AWS Data Exchange vs. Azure Data Factory

AWS Data Exchange fits best for scenarios where you have datasets that you would like to sell and you want to deliver it to anyone who would like to purchase it. It really beats having to set up downloads via your own website or portal. However, it can get complicated to manage if you're trying to deliver a dataset a client has already paid for.

Incentivized

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

• Simplified data delivery
• Ability to create any amount of data products
• Ability to integrate payment plans with data products
• Tracking data downloads and users
• Integration with other AWS data services

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

• Integration with more data sources
• Ability to deliver data to clients without AWS accounts
• Inclusion of direct data downloads in addition to asynchronous methods

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

There have been a lot of problems with ADX. First, the entire system is incredibly clunky from beginning to end.First, by AWS's own admission they're missing a lot of "tablestakes functionality" like the ability to see who is coming to your pages, more flexibility to edit and update your listings, the ability to create a storefront or catalog that actually tries to sell your products. All-in-all you're flying completely blind with AWS. In our convos with other sellers we strongly believe very little organic traffic is flowing through the AWS exchange. For the headache, it's not worth the time or the effort. It's very difficult to market or sell your products.We've also had a number of simple UX bugs where they just don't accurately reflect the attributes of your product. For instance for an S3 bucket they had "+metered costs" displayed to one of our buyers in the price. This of course caused a lot of confusion. They also misrepresented the historical revisions that were available in our product sets because of another UX bug. It's difficult to know what other things in the UX are also broken and incongruent.We also did have a purchase, but the seller is completely at their whim at providing you fake emails, fake company names, fake use cases because AWS hasn't thought through simple workflows like "why even have subscription confirmation if I can fake literally everything about a subscription request." So as a result we're now in an endless, timewasting, unhelpful thread with AWS support trying to get payment. They're confused of what to do and we feel completely lost.Lastly, the AWS team has been abysmal in addressing our concerns. Conversations with them result in a laundry list of excuses of why simple functionalities are so hard (including just having accurate documentation). It was a very frustrating and unproductive call. Our objective of our call was to help us see that ADX is a well-resourced and well-visioned product. Ultimately they couldn't clearly articulate who they built the exchange for both on the seller side and the buyer side.Don't waste your time. This is at best a very foggy experiment. Look at other sellers, they have a lot of free pages to try to get attention, but then have smart tactics to divert transactions away from the ADX. Ultimately, smart move. Why give 8-10% of your cut to a product that is basically bare-bones infrastructure.

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

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

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

• Reduced time to publish datasets for sale by more than 80%
• Increased net profit from dataset sales by ~10%
• Reduced data delivery time to clients by 15%

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