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A financial services company needs to implement a data pipeline for transactional data, which must be cleansed and validated for compliance purposes before loading into BigQuery. They also plan to conduct further analytics on the data after loading. Which data manipulation methodology is the most appropriate for these requirements?
A company wants to migrate a large dataset of historical data from its on-premises storage to Cloud Storage. The dataset is around 100 TB, and the company has a limited network bandwidth, which would make online transfer highly time-consuming and resource-intensive. They need a solution that allows them to quickly and securely transfer this data to Google Cloud without causing disruptions to their ongoing operations. Which data transfer tool should the company choose?
A retail business analyst needs to create a dashboard in Looker to monitor the weekly sales performance across various product categories. The business stakeholders require a single-view dashboard that shows key performance metrics such as total revenue, average order value, and weekly growth rates. Which action should the analyst take to best meet these business requirements in Looker?
Which of the following is the best use case for using BigQuery ML to develop a machine learning model?
You are working with a client who wants to extract data from various public APIs and load it into a Google Cloud service. The data should be stored in a highly durable, scalable, and cost-effective manner. Additionally, the data might later be integrated with BigQuery for advanced analytics. Which service would be best suited for the initial storage of this data?
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