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GCP

Google Cloud Platform (GCP)

cloud cloud_provider infrastructure services

1670

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31 peak/qtr
2020-Q1 2026-Q1

Activities

1670 activities · Newest first

Unlocking Access: Simplifying Identity Management at Scale With Databricks

Effective Identity and Access Management (IAM) is essential for securing enterprise environments while enabling innovation and collaboration. As companies scale, ensuring users have the right access without adding administrative overhead is critical. In this session, we’ll explore how Databricks is simplifying identity management by integrating with customers’ Identity Providers (IDPs). Learn about Automatic Identity Management in Azure Databricks, which eliminates SCIM for Entra ID users and ensures scalable identity provisioning for other IDPs. We'll also cover externally managed groups, PIM integration and upcoming enhancements like a bring-your-own-IDP model for Google Cloud. Through a customer success story and live demo, see how Databricks is making IAM more scalable, secure and user-friendly.

Enabling Sleep Science Research With Databricks and Delta Sharing

Leveraging Databricks as a platform, we facilitate the sharing of anonymized datasets across various Databricks workspaces and accounts, spanning multiple cloud environments such as AWS, Azure, and Google Cloud. This capability, powered by Delta Sharing, extends both within and outside Sleep Number, enabling accelerated insights while ensuring compliance with data security and privacy standards. In this session, we will showcase our architecture and implementation strategy for data sharing, highlighting the use of Databricks’ Unity Catalog and Delta Sharing, along with integration with platforms like Jira, Jenkins, and Terraform to streamline project management and system orchestration.

Let's Save Tons of Money With Cloud-Native Data Ingestion!

Delta Lake is a fantastic technology for quickly querying massive data sets, but first you need those massive data sets! In this session we will dive into the cloud-native architecture Scribd has adopted to ingest data from AWS Aurora, SQS, Kinesis Data Firehose and more. By using off-the-shelf open source tools like kafka-delta-ingest, oxbow and Airbyte, Scribd has redefined its ingestion architecture to be more event-driven, reliable, and most importantly: cheaper. No jobs needed! Attendees will learn how to use third-party tools in concert with a Databricks and Unity Catalog environment to provide a highly efficient and available data platform. This architecture will be presented in the context of AWS but can be adapted for Azure, Google Cloud Platform or even on-premise environments.

CDAOs and AI leaders are grappling with two crucial questions: 1. What public cloud provider should we choose for AI and GenAI initiatives, and 2. how do we assemble the right cloud architecture to scale and deploy AI more effectively?
This session compares public cloud AI and Generative AI architectures from AWS, Azure and GCP and provides insights on their points of differentiation.

Looker’s AI-first analytics experience, with a conversational interface, enables all users in your organization to leverage trusted data and make better decisions. Discover how you can lay the foundations to deliver best-in-class conversational AI experiences. Join us, along with a cohort of your peers, to participate in discussions around foundational strategies for conversational AI and share existing use cases and experiences.

AI-powered Data Engineering Agents usher in a new era of data agility. Engage with Google Cloud and your peers to explore the implementation of autonomous data agents and their impact on enterprise agility. From automating data pipelines to ingestion to transformation, discover how to leverage autonomous data agents to build self-managing data ecosystems and accelerate the time from raw data to impactful decisions. This is where data's potential truly meets AI power.

Accelerating AI use cases demands strong data governance and many organizations struggle to manage complex, growing data volumes effectively. This session explores essential strategies for building a solid data governance foundation. Learn how organizations are overcoming common data governance obstacles, like data silos and inconsistent rules, to achieve measurable gains in data quality and efficiency. Through real-world examples, discover how unified data platforms can simplify data discovery, classification, and policy enforcement, leading to faster, data-driven decisions and reduced risk.

AI's potential depends on quality data. Many struggle with AI due to data governance or slow processes, especially with unstructured data. Join peers in discussing strategies for improving and governance to maximise AI potential, managing structured and unstructured data, connecting LLMs with enterprise data and data security best practices.

Just wrapped up a whirlwind tour, giving a workshop in Atlanta and then attending Google Cloud Next. B2b nonstop action, and I'm glad to home for a bit.

