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.
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CData Sync
Score 7.0 out of 10
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CData's Sync is a data pipeline tool able to connect data sources to the user's database or data warehouse, supporting at present over 200 possible sources, and a range of destinations (e.g. Snowflake, S3, Redshift), connecting on-premise or SaaS sources and destinations.
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Pricing
Azure Data Factory
CData Sync
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Azure Data Factory
CData Sync
Free Trial
No
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
Azure Data Factory
CData Sync
Features
Azure Data Factory
CData Sync
Data Source Connection
Comparison of Data Source Connection features of Product A and Product B
Azure Data Factory
8.5
10 Ratings
3% above category average
CData Sync
8.0
1 Ratings
3% below category average
Connect to traditional data sources
9.010 Ratings
8.01 Ratings
Connecto to Big Data and NoSQL
8.010 Ratings
00 Ratings
Data Transformations
Comparison of Data Transformations features of Product A and Product B
Azure Data Factory
7.8
10 Ratings
3% below category average
CData Sync
-
Ratings
Simple transformations
8.710 Ratings
00 Ratings
Complex transformations
7.010 Ratings
00 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
Azure Data Factory
6.3
10 Ratings
21% below category average
CData Sync
7.5
1 Ratings
4% below category average
Data model creation
4.57 Ratings
00 Ratings
Metadata management
5.58 Ratings
00 Ratings
Business rules and workflow
6.010 Ratings
00 Ratings
Collaboration
7.09 Ratings
8.01 Ratings
Testing and debugging
6.310 Ratings
7.01 Ratings
Data Governance
Comparison of Data Governance features of Product A and Product B
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.
Evidently, CData Sync is an excellent middleware tool that is perfect for syncing data between systems. It is especially suitable and works well on SQL servers, DB2, MySQL, and Snowflake, and some of their brothering domains. However, it is a limitation in working with Sage 50 API. But if it is extracted with ODBC, it works well.
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
Although efficient for SQL servers and MySQL, as well as Snowflake. It is not strong for other database engines, and an upgrade on this would do a lot.
The installation process is manual as opposed to the cloud installation it should be.
A feature of syncing auto increment ID key, that will help in existing data management.
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.
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
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.
Tableau is another similar software tool, unlike CData Sync, rather transforms data into actionable insights. While CData Sync works with automation, Tableau uses a drag box on its AVA, which in turn slows the work speed on the syncing of data. Over the years, I enjoyed the friendly and customizable option of CData Sync over Tableau's.