Azure Data Factory vs Microsoft Azure Synapse Analytics comparison

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Comparison Buyer's Guide
Executive Summary
Updated on Jul 18, 2022

We performed a comparison between Azure Data Factory and Microsoft Azure Synapse Analytics based on our users’ reviews in four categories. After reading all of the collected data, you can find our conclusion below.

  • Ease of Deployment: Users tell us deployment of both these solutions is very easy and straightforward.
  • Features: Azure Data Factory offers packages and data transformations that can be completed with a simple drag-and-drop process. The solution performs very well with pipeline orchestration, is very flexible, and scales easily. Users would like to see machine learning options and better integration with AWS, Oracle, and other products.

    Microsoft Azure Synapse Analytics users like the major advantage provided with the easy scale out and down on-demand services; this gives tremendous control over expenditures and power. The platform is very intuitive and they have different cloud offerings. Users would like to see better native support for NoSQL, social media, and internet data.
  • Pricing: Both of these solutions are pay-as-you-go. Although some users think the pricing is fair, there are users who feel the pricing could be better.
  • Service and Support: For the most part, users of both solutions are satisfied with the level of service and response they have received.

Comparison Results: Both of these solutions are very dynamic, robust, stable, and very flexible. As they are both part of the Microsoft Azure ecosystem, they are both very popular and highly regarded. Many of our users feel Azure Data Factory is an easier solution to understand and get started with out of the box. Microsoft Azure Synapse Analytics is more diverse and works better with a varied amount of different areas and industries.

To learn more, read our detailed Azure Data Factory vs. Microsoft Azure Synapse Analytics Report (Updated: November 2022).
653,522 professionals have used our research since 2012.
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"The most valuable features of Azure Data Factory are the flexibility, ability to move data at scale, and the integrations with different Azure components.""The data copy template is a valuable feature.""Azure Data Factory became more user-friendly when data-flows were introduced.""It's cloud-based, allowing multiple users to easily access the solution from the office or remote locations. I like that we can set up the security protocols for IP addresses, like allow lists. It's a pretty user-friendly product as well. The interface and build environment where you create pipelines are easy to use. It's straightforward to manage the digital transformation pipelines we build.""Azure Data Factory's most valuable features are the packages and the data transformation that it allows us to do, which is more drag and drop, or a visual interface. So, that eases the entire process.""The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring.""The solution includes a feature that increases the number of processors used which makes it very powerful and adds to the scalability.""Its integrability with the rest of the activities on Azure is most valuable."

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"The features most valuable are the simplicity, how easy it is to create a dashboard from different information systems.""The most valuable feature of Microsoft Azure Synapse Analytics is the pipeline that is the ETL tool. It's very well designed and is overall very good. We usually don't use the ETL tool in Databricks, but we use the ETL tool in this solution.""The useability, the user interface, is very user-friendly.""Can capture all the transactional data throughout a company.""Synapse Analytics' best features are notebooks, pipelines, and monitoring.""The architecture, including compute and storage, is good.""The integrated workspace in Microsoft Azure Synapse Analytics where everything comes together, such as Power BI and Data Factory, is very good. Additionally, the ability to do dedicated SQL pooling is a benefit.""The most valuable feature is the level of processing power, and being able to complete tasks in parallel."

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Cons
"Integration of data lineage would be a nice feature in terms of DevOps integration. It would make implementation for a company much easier. I'm not sure if that's already available or not. However, that would be a great feature to add if it isn't already there.""Occasionally, there are problems within Microsoft itself that impacts the Data Factory and causes it to fail.""There are limitations when processing more than one GD file.""The deployment should be easier.""They require more detailed error reporting, data normalization tools, easier connectivity to other services, more data services, and greater compatibility with other commonly used schemas.""Data Factory has so many features that it can be a little difficult or confusing to find some settings and configurations. I'm sure there's a way to make it a little easier to navigate.""It can improve from the perspective of active logging. It can provide active logging information.""Some of the optimization techniques are not scalable."

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"Right now, we are really struggling with the performance. it's not as good as we had hoped.""The initial setup has a bit of a learning curve.""We'd like the solution to have more analytics capability.""Documentation could be improved.""The filing can be improved.""Microsoft Azure Synapse Analytics can improve by increasing the size of the files that we can load on the platform. We have some files that are too large to be loaded and it would be a benefit to us if the limit was increased. Additionally, the way we use the tool for generating reports can be made better. They should add some drag-and-drop rules without the need of programming these rules using some programming language. It would be helpful if we did not need someone that was technically advanced to be able to do it with, such as someone with no IT background. Having a reporting tool without code would be great.""The product could be more feature-rich.""Integration with other vendors has limitations and could be improved."

