Azure Data Factory and Alteryx Designer compete in the ETL and data integration market, with Azure offering robust scalability and Alteryx focusing on ease of use. Azure Data Factory seems to have the upper hand in handling large-scale deployments, whereas Alteryx is favored for its user-friendly interface for quicker, straightforward data manipulations.
Features: Azure Data Factory includes over 100 built-in connectors, supports drag-and-drop, and integrates well with GitHub and Databricks, enhancing its capacity for data flows and transformations. Alteryx Designer is appreciated for its intuitive drag-and-drop functionality, strong data preparation and transformation capabilities, and ease of creating workflows without coding.
Room for Improvement: Azure Data Factory could improve its integration with existing Azure services and machine learning tools, as well as enhance its documentation. Users express a need for simplified pricing and better real-time functionality. Alteryx Designer faces criticism for its high cost, especially in automation, and desires for better scalability and collaboration features.
Ease of Deployment and Customer Service: Azure Data Factory benefits from its cloud-native structure, offering diverse deployment options including public and hybrid cloud environments, with generally satisfactory customer service. Alteryx Designer, often used on-premises, is straightforward to deploy, though feedback indicates room for better user support.
Pricing and ROI: Azure Data Factory's usage-based pricing can complicate cost forecasting but is viewed as cost-effective with significant ROI through infrastructure savings. Alteryx Designer is seen as expensive, particularly as usage scales, yet provides time-saving benefits. Both products yield positive ROI, with Azure's larger-scale deployments being more beneficial in reducing long-term costs.
Our stakeholders and clients have expressed satisfaction with Azure Data Factory's efficiency and cost-effectiveness.
There are areas where they need to improve response time and overall competence.
The technical support is responsive and helpful
The technical support from Microsoft is rated an eight out of ten.
The technical support for Azure Data Factory is generally acceptable.
Azure Data Factory is highly scalable.
The solution has a high level of stability, roughly a nine out of ten.
Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically.
Azure Data Factory should consider how to enhance integration or filtering for more transformations, such as integrating with Spark clusters.
The inability to connect local VMs and local servers into the data flow is a limitation that prevents giving Azure Data Factory a perfect score.
It's cheaper than Palantir, but even Alteryx is too much for small clients.
The pricing is cost-effective.
It is considered cost-effective.
The main valuable aspect is the simplicity of use across all features.
Regarding the integration feature in Azure Data Factory, the integration part is excellent; we have major source connectors, so we can integrate the data from different data sources and also perform basic transformation while transforming, which is a great feature in Azure Data Factory.
The platform excels in handling major datasets, particularly when working with Power BI for reporting purposes.
It connects to different sources out-of-the-box, making integration much easier.
| Product | Market Share (%) |
|---|---|
| Azure Data Factory | 5.6% |
| Alteryx Designer | 1.9% |
| Other | 92.5% |
| Company Size | Count |
|---|---|
| Small Business | 12 |
| Midsize Enterprise | 3 |
| Large Enterprise | 17 |
| Company Size | Count |
|---|---|
| Small Business | 31 |
| Midsize Enterprise | 19 |
| Large Enterprise | 55 |
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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