises support
  • Support to vast no of data connectors
  • Cloud migration
Cons
  • Native transform functions missing.
  • Pricing
  • Limited trigger functions.
Likelihood to Recommend
Azure Data Factory can perform better with Azure services and can easily do cloud migrations from on-premises services like SQL Server. It has a limited set of functionalities to transform data using SSIS integration services and data flow.

Azure Data Factory - Don't Abandon SSIS Just Yet

Rating: 7 out of 10
Incentivized
July 24, 2020
Verified User
Professional in Information Technology
Architecture & Planning Company, 1001-5000 employees
Vetted Review
Verified User
Azure Data Factory
1 year of experience
Use Cases and Deployment Scope
Azure Data Factory, particularly V2, offers a good option as a cloud-based ETL tool if you are leveraging the Azure cloud. We are using it as we begin to hybridize our on-prem data warehouse and applications with Azure. Up until now, we have leveraged SSIS for these purposes, but are beginning to migrate ETL and other data movement functions to the cloud, with Azure Data Factory as the primary utility.
Pros
  • Easy to set up and get started.
  • Runtimes make integration with on-prem data simple and also allow for support of existing investments in SSIS.
Cons
  • Limited source/sink (target) connectors depending on which area of Azure Data Factory you are using.
  • Does not yet have parity with SSIS as far as the transforms available.
Likelihood to Recommend
If you are just getting started and all your data is resident in the Azure cloud, then Azure Data Factory is likely to work fine without having to jump through too many hoops. However, in a hybrid environment (which is most of them these days), ADF will likely need a leg up. It works well for scheduling and basic scheduling/orchestration tasks, but the feature set is not at a level with SSIS (which has been around for 15 years so...). As ADF now supports deploying SSIS, it is also a good candidate if large amounts of your data are resident in the Azure cloud and you have an existing SSIS investment in code and licensing. We are using it in a hybrid fashion for the data warehouse and will slowly transition over to ADF as the feature set improves. We are also using it for cloud-native applications that only require supplemental data from on-prem resources.

Database management and ETL tool for big data that is smart and reliable

Rating: 9 out of 10
Incentivized
June 05, 2021
NK
Niloofar Keshvari Nia
IT Operations and Support Specialist
AZEREX LLC. (Information Technology & Services, 1-10 employees)
Vetted Review
Verified User
Azure Data Factory
1 year of experience
View profile
Use Cases and Deployment Scope
Get full data integration at scale with Microsoft Azure Data Factory's management service for serverless data integration,‌ hybrid data integration is easily and agilely possible through this software. We are creating ETL and ELT workflows as well as orchestrating and monitoring pipelines without writing any code. Full management and serverless integration with default features installed on the system and various connectors reduce costs. It is used in all company departments and project management units of our customers. Since ADF adopts an intelligent intent-driven mapping methodology, it enables copy activities to be automated.
Pros
  • Creating ETL and ELT workflows as well as orchestrating and monitoring pipelines without writing any code.
  • Hybrid data integration is easily and agilely possible through this software.
  • It has lot of various useful components
Cons
  • It should integrate more ETL and audit functionality.
  • Pipelines lack flexibility because moving Data Factory pipelines between different environments, such as for development or testing, require increased security and flexibility.
  • The number of pre-defined templates is small and they should have more variety.
Likelihood to Recommend
If you know a bit about database management everything is pretty easy, based on my personal experience. You can build a lot of things entirely in design, even if you do not know all the syntax, [since] having ready-made templates simplifies everything to get started and build the first pipeline.

