Azure Data Factory vs. Fivetran vs. Mashery (now part of Boomi)

Best scenario is for ETL process. The flexibility and connectivity is outstanding. For our environment, SAP data connectivity with Azure Data Factory offers very limited features compared to SAP Data Sphere. Due to the limited modelling capacity of the tool, we use Databricks for data modelling and cleaning. Usage of multiple tools could have been avoided if adf has modelling capabilities.

Incentivized

Fivetran's business model justifies the use-case where we require data from a single source basically a lot of data but if the requirement is not on the heavier side, Fivetran comes to costly operation when compared to its peers. Otherwise, I'll recommend Fivetran for stability and update and seamless service provider.

Incentivized

Mashery is great when it comes to deployment to your own datacenter and when it comes to managing third party API's like Salesforce using Mashery Cloud version. I would be a little bit more careful when deploying it on Kubernetes as it was not designed for it. New version 5 is re-architected to run more on natively on Kubernetes, but we have not tested it yet.

Incentivized

• Data Ingestion - it works very well with numerous data sources.
• Data pipeline orchestration: It is a generic, popular tool for orchestrating data pipelines.
• Works well in Azure ecosystem, Azure services and data platforms like Databricks.
• It is a serverless and scalable solution for cloud data integration.

Incentivized

• Easily connects to source data using delivered connectors
• Transforms data into standard models and schemas
• Has very good documentation to help quickly setup connectors

Incentivized

• Easy integration of the TIBCO Cloud Mashery developer portal allows simple onboarding and user and application management.
• Configuration of TIBCO Cloud Mashery is both flexible and focused to address the main tasks for an API Management solution.
• The built-in OAuth Token capabilities provide a quick start to properly manage application access.

Incentivized

• 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

Incentivized

• Very difficult to get connectors enhanced if a specific needed object is not supported by them
• Depending on the edition needed and the data volumes, can get quite expensive

Incentivized

• The "Control Center" admin dashboard is not performant. We have a lot of configuration data in Mashery (many endpoints, many plans, many users, many keys, etc.) and the website struggles with the volume of data it has to deal with.
• Their systems have limitations that make it more difficult for us to operate the way we would like. For example, there is a limit to the number of API Definitions we can create, as well as a limit to the number of Endpoints we can define in a Plan.
• Their support organization leaves a lot to be desired. Responses are slow, and when they do come they are often inadequate. We have to re-phrase the question to get them to answer it differently, or we have to repeatedly follow up to ask for additional clarifying information.

Incentivized

Ease of use.

Incentivized

So far product has performed as expected. We were noticing some performance issues, but they were largely Synapse related. This has led to a shift from Synapse to Databricks. Overall this has delayed our analytic platform. Once databricks becomes fully operational, Azure Data Factory will be critical to our environment and future success.

Incentivized

Very easy and intuitive to setup and maintain as there usually are not that many options. Very well documented (e.g. how to setup each connector, how the schema looks like, any specific features of this connector etc.). Also the operation is intuitive, e.g. you have status pages, log pages, configuration pages etc. for each connector.

Incentivized

i find some of the package adding and key are complex . UI experience is bad . Every step need front and backward navigation too much. It would be better if endpoint itself contain package and key addiction option

Incentivized

It runs pretty well and gets our data from point A to point cluster quickly enough. Honestly, it's not something I think about unless it breaks and that's pretty rare.

Incentivized

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

Incentivized

Support has been good overall.

Incentivized

Initially it was difficult to understand . after few day , it was easy

Incentivized

Azure Data Factory helps us automate to schedule jobs as per customer demands to make ETL triggers when the need arises. Anyone can define the workflow with the Azure Data Factory UI designer tool and easily test the systems. It helped us automate the same workflow with programming languages like Python or automation tools like ansible. Numerous options for connectivity be it a database or storage account helps us move data transfer to the cloud or on-premise systems.

Incentivized

We never seriously considered using anything else. Our data engineers had used Fivetran extensively in previous roles so when it came time to make a decision, there wasn't much of a process. They gladly signed the contract with Fivetran pretty quickly.

Incentivized

I've really only looked at Mashery since it has been around a very long time and has a rich feature set. I do know our platform teams are looking into AWS gateway but not sure this product has everything we need.

Incentivized

• Facilitate better decision-making and improve business processes.
• Optimize business process outcomes by increasing internal efficiency and operational effectiveness.
• Boosts revenue growth while improving business process agility.

Incentivized

• It has been very positive in serving BI team with new source requests
• It has been OK at scaling to match as data volumes as source data size grows

Incentivized

• As a API Mediation layer it has helped with the troublesome port setup with internal and external clients. Meaning it made that easy to the client that they know where to go for access to an API.
• The interactive documentation is very well put together and has reduced the time that developers have to go back and forth with each other to figure how to call the API.

Incentivized