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.
rating the customer support at ten points out of ten
Technical support is very costly for me, accounting for twenty-five to thirty percent of the product cost.
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.
It is provided as a pre-configured box, and scaling is not an option.
In general, if I know SQL and start playing around, it will start making sense.
Troubleshooting requires opening each pipeline individually, which is time-consuming.
The cloud version is only available in AWS, and in the Middle East, it is not well-developed in the Azure environment.
The price is perceived as expensive, rated at eight out of ten in terms of costliness.
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.
It operates as a high-speed data warehouse, which is essential for handling big data.
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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