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.
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Azure Data Factory
Score 8.5 out of 10
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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.
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.
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.
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.
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
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
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.
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.
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
