Item: Azure Data Factory
Rating: 10
Author: Murray Clear

Overall Satisfaction with Azure Data Factory

Use Cases and Deployment Scope

Thanks to the implementation of Azure as software for our data integrations we have been able to transport different data structures to the cloud without losing any content, with entirely automated movements that we have been able to leverage in several ways: one of them was to streamline the creation of funnels, so that we could transform the data before transporting it. It does not require prior configuration of some kind of ETL process to write code, since the platform lets you do it by itself, and without the need to necessarily create an environment where data traffic is intuitive. That is to say, it can be used to create varied programming environments. Basically creates a layer where data is integrated in different ways, whether or not business intelligence or IT mechanics are being used to integrate with analytics software. Creates very fast transformations where data mapping can be managed so that it carries some level of intelligence, and data source copy operations require less manual review and management.

Pros and Cons

Pros

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.

Cons

If you are working with Azure synapse Analytics you have to keep in mind that the collection of all local data is not properly handled when integrating it to data Factory, and this can result in problems in the long run if you have not been able to transform the data into other streams.
There are very few data sets allowed by the platform, the most adaptable being blob and SQL as tables, but it leaves out many other dynamics that are currently used, such as string and raw SQL data coming from database services like MongoDB.
It is not recommended for working with machine learning mechanics, mostly because the reports handled within the platform have restructurable formats, but do not adapt correctly to services such as PowerBI.

Return on Investment

Since Azure Data Factory does not directly handle operations such as sales or revenue, it has been a bit difficult to calculate how big the impact has been on our return on investment.
After some research, we found that labor results have increased threefold since we started using Azure Data Factory to move data and streamline employee work.

Alternatives Considered

NONE

Key Insights

Do you think Azure Data Factory delivers good value for the price?

Yes

Are you happy with Azure Data Factory's feature set?

Yes

Did Azure Data Factory live up to sales and marketing promises?

Yes

Did implementation of Azure Data Factory go as expected?

Yes

Would you buy Azure Data Factory again?

Yes

Likelihood to Recommend

Suitable for virtually any scenario that involves working with linked Service, so that it is possible to connect Azure Data Factory to storage services for SQL data in the cloud, and that it is possible to access them at any time. Serves to make the information much cleaner and have the desired formats and structures in case of working with time tracking or general employee work tracking applications, so it is easy to create accurate and aesthetically acceptable analysis for general tracking software. It is extremely useful if you do not have any data server service, as Azure Data Factory has the ability to maintain almost 90 different data connectors without the need to use a server.

Azure Data Factory Feature Ratings

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