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Ataccama Reference Data Manager [EOL] vs Azure Data Factory comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Ataccama Reference Data Man...
Average Rating
10.0
Number of Reviews
1
Ranking in other categories
No ranking in other categories
Azure Data Factory
Average Rating
8.0
Reviews Sentiment
6.8
Number of Reviews
94
Ranking in other categories
Data Integration (3rd), Cloud Data Warehouse (2nd)
 

Featured Reviews

it_user69087 - PeerSpot reviewer
Head of Data Analytics at a financial services firm with 5,001-10,000 employees
We evaluated Microsoft master data services – it's nice but far from Ataccama RDM especially in workflow features
We use this system for managing reference data. It’s the most useful system for data warehouse because all data sources have to come from relational database. Unfortunately before Ataccama RDM was installed, some reference code books came from EXCEL, which is definitely an error-prone data source that does not have proper dataflow and quality checks when inputting data. We are using this system also for managing hard-coded logic in ETLs by creating special reference tables and creating workflows to update it in a manageable way. We will use it for managing code book which is currently booked in some systems but doesn’t have sexy features as in Ataccama RDM. This system can simplify data transfer between systems, eliminate extra copies and is different from a time perspective.
KandaswamyMuthukrishnan - PeerSpot reviewer
Director at a computer software company with 1,001-5,000 employees
Integrates diverse data sources and streamlines ETL processes effectively
Regarding potential areas of improvement for Azure Data Factory, there is a need for better data transformation, especially since many people are now depending on DataBricks more for connectivity and data integration. Azure Data Factory should consider how to enhance integration or filtering for more transformations, such as integrating with Spark clusters. I am satisfied with Azure Data Factory so far, but I suggest integrating some AI functionality to analyze data during the transition itself, providing insights such as null records, common records, and duplicates without running a separate pipeline or job. The monitoring tools in Azure Data Factory are helpful for optimizing data pipelines; while the current feature is adequate, they can improve by creating a live dashboard to see the online process, including how much percentage has been completed, which will be very helpful for people who are monitoring the pipeline.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"Really best product for this certain task area – to make reference data is manageable."
"Data Factory's most valuable feature is Copy Activity."
"Azure Data Factory was not difficult to deploy because it is a small area, so we completed it very quickly."
"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 can scale very easily."
"Data Factory's best features include its data source connections, GUI for building data pipelines, and target loading within Azure."
"It is a complete ETL Solution."
"The most valuable feature I have found at Azure Data Factory is the data flow function."
"On the tool itself, we've never experienced any bugs or glitches. There haven't been crashes. Stability has been good."
 

Cons

"Creation process of services for on-line validation data (special services which are able to be used by another system) is too complicated."
"The performance could be better. It would be better if Azure Data Factory could handle a higher load. I have heard that it can get overloaded, and it can't handle it."
"The pricing scheme is very complex and difficult to understand."
"When we initiated the cluster, it took some time to start the process."
"The solution can be improved by decreasing the warmup time which currently can take up to five minutes."
"There is no built-in function for automatically adding notifications concerning the progress or outline of a pipeline run."
"The only thing that we're struggling with is increasing the competency of my team, so we think that the Microsoft documentation is too complicated."
"Currently, smaller businesses face a disadvantage in terms of pricing, and reducing costs could address this issue."
"The thing we missed most was data update, but this is now available as of two weeks ago."
 

Pricing and Cost Advice

Information not available
"In terms of licensing costs, we pay somewhere around S14,000 USD per month. There are some additional costs. For example, we would have to subscribe to some additional computing and for elasticity, but they are minimal."
"Pricing is comparable, it's somewhere in the middle."
"This is a cost-effective solution."
"Understanding the pricing model for Data Factory is quite complex."
"I would not say that this product is overly expensive."
"The pricing model is based on usage and is not cheap."
"I am aware of the pricing of Azure Data Factory, but I prefer not to disclose specific details."
"The licensing is a pay-as-you-go model, where you pay for what you consume."
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Top Industries

By visitors reading reviews
No data available
Financial Services Firm
12%
Computer Software Company
10%
Manufacturing Company
9%
Government
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
By reviewers
Company SizeCount
Small Business31
Midsize Enterprise20
Large Enterprise57
 

Questions from the Community

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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 Q...
 

Also Known As

Reference Data Manager
No data available
 

Overview

 

Sample Customers

Bank of Montreal
1. Adobe 2. BMW 3. Coca-Cola 4. General Electric 5. Johnson & Johnson 6. LinkedIn 7. Mastercard 8. Nestle 9. Pfizer 10. Samsung 11. Siemens 12. Toyota 13. Unilever 14. Verizon 15. Walmart 16. Accenture 17. American Express 18. AT&T 19. Bank of America 20. Cisco 21. Deloitte 22. ExxonMobil 23. Ford 24. General Motors 25. IBM 26. JPMorgan Chase 27. Microsoft (Azure Data Factory is developed by Microsoft) 28. Oracle 29. Procter & Gamble 30. Salesforce 31. Shell 32. Visa
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