More Microsoft Azure Synapse Analytics Cons →

Pricing and Cost Advice
  • "I would not say that this product is overly expensive."
  • "The licensing is a pay-as-you-go model, where you pay for what you consume."
  • "Our licensing fees are approximately 15,000 ($150 USD) per month."
  • "The licensing cost is included in the Synapse."
  • "It's not particularly expensive."
  • "Product is priced at the market standard."
  • "There's no licensing for Azure Data Factory, they have a consumption payment model. How often you are running the service and how long that service takes to run. The price can be approximately $500 to $1,000 per month but depends on the scaling."
  • "I don't see a cost; it appears to be included in general support."
  • More Azure Data Factory Pricing and Cost Advice →

  • "The cost of the licensing depends on the size of the warehouse, where the cost of storage is approximately $130 USD per terabyte."
  • "This is a cost-effective product."
  • "Because it's cloud the cost is a different convention and the licensing costs are not the same."
  • "It's very difficult to price unless you know exactly how the customer is using it."
  • "We normally pay between $300 and $500 per month, which is quite expensive for how much we actually use it, performance- and usage-wise. They have a cheap version and an expensive version, and our usage usually falls in the middle ground, which makes it not as cost-effective as it could be."
  • "The solution is subscription-based. You can also pay to use the product as you go."
  • "It requires a less expensive version because currently, not every customer is able to buy it. If it could have a smaller setup that doesn't require so many resources, it would be helpful, and we would be able to use it in more cases. We are a small country, and most of our customers are quite small businesses."
  • "It goes by the usage, and there are some limits. Synapse goes by particular pricing, and it is expensive. Both Azure Synapse Analytics and Snowflake are pretty expensive. They don't have standard pricing. They deal with each customer differently."
  • More Microsoft Azure Synapse Analytics Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:AWS Glue and Azure Data factory for ELT best performance cloud services.
    Top Answer:Azure Data Factory is flexible, modular, and works well. In terms of cost, it is not too pricey. It offers the stability and reliability I am looking for, good scalability, and is easy to set up and… more »
    Top Answer:Azure Data Factory is a solid product offering many transformation functions; It has pre-load and post-load transformations, allowing users to apply transformations either in code by using Power… more »
    Top Answer:Amazon Redshift is very fast, has a very good response time, and is very user-friendly. The initial setup is very straightforward. This solution can merge and integrate well with many different… more »
    Top Answer:Traditional ETL would usually use a dedicated database (or even database server) where you'll load & transform your raw data before ingesting it into the final destination. This would allow checking… more »
    Top Answer:The most valuable feature of Microsoft Azure Synapse Analytics is the pipeline that is the ETL tool. It's very well designed and is overall very good. We usually don't use the ETL tool in Databricks… more »
    Ranking
    2nd
    Views
    35,763
    Comparisons
    29,092
    Reviews
    37
    Average Words per Review
    513
    Rating
    8.0
    3rd
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    35,561
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    22,981
    Reviews
    47
    Average Words per Review
    472
    Rating
    7.8
    Comparisons
    Also Known As
    Azure Synapse Analytics, Microsoft Azure SQL Data Warehouse, Microsoft Azure SQL DW, Azure SQL Data Warehouse, MS Azure Synapse Analytics
    Learn More
    Overview

    Azure Data Factory is a managed cloud service built for extract-transform-load (ETL), extract-load-transform (ELT), and data integration projects. This is a digital integration tool as well as a cloud data warehouse that allows users to create, schedule, and manage data in the cloud or on premises. The use cases of the product include data engineering, operational data integration, analytics, ingesting data into data warehouses, and migrating on-premise SQL Server Integration Services (SSIS) packages to Azure.

    The tool allows users to create data-driven workflows for initiating data movement and data transformation at scale. Data can be ingested from disparate data stores via pipelines. Companies can utilize this product to build complex ETL processes for transforming data visually with data flows. Azure Data Factory also offers services such as Azure HDInsight Hadoop, Azure Databricks, Azure Synapse Analytics, and Azure SQL Database. These services are created to facilitate data management and control for organizations, providing them with better visibility of their data for improved decision-making.

    Azure Data Factory allows companies to create schedules for moving and transforming data into their pipelines. This can be done hourly, daily, weekly, or according to the specific needs of the organization. The steps through which the data-driven workflows work in Azure Data Factory are the following:

    1. Connecting to required sources and collecting data. After connecting to the various sources where data is stored, the pipelines move the data to a centralized location for further processing.

    2. Transforming and enriching the data. Once the data is moved to a centralized data store in the cloud, the pipelines transform it through services like HDInsight Hadoop, Azure Data Lake Analytics, Spark, and Machine Learning.

