Azure Data Factory and BigQuery are competitors in the data management and analytics category. Azure Data Factory holds an advantage in data integration and orchestration, while BigQuery excels in complex querying and analytics performance.
Features: Azure Data Factory offers a drag-and-drop interface for simplified data transformations, integration with SAP and databases, and seamless Azure ecosystem connectivity. BigQuery provides advanced SQL querying, high performance for large-scale data analytics, and strong integration with GCP products.
Room for Improvement: Azure Data Factory users seek better machine learning capabilities, improved API handling, and enhanced user interface elements. BigQuery could benefit from increased user-friendliness, simplified query handling, and extended local data residency options.
Ease of Deployment and Customer Service: Azure Data Factory supports hybrid and on-premises deployments, making it versatile for diverse infrastructures, while BigQuery is optimized for public cloud deployment within the Google ecosystem. Both offer comprehensive support; however, Azure's support can sometimes be slow, and BigQuery could improve proactive service.
Pricing and ROI: Azure Data Factory's pay-as-you-go pricing is competitive but complex as data volumes grow, justified by efficiencies gained. BigQuery's pricing is affordable yet flexible, offering cost-effectiveness for significant data workloads and substantial ROI from large-scale analytics.
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.
rating the customer support at ten points out of ten
Azure Data Factory is highly scalable.
The scalability is definitely good because we are migrating to the cloud since the computers on the premises or the big database we need are no longer enough.
The solution has a high level of stability, roughly a 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.
Troubleshooting requires opening each pipeline individually, which is time-consuming.
In general, if I know SQL and start playing around, it will start making sense.
The pricing is cost-effective.
It is considered cost-effective.
The price is perceived as expensive, rated at eight out of ten in terms of costliness.
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.
The interface of Azure Data Factory is very usable with a more interactive visual experience, making it easier for people who are not as experienced in coding to work with.
It is really fast because it can process millions of rows in just a matter of one or two seconds.
BigQuery processes a substantial amount of data, whether in gigabytes or terabytes, swiftly producing desired data within one or two minutes.
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.
BigQuery is an enterprise data warehouse that solves this problem by enabling super-fast SQL queries using the processing power of Google's infrastructure. ... You can control access to both the project and your data based on your business needs, such as giving others the ability to view or query your data.
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