Google Cloud Unveils Enhancements to its Database, Analytics, and AI Offerings
Google Cloud has announced a wave of improvements to its database, analytics, and artificial intelligence offerings, unveiling a series of features that will enable users to optimize performance, reduce costs, and streamline operations. The improvements announced include new pricing and commitment models for the company’s BigQuery analytics tool, as well as enhancements to its flagship machine learning offering, BigQuery ML. These new offerings provide additional flexibility and customization, enabling users to better tailor their analytics to their individual business needs.
One of the most significant updates to the Google Cloud platform is the introduction of new pricing and commitment models for BigQuery. Users can choose from three editions: Standard, Enterprise, and Enterprise Plus. These packages are designed to match analytics pricing with required performance, enabling users to select the optimal configuration for their business requirements. Additionally, customers with predictable workloads can purchase BigQuery editions for single or multi-year commitments, while those with unpredictable workloads can choose an auto-scaling package that only requires payment for the compute capacity used.
To reduce costs for customers, Google Cloud is introducing a new “compressed storage billing” model that reduces the cost of storage for BigQuery customers. With this innovation, the company is making it easier and more cost-effective to store and retrieve data, making analytics more accessible to organizations of all sizes. This new feature aligns with Google Cloud’s commitment to providing a cost-effective, scalable, and secure platform for businesses in any industry.
Google Cloud is also unveiling new capabilities for its machine learning offering, BigQuery ML. One of the most notable features is the capability to import models from PyTorch, a popular open-source machine learning framework for deep learning. This new feature enables users to build and train models in their preferred environment, then import the finished model into BigQuery ML for deployment. In addition, BigQuery ML now allows users to run pre-trained models from Vertex AI, providing even more capabilities to users in the machine learning space.
Another significant feature announced by Google Cloud is the introduction of a “LookerML” developer tool for Looker, the company’s business intelligence tool. LookerML enables developers to automate the creation of dashboards, reports, and other visualizations, making it easier to develop and deploy data analytics solutions. With this new tool, Looker users can save time on routine tasks, allowing them to focus on more complex challenges, such as data modeling and analysis.
Finally, Google Cloud is introducing new data protection and security features for its cloud platform, including new data encryption and key management capabilities. These features include tools for data de-identification, key management, and security analytics, providing businesses with additional layers of protection for sensitive data.
Google Cloud’s updated database, analytics, and artificial intelligence offerings provide businesses with new tools to optimize performance, reduce costs, and streamline operations. The new pricing and commitment models for BigQuery enable users to match analytics pricing with required performance, while the compressed storage billing model reduces the cost of storage for BigQuery customers. The enhancements to BigQuery ML, Looker, and the cloud platform’s data protection and security capabilities provide even more capabilities to users of Google Cloud. As the cloud computing market expands, Google Cloud continues to be at the forefront of innovation, providing businesses with the tools they need to succeed.