One of the best and reliable ETL & ELT platforms for pulling data from multiple sources

Rating: 10 out of 10
Incentivized
November 12, 2022
Use Cases and Deployment Scope
One of the best Data Integration tools for both ETL and ELT. I have been using ADF for the last 6+ years and it helped me in extracting several data feeds within our organization that meets our specific business needs. The tool provides many features such as Move and Transform, Data explorer, Azure Functions, Data bricks, Data Lake Analytics, Blob Storage, Linked services, Machine Learning, and Power Query.
Pros
  • It allows copying data from various types of data sources like on-premise files, Azure Database, Excel, JSON, Azure Synapse, API, etc. to the desired destination.
  • We can use linked service in multiple pipeline/data load.
  • It also allows the running of SSIS & SSMS packages which makes it an easy-to-use ETL & ELT tool.
Cons
  • For complex JSON when it comes to mapping nested attribute it's not easy to flatten out
  • Data Factory V1 does not have a good implementation experience as compared to V2
  • Work with on premise solutions sometimes is not too friendly because you will need to set a VPN
Likelihood to Recommend
In a data pipeline, you will be able to add different kinds of activities for example connect from your on-premise SFTP and move CSV files to storage accounts. As well data factory has its own data flow if you are an ETL developer who experimented with maybe you have worked with SSIS, thus, you will start quickly with this new feature of the data factory.

The go-to ETL tool for most situations

Rating: 9 out of 10
Incentivized
September 14, 2023
Verified User
C-Level Executive in Information Technology
Financial Services Company, 51-200 employees
Vetted Review
Verified User
Azure Data Factory
3 years of experience
Use Cases and Deployment Scope
Data Integration: We harness Azure Data Factory's capabilities to move data from various sources – both on-premises databases and cloud storage – into our Azure data storage solutions like Azure SQL Database, Azure Blob Storage, and Azure Data Lake Store. This ensures all our data, regardless of its origin, is consolidated in one place.
Transformations: Azure Data Factory's data flow transformations help us clean, transform, and enrich our data before loading it to the destination. This is crucial for maintaining data quality, especially when dealing with diverse datasets.
Pros
  • Azure Data Factory supports a vast array of source and destination connectors, both from within the Microsoft ecosystem (like Azure Blob Storage, Azure SQL Database, Azure Cosmos DB) and external platforms (like Amazon S3, Google Cloud Storage, SAP, Salesforce, and many more).
  • Azure Data Factory's Mapping Data Flows provides a code-free environment to design data transformations visually. Users can drag and drop elements to create complex ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes without needing to write any code.
  • Azure Data Factory provides a unified monitoring dashboard that offers a holistic view of all pipeline activities. I think this makes it easier for users to track the status of various jobs, identify failures, and pinpoint bottlenecks.
Cons
  • Granularity of Errors: Sometimes, Azure Data Factory provides error messages that are too generic or vague for us, making it challenging to pinpoint the exact cause of a pipeline failure. Enhanced error messages with more actionable details would greatly assist us as users in debugging their pipelines.
  • Pipeline Design UI: In my experience, the visual interface for designing pipelines, especially when dealing with complex workflows or numerous activities, can become cluttered. I think a more intuitive and scalable design interface would improve usability. In my opinion, features like zoom, better alignment tools, or grouping capabilities could make managing intricate designs more manageable.
  • Native Support: While Azure Data Factory does support incremental data loads, in my experience, the setup can be somewhat manual and complex. I think native and more straightforward support for Change Data Capture, especially from popular databases, would simplify the process of capturing and processing only the changed data, making regular data updates more efficient
Likelihood to Recommend
Well-suited Scenarios for Azure Data Factory (ADF):
When an organization has data sources spread across on-premises databases and cloud storage solutions, I think Azure Data Factory is excellent for integrating these sources.
Azure Data Factory's integration with Azure Databricks allows it to handle large-scale data transformations effectively, leveraging the power of distributed processing.
For regular ETL or ELT processes that need to run at specific intervals (daily, weekly, etc.), I think Azure Data Factory's scheduling capabilities are very handy.

Less Appropriate Scenarios for Azure Data Factory:
Real-time Data Streaming - Azure Data Factory is primarily batch-oriented.
Simple Data Copy Tasks - For straightforward data copy tasks without the need for transformation or complex workflows, in my opinion, using Azure Data Factory might be overkill; simpler tools or scripts could suffice.
Advanced Data Science Workflows: While Azure Data Factory can handle data prep and transformation, in my experience, it's not designed for in-depth data science tasks. I think for advanced analytics, machine learning, or statistical modeling, integration with specialized tools would be necessary.
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