Big Query Omni cross-cloud MVs (Materialized Views)

Cross-cloud MVs let clients simply generate a summary materialized view on GCP from basic data assets on another cloud

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Cross-cloud MVs make data merging and analysis easier regardless of cloud location. They simplify running and managing complex analytics pipelines, large-scale data duplication, and regularly changing data

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Significant cost reduction: It dramatically minimizes data egress costs across clouds by simply transmitting incremental data when needed

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Automatic refresh: Cross-cloud MVs automatically refresh and incrementally update according on user preferences for convenience.

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Unified governance: BigQuery Omni secures and governs materialized views in both clouds. This feature is essential for local and cross-cloud analytics.

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Data scientists in one department want to send daily or weekly data summaries from AWS to Google Cloud (BigQuery) for aggregate analytics and model building

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A marketing analyst wants to weekly combine, de-duplicate, and segment Ads Whiz data from AWS with Google Cloud listener and audience data to enhance audience reach

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Telecom: A data analyst wants to periodically centralize AWS log data and Ads server streaming data for revenue targeting.

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Data analysts must combine AWS product instrumentation data with Google Cloud enterprise data. With new items on their platform, they want to streamline their ETL process and cost issues with cross-cloud MVs

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A marketing analyst must securely link Azure user profile data to Ads Data Hub campaign data. New retail users enter the system daily, thus they employ cross-cloud MVs for combined analysis to keep data current

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Google enable enterprises to break down cloud silos and use their rich, evolving data in near-real-time in Google Cloud using cross-cloud MVs Read more on Govindhtech.com

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