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medium.com/v2/resize:fit:320/1*s1M8ondCwffP3uExWjLhzw.jpeg)"></div> </div> </div> </a> </div><p id="931a">With announcing the launch of Cross Cloud Materialized Views, Google wants to help customers to realize cross cloud analytics- or to be more exact: BigQuery Omni cross-cloud materialized views (aka cross-cloud MVs)[1].</p><p id="af2b">Here is the official announcement of Google[1]:</p><p id="2f92"><i>Cross-cloud MVs allow customers to very easily create a summary materialized view on GCP from base data assets available on another cloud. Cross-cloud MVs are automatically and incrementally maintained as base tables change, meaning only a minimal data transfer is necessary to keep the materialized view on GCP in sync. The result is an industry-first capability that empowers customers to perform frictionless, efficient, and cost-effective cross-cloud analytics.</i></p><p id="d998">This new feature offers new use cases when working with BigQuery like[1][2]:</p><ul><li><b>Predictive analytics:</b> You can use Google Cloud AI/ML services like Vertex AI integration for external data.</li><li><b>Better Data Compliance:</b> There’s an emerging set of privacy use cases where raw data cannot leave the source region, but at least now you can query it directly, no need for a data integration.</li><li><b>Marketing analytics:</b> Organizations often find themselves combining data sources from various cloud platforms.</li><li><b>Real Time Analytics:</b> Cross-cloud MVs enables data to be always updated to the source system.</li></ul><p id="943c">This approach is in line with the so-called Zero ETL approach that other cloud providers are al

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so focusing on. It’s about offering customers tools that offer easy data integration or directly querying the data like in this example.</p><div id="3079" class="link-block"> <a href="https://readmedium.com/amazon-declares-war-on-etl-9bf434bcf49e"> <div> <div> <h2>Amazon declares War on ETL</h2> <div><h3>How AWS promotes the Zero ETL Approach</h3></div> <div><p>medium.com</p></div> </div> <div> <div style="background-image: url(https://miro.readmedium.com/v2/resize:fit:320/1*sGOVfZJ8DGcDM2G5DqdZaA.jpeg)"></div> </div> </div> </a> </div><p id="ebfd">The benefits of this new feature in BigQuery Omni are a simplified process of combining and analyzing data regardless of whether the data assets live on different clouds, significant cost reduction due to reduced data transfers and storage and automatic refresh of the data. So definitely good news for all BigQuery users which have to analyze data from other clouds and also an opportunity for all companies which use other cloud services for data analytics but want to use BigQuery in the future.</p><h2 id="6e04">Sources and Further Readings</h2><p id="180b">[1] Google, <a href="https://cloud.google.com/blog/topics/inside-google-cloud/whats-new-google-cloud">Cross Cloud Materialized Views</a> (2023)</p><p id="a6a5">[2] Google, <a href="https://cloud.google.com/blog/products/data-analytics/introducing-bigquery-omni-cross-cloud-materialized-views">Cross-cloud materialized views in BigQuery Omni enable multi-cloud analytics at scale</a> (2023)</p></article></body>

Google launched Cross Cloud Analytics

How Cross Cloud Materialized Views for BigQuery overcome Cloud Barriers

Photo by Modestas Urbonas on Unsplash

With Google BigLake, Google has started its journey to offer Google Cloud and BigQuery customers also possibilities to query data directly from other cloud databases. Now, they have announced Cross Cloud Materialized Views.

To build up a decent Data Lakehouse these days, companies often have to integrate or directly query data also from databases and services from other cloud providers like Microsoft Azure or AWS. With BigLake and Omni Google offers solutions to do so. You can read more about it in the article below:

With announcing the launch of Cross Cloud Materialized Views, Google wants to help customers to realize cross cloud analytics- or to be more exact: BigQuery Omni cross-cloud materialized views (aka cross-cloud MVs)[1].

Here is the official announcement of Google[1]:

Cross-cloud MVs allow customers to very easily create a summary materialized view on GCP from base data assets available on another cloud. Cross-cloud MVs are automatically and incrementally maintained as base tables change, meaning only a minimal data transfer is necessary to keep the materialized view on GCP in sync. The result is an industry-first capability that empowers customers to perform frictionless, efficient, and cost-effective cross-cloud analytics.

This new feature offers new use cases when working with BigQuery like[1][2]:

  • Predictive analytics: You can use Google Cloud AI/ML services like Vertex AI integration for external data.
  • Better Data Compliance: There’s an emerging set of privacy use cases where raw data cannot leave the source region, but at least now you can query it directly, no need for a data integration.
  • Marketing analytics: Organizations often find themselves combining data sources from various cloud platforms.
  • Real Time Analytics: Cross-cloud MVs enables data to be always updated to the source system.

This approach is in line with the so-called Zero ETL approach that other cloud providers are also focusing on. It’s about offering customers tools that offer easy data integration or directly querying the data like in this example.

The benefits of this new feature in BigQuery Omni are a simplified process of combining and analyzing data regardless of whether the data assets live on different clouds, significant cost reduction due to reduced data transfers and storage and automatic refresh of the data. So definitely good news for all BigQuery users which have to analyze data from other clouds and also an opportunity for all companies which use other cloud services for data analytics but want to use BigQuery in the future.

Sources and Further Readings

[1] Google, Cross Cloud Materialized Views (2023)

[2] Google, Cross-cloud materialized views in BigQuery Omni enable multi-cloud analytics at scale (2023)

Data Science
Google
Cloud
Technology
Bigquery
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