So long data silos: Announcing BigQuery Omni cross-cloud joins
Organizations worldwide are dealing with growing volumes of data and distributed data lakes across different cloud platforms. Google launched BigQuery Omni to help customers break down these data silos, with the help of several cross-cloud analytics capabilities including cross-cloud transfer and cross-cloud transform (create table as a select). Already, in the…
Enhancing Google Cloud’s blockchain data offering with 11 new chains in BigQuery
Early in 2018, Google Cloud worked with the community to democratize blockchain data via our BigQuery public datasets; in 2019, we expanded with six more datasets. Today, Google added eleven more of the most in-demand blockchains to the BigQuery public datasets, in preview. And Google making improvements to existing…
Automate your data warehouse migration to BigQuery with new data migration tool
As customers embark on their digital transformation journey, migrating on-premises data warehouses to the cloud accounts for a significant percentage of their overall transformation efforts. Today, to accelerate data warehouse migrations to BigQuery, Google are announcing additional services in the form of a data migration…
Built with BigQuery: How Atlas AI helps build a more resilient future
The footprint of humanity is evolving substantially – from climate change alone scientists estimate that more than 600 million people on the planet have already been stranded outside of habitats that best support life. Other factors such as natural disasters, conflict, a global pandemic, and…
Improving sustainability with our Earth Engine and BigQuery connector
Google are thrilled to announce general availability of a new feature that simplifies the export of tabular data from Google Earth Engine into BigQuery. Earth Engine and BigQuery share a common goal to make large-scale data processing accessible and usable by a wide range of…
Scaling machine learning with BigQuery ML inference engine
As enterprises race to extract value from structured, semi-structured, and unstructured data, they face a continuum of challenges related to data gravity, including data acquisition, data management and data governance. Simultaneously, these companies are also grappling with model gravity as they build and scale machine learning workflows for…
Build limitless workloads on BigQuery: New features beyond SQL
Our mission at Google Cloud is to help our customers fuel data driven transformations. As a step towards this, BigQuery is removing its limit as a SQL-only interface and providing new developer extensions for workloads that require programming beyond SQL. These flexible programming extensions are all offered…
Introducing the Hive-BigQuery open-source Connector
In Google work on the Big Data solutions architecture, BigQuery, and Dataproc teams, we talk to a lot of customers who are interested in migrating all or part of their data warehouse from Apache Hive to BigQuery, but who have hit some speed bumps along the way. To…
Build, automate, and monitor BigQuery ML models with Vertex AI MLOps capabilities
Making machine learning (ML) models work in production is hard. It usually requires not only a deep understanding of ML, data engineering, and software engineering, but also a variety of tools and technologies. BigQuery ML and Vertex AI make it easier to create, deploy, and manage machine learning…
Transform your unstructured data with AI using BigQuery object tables, now GA
Today, the vast majority of data that gets generated in the world is unstructured (text, audio, images), but only a fraction of it ever gets analyzed. The AI pipelines required to unlock the value of this data are siloed from mainstream analytic systems, requiring engineers…
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