Azure Data Factory

Azure Data Factory

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Score 8.4 out of 100
Azure Data Factory

Overview

Recent Reviews

Popular Features

View all 12 features

Connect to traditional data sources (5)

9.2
92%

Simple transformations (5)

8.9
89%

Connecto to Big Data and NoSQL (5)

8.7
87%

Complex transformations (5)

8.3
83%

Reviewer Pros & Cons

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Video Reviews

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Pricing

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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 80…

Entry-level set up fee?

  • No setup fee

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

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Features Scorecard

Data Source Connection

8.9
89%

Data Transformations

8.6
86%

Data Modeling

8.8
88%

Data Governance

8.3
83%

Product Details

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 80 natively built and maintenance-free connectors at no added cost. Focus on data—the serverless integration service does the rest.

Azure Data Factory Technical Details

Operating SystemsUnspecified
Mobile ApplicationNo

Comparisons

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Frequently Asked Questions

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 80 natively built and maintenance-free connectors at no added cost. Focus on data—the serverless integration service does the rest.

What is Azure Data Factory's best feature?

Reviewers rate Data model creation highest, with a score of 9.4.

Who uses Azure Data Factory?

The most common users of Azure Data Factory are from Mid-sized Companies (51-1,000 employees) and the Information Technology & Services industry.

Reviews and Ratings

 (40)

Ratings

Reviews

(1-5 of 5)
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Score 8 out of 10
Vetted Review
Verified User
Review Source
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.
  • Orchestration
  • On premises support
  • Support to vast no of data connectors
  • Cloud migration
  • Native transform functions missing.
  • Pricing
  • Limited trigger functions.
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.
Niloofar Keshvari Nia | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
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.
  • 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
  • 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.
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.
Murray Clear | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
Thanks to the implementation of Azure as software for our data integrations we have been able to transport different data structures to the cloud without losing any content, with entirely automated movements that we have been able to leverage in several ways: one of them was to streamline the creation of funnels, so that we could transform the data before transporting it. It does not require prior configuration of some kind of ETL process to write code, since the platform lets you do it by itself, and without the need to necessarily create an environment where data traffic is intuitive. That is to say, it can be used to create varied programming environments. Basically creates a layer where data is integrated in different ways, whether or not business intelligence or IT mechanics are being used to integrate with analytics software. Creates very fast transformations where data mapping can be managed so that it carries some level of intelligence, and data source copy operations require less manual review and management.
  • Once a sales funnel is defined and configured, it can be interconnected to another, so that we can create a complete network where it is possible to monitor the different executions of each funnel linked to our data from one place.
  • Azure Data Factory promotes excellent data management strategies, which we have been able to leverage during the workday, and all thanks to the help provided by their support team, which from the beginning of our interactions kept us properly informed about solutions to every issue that arose.
  • An advantage of Data Factory is that data structures can be stored in several warehouses at the same time, and these can be moved from one warehouse to another by configuring a trigger that is automatically executed when certain predefined parameters are met, such as the generation of a blob within the platform.
  • Azure Data Factory has helped us to carry out data assignments with much more integrity and comfort, and we will continue to use it, given its excellent ease of execution of administrative operations and its incredible approach to business intelligence management.
  • Creating data infrastructures without prior design is entirely possible with Azure, as the platform properly defines all the processes to be followed to create a solid foundation for information and data flows.
  • If you are working with Azure synapse Analytics you have to keep in mind that the collection of all local data is not properly handled when integrating it to data Factory, and this can result in problems in the long run if you have not been able to transform the data into other streams.
  • There are very few data sets allowed by the platform, the most adaptable being blob and SQL as tables, but it leaves out many other dynamics that are currently used, such as string and raw SQL data coming from database services like MongoDB.
  • It is not recommended for working with machine learning mechanics, mostly because the reports handled within the platform have restructurable formats, but do not adapt correctly to services such as PowerBI.
Suitable for virtually any scenario that involves working with linked Service, so that it is possible to connect Azure Data Factory to storage services for SQL data in the cloud, and that it is possible to access them at any time. Serves to make the information much cleaner and have the desired formats and structures in case of working with time tracking or general employee work tracking applications, so it is easy to create accurate and aesthetically acceptable analysis for general tracking software. It is extremely useful if you do not have any data server service, as Azure Data Factory has the ability to maintain almost 90 different data connectors without the need to use a server.
January 20, 2021

ADF is awesome!

Marco Urrea | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
I've used it to perform PoC's and work with data transformation processes that interact with other applications or tools.
  • Cloud-based
  • Fast
  • Reliable
  • Some features exist on the UI but are not implemented
  • Its always changing
It works better than other tools from the same range, it has a beautiful UI and it makes work easy. Its also very easy to integrate with other tools, tools, apps and ecosystems.
Score 7 out of 10
Vetted Review
Verified User
Review Source
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.
  • 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.
  • 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.
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.
We have not had need to engage with Microsoft much on Azure Data Factory, but they have been responsive and helpful when needed. This being said, we have not had a major emergency or outage requiring their intervention. The score of seven is a representation that they have done well for now, but have not proved out their support for a significant issue.