    3. Delivering the transformed data to on-premise sources or keeping it in cloud storage sources for usage by different tools and applications.

    Azure Data Factory Concepts

    The solution consists of a series of interconnected systems that provide data integration and related services for users. The following concepts create the end product for users:

    • Pipelines: A pipeline refers to the logical grouping of activities that performs a unit of work which together perform a task.

    • Mapping data flows: Azure Data Factory lets its users create and manage graphs of data transformation logic for transforming any-sized data. The logic is executed on a Spark cluster, which does not have to be managed or maintained personally by the user.

    • Linked services: The linked services in the tool define the connection to the data source. There are various services used for two main purposes - to represent a data store that the solution supports and to represent a compute resource that can host the execution of an activity.

    • Integration runtime: The integration runtime in the tool provides the bridge between the activity and linked services needed for it.

    • Triggers: There are various types of triggers in the solution, created for different types of events. They determine when a pipeline execution should be initiated.

    • Pipeline runs: Pipeline runs are instantiated by passing the arguments to the parameters that are defined in pipelines, executing the pipelines' work.

    • Control flow: Control flow in Azure Data Factory is an orchestration of pipeline activities.

    • Connect and collect: This serves as the first step of the services that this tool offers. It connects all the required sources of data and processing in order to prepare the data for moving it to a centralized location for further processing. The step eliminates the need for companies to integrate expensive custom solutions for data movement. Through Copy Activity, Azure Blob storage, and Azure HDInsight Hadoop cluster, users can quickly initiate the first step of organizing their data.

    • Transform and enrich: The collected data can be processed or transformed by using the mapping data flows of the product. Data transformation graphs can be executed on Spark without the need to understand its clusters or how programming works.

    • CI/CD and publish: Through Azure DevOps and GitHub clients, the tool can receive full support for CI/CD for their data pipelines, which allows for the development and delivery of ETL processes before publishing the finished product.

    • Monitor: When users have successfully built and deployed their data integration pipelines, the service offers them the option to monitor the scheduled activities and pipelines. This is done through Azure Monitor, API, PowerShell, and health panels on the Azure portal.

    Azure Data Factory Benefits

    Azure Data Factory offers clients many several benefits. Some of these include:

    • An easy-to-use platform which is suitable for both beginner and expert users, as it offers code-free processes and built-in support.

    • Pay-as-you-go option for clients to pay only for the services that they are using.

    • Powerful tool with more than 90 built-in connectors, which allow companies to ingest on-premise and software as service (SaaS) data quickly.

    • Provided autonomous ETL, which unlocks operational efficiencies and citizen integrators.

    • The tool is designed to handle large volumes of data and provide users with better scalability and performance than classic ETL systems.

    • Azure Data Factory allows users to easily migrate ETL workloads to the solution’s cloud.

    • The solution offers great security for its users, as it provides the option for assigning specific permissions and roles within the organization.

    • Azure Data Factory is highly automated, which allows users to orchestrate their data more efficiently.

    • The platform is a combination of GUI and scripting-based interfaces, which gives users more freedom over data management.

    • The tool provides organizations with the option to rely on Microsoft to fully manage the process. This eliminates the potential need of hiring a third-party expert.

    Reviews from Real Users

    According to Dan M., a Chief Strategist & CTO at a consultancy, Azure Data Factory is secure and reasonably priced.

    A Senior Manager at a tech services company evaluates the tool as reasonably priced, scales well, good performance.

    Microsoft Azure Synapse Analytics is an end-to-end analytics solution that successfully combines analytical services to merge big data analytics and enterprise data warehouses into a single unified platform. The solution can run intelligent distributed queries among nodes, and provides the ability to query both relational and non-relational data.

    Microsoft Azure Synapse Analytics is built with these 4 components:

    1. Synapse SQL
    2. Spark
    3. Synapse Pipeline
    4. Studio

    Microsoft Azure Synapse Analytics Features

    Microsoft Azure Synapse Analytics has many valuable key features, including:

    • Cloud Data Service: WIth Microsoft Azure Synapse Analytics you can operate services (data analytics, machine learning, data warehousing, dashboarding, etc.) in a single workspace via the cloud.

    • Structured and unstructured data: Microsoft Azure Synapse Analytics supports both structured and unstructured data and allows you to manage relational and non-relational data - unlike data warehouses and lakes that tend to store them respectively.

    • Effective data storage: Microsoft Azure Synapse Analytics offers next-level data storage with high availability and different tiers.

