Azure Data Factory and Snowflake Analytics are leading data integration solutions. Azure Data Factory appears to have an edge in terms of versatile deployment options and integration flexibility. However, Snowflake Analytics stands out for its scalability and ease of management.
Features: Azure Data Factory offers extensive integration options with over 100 connectors and robust ETL capabilities, allowing users to build flexible data flow pipelines. It is noted for its good performance and ease of use with a user-friendly interface. Snowflake Analytics, meanwhile, is recognized for its efficient data analytics, performance, and scalability. Its built-in infrastructure requires minimal management, perfect for organizations prioritizing data-driven decision-making.
Room for Improvement: Azure Data Factory could improve its user interface and enhance integration, especially concerning machine learning and third-party tools. There is also room for more transparent pricing and streamlined integration processes. Snowflake Analytics needs better integration with machine learning tools and a more intuitive user interface. Additional support for transactional processing could enhance its real-time data handling capabilities.
Ease of Deployment and Customer Service: Both Azure Data Factory and Snowflake Analytics support multi-cloud deployment, but Azure Data Factory provides greater flexibility, including hybrid and on-premises options. Azure Data Factory is praised for its support, though response times can be slow. Snowflake Analytics generally receives satisfactory support feedback, but there's room for improvement in providing technical assistance for complex deployments.
Pricing and ROI: Azure Data Factory operates on a pay-as-you-go model, which can be complex due to unpredictable usage-based costs. It’s considered cost-effective, offering good ROI through savings on manual effort and infrastructure. Snowflake Analytics offers competitive pricing models including pay-as-you-go and pre-purchased credits, which some users find expensive. However, the solution's scalability and ease of use justify its pricing. Both solutions contribute positively to ROI, though their distinct pricing approaches influence user perspectives differently.
Our stakeholders and clients have expressed satisfaction with Azure Data Factory's efficiency and cost-effectiveness.
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.
The technical support for Snowflake Analytics is excellent based on what I have heard from others.
Azure Data Factory is highly scalable.
It supports both horizontal and vertical scaling effectively.
Storage is unlimited because they use S3 if it is AWS, so storage has no limit.
The solution has a high level of stability, roughly a nine out of ten.
Snowflake Analytics is stable, scoring around eight point five to nine out of ten.
Incorporating more dedicated API sources to specific services like HubSpot CRM or Salesforce would be beneficial.
There is a problem with the integration with third-party solutions, particularly with SAP.
Sometimes, the compute fails to process data if there is a heavy load suddenly, and it doesn't scale up automatically.
If it offered flexibility similar to Oracle and supported more heterogeneous data sources and database connectivity, it would be even better.
AIML-based SQL prompt and query generation could be an area for enhancement.
Navigating the user console can be challenging, particularly when looking for details like the account ID.
The pricing is cost-effective.
It is considered cost-effective.
Snowflake Analytics is quite economical.
Snowflake charges per query, which amounts to a very minor cost, such as $0.015 per query.
Snowflake is better and cheaper than Redshift and other cloud warehousing systems.
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.
I find the most valuable feature in Azure Data Factory to be its ability to handle large datasets.
Snowflake Analytics supports data security with a single sign-on feature and complies with framework regulations, which is highly beneficial.
Running a considerable query on Microsoft SQL Server may take up to thirty minutes or an hour, while Snowflake executes the same query in less than three minutes.
It is a data offering where I can see data lineage, data governance, and data security.
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.
Conventional data platforms and big data solutions struggle to deliver on their fundamental purpose: to enable any user to work with any data, without limits on scale, performance or flexibility. Whether you’re a data analyst, data scientist, data engineer, or any other business or technology professional, you’ll get more from your data with Snowflake.
To achieve this, we built a new data platform from the ground up for the cloud. It’s designed with a patented new architecture to be the centerpiece for data pipelines, data warehousing, data lakes, data application development, and for building data exchanges to easily and securely share governed data. The result, A platform delivered as a service that’s powerful but simple to use.
Snowflake’s cloud data platform supports a multi-cloud strategy, including a cross-cloud approach to mix and match clouds as you see fit. Snowflake delivers advantages such as global data replication, which means you can move your data to any cloud in any region, without having to re-code your applications or learn new skills.
We monitor all Cloud Data Warehouse reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
