AVEVA Historian vs. Azure Data Factory

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.

Incentivized

Azure Data Factory is a great data integration tool for developing a cloud data platform, especially within the Azure ecosystem. Azure Data Factory is very good for the Data Ingestion part. It can work for simple data transformation with its Data Flow, but it will also need cluster configuration, and there is some cost. Also, it is an excellent tool for orchestrating data pipelines. But for complex data transformations, you may need to use technologies like Databricks and PySpark.

Incentivized

• 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)

Incentivized

• Azure Data Factory supports a vast array of source and destination connectors, both from within the Microsoft ecosystem (like Azure Blob Storage, Azure SQL Database, Azure Cosmos DB) and external platforms (like Amazon S3, Google Cloud Storage, SAP, Salesforce, and many more).
• Azure Data Factory's Mapping Data Flows provides a code-free environment to design data transformations visually. Users can drag and drop elements to create complex ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes without needing to write any code.
• Azure Data Factory provides a unified monitoring dashboard that offers a holistic view of all pipeline activities. I think this makes it easier for users to track the status of various jobs, identify failures, and pinpoint bottlenecks.

Incentivized

• 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

Incentivized

• 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

Incentivized

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

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.

Incentivized

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.

Incentivized

• 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

Incentivized

• It is very useful and make things easier
• Debugging can improve
• Its better suited than other products with the same objective

Incentivized