    • Responsive data engine: Microsoft Azure Synapse Analytics uses Massive Parallel Processing (MPP) and is designed to handle large volumes of data and analytical workloads efficiently without any problems.

    • Several scripting languages: The solution provides language compatibility and supports different programming languages, such as Python, Java, Spark SQL, and Scala.

    • Query optimization: Microsoft Azure Synapse Analytics works well to facilitate limitless concurrency and performance optimization. It also simplifies workload management.

    Microsoft Azure Synapse Analytics Benefits

    Some of the benefits of using Microsoft Azure Synapse Analytics include:

    • Database templates: Microsoft Azure Synapse Analytics offers industry-specific database templates that make it easy to combine and shape data.

    • Better business insights: With Microsoft Azure Synapse Analytics you can expand discovery of insights from all your data and apply machine learning models to all your intelligent apps.

    • Reduce project development time: Microsoft Azure Synapse Analytics makes it possible to have a unified experience for developing end-to-end analytics, which reduces project development time significantly.

    • Eliminate data barriers: By using Microsoft Azure Synapse Analytics, you can perform analytics on operational and business apps data without data movement.

    • Advanced security: Microsoft Azure Synapse Analytics provides both advanced security and privacy features to ensure data protection.

    • Machine Learning: Microsoft Azure Synapse Analytics integrates Azure Machine Learning, Azure Cognitive Services, and Power BI.

    Reviews from Real Users

    Below are some reviews and helpful feedback written by Microsoft Azure Synapse Analytics users who are currently using the solution.

    PeerSpot user Jael S., who is an Information Architect at Systems Analysis & Design Engineering, comments on her experience using the product, saying that it is “Scalable, intuitive, facilitates compliance and keeps your data secure”. She also says "We also like governance. It looks at what the requirements are for the company to identify the best way to ensure compliance is met when you move to the cloud."

    Michel T., CHTO at Timp-iT, mentions that "the features most valuable are the simplicity, how easy it is to create a dashboard from different information systems."

    A Senior Teradata Consultant at a tech services company says, "Microsoft provides both the platform and the data center, so you don't have to look for a cloud vendor. It saves you from having to deal with two vendors for the same task."


    Offer
    Learn more about Azure Data Factory
    Learn more about Microsoft Azure Synapse Analytics
    Sample Customers
    Milliman, Pier 1 Imports, Rockwell Automation, Ziosk, Real Madrid
    Toshiba, Carnival, LG Electronics, Jet.com, Adobe, 
    Top Industries
    REVIEWERS
    Computer Software Company35%
    Non Profit9%
    Manufacturing Company9%
    Insurance Company6%
    VISITORS READING REVIEWS
    Computer Software Company19%
    Financial Services Firm11%
    Comms Service Provider8%
    Energy/Utilities Company7%
    REVIEWERS
    Computer Software Company24%
    Comms Service Provider12%
    Financial Services Firm12%
    Manufacturing Company9%
    VISITORS READING REVIEWS
    Computer Software Company20%
    Comms Service Provider11%
    Financial Services Firm9%
    Manufacturing Company6%
    Company Size
    REVIEWERS
    Small Business24%
    Midsize Enterprise21%
    Large Enterprise55%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise13%
    Large Enterprise70%
    REVIEWERS
    Small Business29%
    Midsize Enterprise21%
    Large Enterprise50%
    VISITORS READING REVIEWS
    Small Business18%
    Midsize Enterprise14%
    Large Enterprise68%
    Buyer's Guide
    Azure Data Factory vs. Microsoft Azure Synapse Analytics
    November 2022
    Find out what your peers are saying about Azure Data Factory vs. Microsoft Azure Synapse Analytics and other solutions. Updated: November 2022.
    653,522 professionals have used our research since 2012.

    Azure Data Factory is ranked 2nd in Cloud Data Warehouse with 40 reviews while Microsoft Azure Synapse Analytics is ranked 3rd in Cloud Data Warehouse with 47 reviews. Azure Data Factory is rated 7.8, while Microsoft Azure Synapse Analytics is rated 7.8. The top reviewer of Azure Data Factory writes "There's the good, the bad and the ugly....unfortunately lots of ugly". On the other hand, the top reviewer of Microsoft Azure Synapse Analytics writes "Multifeatured, has better performance over other solutions, and lets users manage structured and unstructured information, but the platform needs to be more user-friendly". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Talend Open Studio and Snowflake, whereas Microsoft Azure Synapse Analytics is most compared with Snowflake, Amazon Redshift, SAP BW4HANA, Apache Hadoop and AWS Lake Formation. See our Azure Data Factory vs. Microsoft Azure Synapse Analytics report.

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    We monitor all Cloud Data Warehouse reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.