Google, on the occasion of its third Google Data Cloud & AI Summit, announced on its blog product innovations and partner offers able to: optimize the price/performance ratio; foster the adoption of open ecosystems; securely set data standards; deliver the benefits of AI and ML to existing data, engaging a dynamic ecosystem of partners.
The main innovations that Google intends to bring in terms of data cloud and AI, will allow customers to:
Let’s see them in detail.
With a rapidly evolving marketplace, organizations need smarter systems that deliver efficiency and flexibility. For this reason, Google introduced the new pricing editions of BigQuery, as well as some innovations for autoscaling and a new compressed storage billing model.
The new editions of BigQuery allow for greater choice and flexibility, allowing you to select the feature set that best suits the needs of various workloads and to combine Standard, Enterprise and Enterprise Plus editions to obtain the ideal price-performance ratio based on the workload.
The new editions of BigQuery include the ability to take out annual or multi-year subscriptions at lower prices and the new autoscaling feature supports unpredictable workloads, offering the option to pay only for the compute capacity used.
Additionally, BigQuery leverages the power of a serverless architecture to deliver additional capacity in granular increments, as well as a new compressed storage billing model for customers using BigQuery editions.
For some, cutting costs means switching expensive legacy databases. But sometimes workloads are locked into on-premises data centers due to regulatory or data sovereignty requirements, or because apps are managed at the edge.
So many customers need a path to support their ongoing modernization with AlloyDB, Google’s PostgreSQL-compatible high-performance database downloadable with AlloyDB Omni.
AlloyDB Omni offers all the benefits of AlloyDB and is found to be twice as fast as standard PostgreSQL.
Data driven companies need to be able to trust the data contained in their business intelligence (BI) tools, so Google has devised Looker Modelera metrics hub that can be shared with the BI tools of your choice, such as PowerBI, Tableau, Thoughtspot, Connected Sheets, and Looker Studio.
In addition to Looker Modeler, there is also BigQuery data clean roomto help organizations share and match datasets with each other while respecting user privacy.
BigQuery ML enables data analysts to leverage machine learning through existing SQL tools and skills. It also reported more than 200% usage growth in 2022.
Last but not least, to bring machine learning closer to enterprise data, Google announced new features in BigQuery, which will allow users to import models like PyTorch, host remote models on Vertex AI, and run pre-trained models from Vertex AI.
As part of our open ecosystem for AI development, the company is announcing new partnerships that will give customers more choice and the ability to transform data into insights through AI and ML, including new integrations between:
Over 900 software partners rely on Google Data Clouds, introducing new data-driven tools for their customers. Some of the updates made by Google’s cloud data partners include:
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