Google Cloud Next '22: Our top 3 takeaways
This year Google Cloud Next was full of exciting announcements, from the launch of new AI agents and Chronicle Security Operations to six new cloud regions and a partner delivery program.
With a strong focus on data analytics, AI, ML, data storage and cybersecurity, there are many ways that enterprises can take advantage of these new technologies to grow their businesses. Here are our main takeaways:
1- Google is building the most open data cloud ecosystem
Google’s Data Cloud continues to expand the industry’s most open, trusted and secure cloud with new integrations to let customers realize the full value of their data. Some new product announcements included:
- Apache Spark integration in preview to accelerate time to value of SQL pipelines from BigQuery. Additional and expanded data format support for industry standards including Apache Iceberg, Delta Lake, and Apache Hudi.
- Datastream for BigQuery in GA lets organizations accelerate real-time data replication - from sources including AlloyDB, PostgreSQL, MySQL and third-party databases like Oracle - directly into BigQuery.
- Expansion of Dataplex for business discovery, governance and trust. This includes new metadata support for GCP-native databases as well as Looker, business context enrichment, support for BigLake asset discoverability, and governance for Spark SQL and Notebooks exploration.
Google continues to integrate and empower all styles of analysis with AI
- Unifying the Looker umbrella for deep integration of Looker, Data Studio, and core Google technologies like AI and ML. Looker Studio (formerly Data Studio) enables free self-service analytics from ad-hoc data sources alongside trusted data vetted and modeled in Looker. This unification democratizes data activation beyond dashboards, empowering workflows and applications with the intelligence needed to help make data-driven decisions.
- Enhancements between Looker and BigQuery with Microsoft Power BI will allow Tableau and Microsoft customers to analyze trusted data from Looker and connect simply with BigQuery.
- A new announcement for Vertex AI Vision extends capabilities of Vertex AI to ingest, analyze, and store visual data in a drag-and-drop interface with a library of pre-trained ML models for common tasks such as occupancy counting, product recognition, and object detection to help speed development of streaming computer vision applications from from weeks to hours at one-tenth the cost of current offerings.
Supporting an open data ecosystem
- Increasing commitment to the Data Cloud Alliance with promotion of open standards and interoperability. This includes new partner integrations to move data between platforms, extend Google’s data cloud capabilities to partner platforms including capabilities in governance and lineage with Dataplex, enhanced federated search on BigQuery and Looker, and support for NoSQL workloads with the power of Vertex AI.
2- Driving business results faster with AI agents
- The announcement of Translation Hub, an AI Agent for self-service document translation in 135 languages, lets researchers share findings and translate content from the most common enterprise document types, instantly reach underserved markets, and facilitate public sector impact in a more connected, inclusive world.
- Document AI Workbench removes barriers to building custom document parsers, helping organizations extract fields of interest that are specific to their business needs, requiring less training data, and in a simple interface for labeling and one-click model training.
- Document AI Warehouse extends Google’s native Search technologies to Document AI, simplifying document search and management to accommodate invoice processing, contracts, approvals and custom workflows.
- Contact Center AI Platform now in GA provides additional deployment choice and flexibility with turnkey integrations to contact centers. Its deeper integration with Google CCAI offerings, like Insights and Dialogflow along with mobile-first and CRM-centric design helps keep agents focussed on the customer.
3- Building a secure software supply chain with Software Delivery Shield
Software Delivery Shield includes capabilities across five different areas to address security concerns along the software supply chain: application development, software “supply,” continuous integration (CI) and continuous delivery (CD), production environments, and policies. It also allows for an incremental adoption path, so organizations can tailor the solution to their specific needs, choosing the preferred tools to start with based on their existing environment and security priorities.
- Develop fast, securely: Cloud Workstations provides fully managed development environments on Google Cloud. With Cloud Workstations, developers can access secure, fast, and customizable development environments via a browser anytime and anywhere, with consistent configurations and customizable tooling. At the same time, IT and security administrators can easily provision, scale, manage, and secure the development environments on Google Cloud’s infrastructure.
- Safeguard the software 'supply': Securing the software supply — build artifacts and application dependencies — is another critical step in improving software supply chain security. The pervasive use of open source software makes this problem particularly challenging. Assured Open Source Software is Google’s first “curated” open source where we are adding a layer of accountability on top of today’s free or “as-is” open source. With Software Delivery Shield, DevOps teams can store, manage and secure the build artifacts in Artifact Registry, and also proactively detect vulnerabilities with the integrated scanning provided by Container Analysis, to which we’ve added support for more language packs.
- Lock down the CI/CD pipeline: Cloud Build now officially supports SLSA Level 3 builds, implementing SLSA level 3 best practices by default. In addition to providing ephemeral and isolated build environments, Cloud Build can now generate authenticated and non-falsifiable build provenance for both containerized applications and non-containerized Maven and Python packages, as well as display security insights for built applications.
- Help protect applications in production: New built-in security posture management capabilities for GKE, currently in Preview, to help identify and fix security concerns in GKE clusters and workloads. Based on industry standards and the GKE team's security expertise, GKE now provides detailed assessments, assigns severity ratings, and advises on the security posture of the clusters and workloads, including insights into OS vulnerabilities and workload configurations.
- Build a chain of trust through policy: Binary Authorization is a deploy-time security control that can ensure only trusted container images are deployed on GKE or Cloud Run. With Binary Authorization, DevOps or security teams can require images to be signed by trusted authorities during the development process and then enforce signature validation when deploying.
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