Taiwan has controlled the pandemic properly and is now marching towards the new life of the pandemic prevention stage; Taiwan’s outstanding performance of the outbreak prevention has also been reported by international media repeatedly, especially during the outbreak of group infection on the Diamond Princess cruise ship when 2,700 passengers entered Taiwan at that time. The Executive Yuan team used the mobile phone signals of the passengers to analyze their footprints and used the base station number signals of telecommunication operators and geographic location positioning of mobile phones to successfully locate over 620,000 people who had contact with the passengers using big data analysis technology, and then followed up and screened them. In such pandemic prevention work that races against time, only new generation cloud data warehouse technology can be relied on to quickly process large amounts of data in real-time.
QUICK AND EXPANDABLE BIGQUERY THAT DOES NOT REQUIRE INFRASTRUCTURE MANAGEMENT CAN SAVE HALF THE COST FOR ENTERPRISES
Compared to the traditional data warehouse system built locally by enterprises that require large amounts of software/hardware construction costs and infrastructure maintenance manpower, through cloud data warehouse services with their server-less feature, they allow enterprises to focus on data analysis tasks without having to worry about system updates and upgrades or related security issues.
Also according to the survey by ESG, the Data Warehouse service of Google Cloud, BigQuery, can save enterprises 52%~41% total cost of ownership compared to traditional data warehouse systems or the practice of migrating data warehouse systems to IaaS.
Secondly, before querying data with BigQuery, high-speed streaming data can be written to Cloud Bigtable first for data processing. Nowadays there are many sources of data on website user behaviors, real-time mobile devices, and IoT message sources; by having BigQuery perform machine learning, data labels can be read directly, which is the same as converting unstructured data to structured data to accelerate modeling. Thirdly, it takes care of both automation and high availability.
Through the Data Transfer Service tool, SaaS data can be loaded into Bigquery automatically according to schedule for analysis; it also provides high availability copy and storage spaces at several locations automatically and enterprises do not have to pay additional fees to make additional adjustments or settings. Local data warehouse systems always required estimations and allocations of computing resources in the past; if it was found that operations were too slow when query instructions were given, it was more inconvenient to expand resources so suddenly.