Find out in this report how the two Cloud Data Warehouse solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
Our stakeholders and clients have expressed satisfaction with Azure Data Factory's efficiency and cost-effectiveness.
It's not structured support, which is why we don't use purely open-source projects without additional structured support.
The technical support from Microsoft is rated an eight out of ten.
The technical support is responsive and helpful
The technical support for Azure Data Factory is generally acceptable.
It is a distributed file system and scales reasonably well as long as it is given sufficient resources.
Azure Data Factory is highly scalable.
Continuous management in the way of upgrades and technical management is necessary to ensure that it remains effective.
The solution has a high level of stability, roughly a nine out of ten.
The problem with Apache Hadoop arose when the guys that originally set it up left the firm, and the group that later owned it didn't have enough technical resources to properly maintain it.
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.
Incorporating more dedicated API sources to specific services like HubSpot CRM or Salesforce would be beneficial.
Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically.
The pricing is cost-effective.
It is considered cost-effective.
If you don't do the upgrades, the platform ages out, and that's what happened to the Hadoop content.
Apache Hadoop helps us in cases of hardware failure because it works 24/7, and sometimes servers crash in the field.
It connects to different sources out-of-the-box, making integration much easier.
The orchestration features in Azure Data Factory are definitely useful, as it is not only for Azure Data Factory; we can also include DataBricks and other services for integrating the data solution, making it a very beneficial feature.
The platform excels in handling major datasets, particularly when working with Power BI for reporting purposes.
| Company Size | Count |
|---|---|
| Small Business | 14 |
| Midsize Enterprise | 8 |
| Large Enterprise | 21 |
| Company Size | Count |
|---|---|
| Small Business | 31 |
| Midsize Enterprise | 19 |
| Large Enterprise | 57 |
Azure Data Factory efficiently manages and integrates data from various sources, enabling seamless movement and transformation across platforms. Its valuable features include seamless integration with Azure services, handling large data volumes, flexible transformation, user-friendly interface, extensive connectors, and scalability. Users have experienced improved team performance, workflow simplification, enhanced collaboration, streamlined processes, and boosted productivity.
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