AVEVA Historian vs. Azure Data Factory

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
AVEVA Historian
Score 9.0 out of 10
N/A
AVEVA Historian, formerly from Wonderware, is a time-series optimized data store, allowing the user to capture and store high-fidelity industrial big data, to unlock trapped potential for operational improvements.N/A
Azure Data Factory
Score 8.5 out of 10
N/A
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.N/A
Pricing
AVEVA HistorianAzure Data Factory
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
AVEVA HistorianAzure Data Factory
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
AVEVA HistorianAzure Data Factory
Top Pros
Top Cons
Features
AVEVA HistorianAzure Data Factory
Data Source Connection
Comparison of Data Source Connection features of Product A and Product B
AVEVA Historian
-
Ratings
Azure Data Factory
9.1
8 Ratings
9% above category average
Connect to traditional data sources00 Ratings9.38 Ratings
Connecto to Big Data and NoSQL00 Ratings9.08 Ratings
Data Transformations
Comparison of Data Transformations features of Product A and Product B
AVEVA Historian
-
Ratings
Azure Data Factory
8.4
8 Ratings
0% below category average
Simple transformations00 Ratings9.38 Ratings
Complex transformations00 Ratings7.58 Ratings
Data Modeling
Comparison of Data Modeling features of Product A and Product B
AVEVA Historian
-
Ratings
Azure Data Factory
8.0
8 Ratings
2% below category average
Data model creation00 Ratings8.56 Ratings
Metadata management00 Ratings7.87 Ratings
Business rules and workflow00 Ratings7.88 Ratings
Collaboration00 Ratings7.67 Ratings
Testing and debugging00 Ratings7.88 Ratings
Data Governance
Comparison of Data Governance features of Product A and Product B
AVEVA Historian
-
Ratings
Azure Data Factory
7.8
8 Ratings
6% below category average
Integration with data quality tools00 Ratings7.68 Ratings
Integration with MDM tools00 Ratings8.08 Ratings
Best Alternatives
AVEVA HistorianAzure Data Factory
Small Businesses
InfluxDB
InfluxDB
Score 8.4 out of 10
Skyvia
Skyvia
Score 9.5 out of 10
Medium-sized Companies

No answers on this topic

InfoSphere
InfoSphere
Score 8.4 out of 10
Enterprises

No answers on this topic

InfoSphere
InfoSphere
Score 8.4 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
AVEVA HistorianAzure Data Factory
Likelihood to Recommend
8.0
(1 ratings)
9.4
(8 ratings)
Support Rating
-
(0 ratings)
7.0
(1 ratings)
User Testimonials
AVEVA HistorianAzure Data Factory
Likelihood to Recommend
AVEVA
Paired with Citect SCADA or System Platform, this is an excellent process historian. It also works well collecting OPC data. For basic data storage, retrieval, and analysis, this is well suited. This is not well suited for very large deployments. Multiple instances would need to be used to scale up, and the data fed into a second-tier/enterprise historian for corporate user consumption.
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Microsoft
Well-suited Scenarios for Azure Data Factory (ADF): When an organization has data sources spread across on-premises databases and cloud storage solutions, I think Azure Data Factory is excellent for integrating these sources. Azure Data Factory's integration with Azure Databricks allows it to handle large-scale data transformations effectively, leveraging the power of distributed processing. For regular ETL or ELT processes that need to run at specific intervals (daily, weekly, etc.), I think Azure Data Factory's scheduling capabilities are very handy. Less Appropriate Scenarios for Azure Data Factory: Real-time Data Streaming - Azure Data Factory is primarily batch-oriented. Simple Data Copy Tasks - For straightforward data copy tasks without the need for transformation or complex workflows, in my opinion, using Azure Data Factory might be overkill; simpler tools or scripts could suffice. Advanced Data Science Workflows: While Azure Data Factory can handle data prep and transformation, in my experience, it's not designed for in-depth data science tasks. I think for advanced analytics, machine learning, or statistical modeling, integration with specialized tools would be necessary.
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Pros
AVEVA
  • Data storage--multiple data sources stream data in real time; it is stored without issue
  • Data retrieval--small to moderate queries and trends are generally fast and efficient
  • Cost effectiveness--it is one of the cheaper (non-enterprise) historian offers and therefore is good value for money (with a reduced feature set)
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Microsoft
  • Once a sales funnel is defined and configured, it can be interconnected to another, so that we can create a complete network where it is possible to monitor the different executions of each funnel linked to our data from one place.
  • Azure Data Factory promotes excellent data management strategies, which we have been able to leverage during the workday, and all thanks to the help provided by their support team, which from the beginning of our interactions kept us properly informed about solutions to every issue that arose.
  • An advantage of Data Factory is that data structures can be stored in several warehouses at the same time, and these can be moved from one warehouse to another by configuring a trigger that is automatically executed when certain predefined parameters are met, such as the generation of a blob within the platform.
  • Azure Data Factory has helped us to carry out data assignments with much more integrity and comfort, and we will continue to use it, given its excellent ease of execution of administrative operations and its incredible approach to business intelligence management.
  • Creating data infrastructures without prior design is entirely possible with Azure, as the platform properly defines all the processes to be followed to create a solid foundation for information and data flows.
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Cons
AVEVA
  • Query performance--for very long-term/large queries; the latest version which we are yet to commission has some improvements in this area
  • User interface--the trend, query, and Excel add-ins are basic and could do with a refresh; web-based clients are a paid add-on and less full featured, so not a true replacement
  • Connectivity--Wonderware System Platform driver packs are required for additional data source types, where native connectors are not provided by other products
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Microsoft
  • For complex JSON when it comes to mapping nested attribute it's not easy to flatten out
  • Data Factory V1 does not have a good implementation experience as compared to V2
  • Work with on premise solutions sometimes is not too friendly because you will need to set a VPN
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Support Rating
AVEVA
No answers on this topic
Microsoft
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
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Alternatives Considered
AVEVA
AVEVA Historian, formerly Wonderware, was the best of the process tier historians in terms of reliability and functionality. It is still under development and not a "dead" product. It is also more cost effective than the more full-featured enterprise historians, such as PI, which our organization is not yet ready for. The feature set is at the right cost level, coupled with current support, were the key factors in the decision.
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Microsoft
The easy integration with other Microsoft software as well as high processing speed, very flexible cost, and high level of security of Microsoft Azure products and services stack up against other similar products.
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Return on Investment
AVEVA
  • Increased efficiency, reduction in labour for preparing reports--data is available to be queried and reported with less effort
  • Increased production efficiency--near real-time data availability and comparisons to historical data has been used to make faster and better operational decisions
  • Increased reliability--data has been used for maintenance optimization and planning purposes
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Microsoft
  • It is very useful and make things easier
  • Debugging can improve
  • Its better suited than other products with the same objective
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ScreenShots