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Explainer

Can Your Data Warehouse Handle a 100-Trillion Row Query?

Today’s enterprise demands from data go far beyond the capabilities of traditional data warehousing and for many leaders, the need to digitally transform their businesses is a key driver for data analytics spending.

Businesses want to make real-time decisions from fresh information as well as make future predictions from their data in order to remain competitive.

In this video, Jordan Tigani, Director of Product Management, Google BigQuery reveals the power of Google Cloud’s modern data warehouse, BigQuery, that helps businesses make informed decisions quickly.

In addition, he talks about how big Google BigQuery can get. He shares examples of how one customer ran a query against a giant table of 100 trillion rows. “I think it was something like 19 petabytes of data scanned. It took about took about 20 minutes. It used 39,000 slots, which is about 20,000 cores,” says Tigani.

He also shares examples of how businesses, such as online retailer, Zulily generate real business benefits from being able to query large datasets faster, and more easily than ever–without having to invest time managing infrastructure.

Finally, Amir Aryanpour, Technical Architect, Channel 4, talks abouut how connecting connecting Google BigQuery to other solutions with the Google Cloud Platform, including storage, data visualisation, and a sentiment analysis engine, among others, helped the company.

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BigQuery is Google Cloud's enterprise data warehouse designed to help you ingest, store, analyze, and visualize big data with ease. Organizations rely on data warehouses to aggregate data from disparate sources, process it, and make it readily available for data analysis that supports their strategic decision-making. You can ingest data

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