While at Next, I had a conversation with another tech old timer friend. We talked about how much we're having using AI as a coding assistant. I'm having fun coming up with wild stuff and seeing if it's possible to build with code. AI's made coding fun again!

📈 This episode is brought to you by GoodData. Design and deploy custom data applications and integrate AI-assisted analytics capabilities wherever your users need them.

For more information, visit https://www.gooddata.com

session
by Moontae Lee (LG AI Research) , Cesar Naranjo (Moloco) , Chelsie Czop (Google Cloud) , Kshetrajna Radhaven (Shopify) , Newfel Harrat (Google Cloud) , Kasper Piskorski, PhD (Technology Innovation Institute)

AI Hypercomputer is a revolutionary system designed to make implementing AI at scale easier and more efficient. In this session, we’ll explore the key benefits of AI Hypercomputer and how it simplifies complex AI infrastructure environments. Then, learn firsthand from industry leaders Shopify, Technology Innovation Institute, Moloco, and LG AI Research on how they leverage Google Cloud’s AI solutions to drive innovation and transform their businesses.

APIs dominate the web, accounting for the majority of all internet traffic. And more AI means more APIs, because they act as an important mechanism to move data into and out of AI applications, AI agents, and large language models (LLMs). So how can you make sure all of these APIs are secure? In this session, we’ll take you through OWASP’s top 10 API and LLM security risks, and show you how to mitigate these risks using Google Cloud’s security portfolio, including Apigee, Model Armor, Cloud Armor, Google Security Operations, and Security Command Center.

Migrating from AWS or Azure to Google Cloud runtimes can feel like navigating a maze of complex services and dependencies. In this session, we’ll explore key considerations for migrating legacy applications, emphasizing the “why not modernize?” approach with a practice guide. We’ll share real-world examples of successful transformations. And we’ll go beyond theory with a live product demo that showcases migration tools, and a code assessment demo powered by Gemini that demonstrates how you can understand and modernize legacy code.

JavaScript gets a lot of flak for not being strongly typed. But if you’re running JavaScript in production today, you don’t need to wait for runtime errors to catch problems. TypeScript has taken JavaScript from a loosely typed language, where a variable can change from a string to a number without warning, and made it strongly typed. Now Zod and Effect are here to tame even the wildest unknown parameters from your users. We’ll demonstrate using these tools in an application and we’ll deploy that application to Google Cloud.

Simplify real-time data analytics and build event-driven, AI-powered applications using BigQuery and Pub/Sub. Learn to ingest and process massive streaming data from users, devices, and microservices for immediate insights and rapid action. Explore BigQuery's continuous queries for real-time analytics and ML model training. Discover how Flipkart, India’s leading e-commerce platform, leverages Google Cloud to build scalable, efficient real-time data pipelines and AI/ML solutions, and gain insights on driving business value through real–time data.

Cloud marketplaces are projected to reach $85B by 2028, with over half involving channel partners (Source: Canalys). Google Cloud Marketplace offers a unique advantage for Channel Partners and ISVs to collaborate and succeed. Learn about the latest platform capabilities to drive deal velocity, new resell offerings and incentives designed to scale growth. Tap into new revenue through the channel and thrive in the evolving cloud marketplace.

Unlock the power of code execution with Gemini 2.0 Flash! This hands-on lab demonstrates how to generate and run Python code directly within the Gemini API. Learn to use this capability for tasks like solving equations, processing text, and building code-driven applications.

If you register for a Learning Center lab, please ensure that you sign up for a Google Cloud Skills Boost account for both your work domain and personal email address. You will need to authenticate your account as well (be sure to check your spam folder!). This will ensure you can arrive and access your labs quickly onsite. You can follow this link to sign up!

In this session, you’ll learn how to build an AI agent using MongoDB Atlas on Google Cloud in just 15 minutes. We’ll cover how to embed and store your data along with vectors in a single database; build a vector search index and run search queries; and implement an AI agent.

This Session is hosted by a Google Cloud Next Sponsor.
Visit your registration profile at g.co/cloudnext to opt out of sharing your contact information with the sponsor hosting this session.

In today’s global media landscape, localizing video content is key to reaching wider audiences. This session demonstrates how to build a robust, end-to-end AI video dubbing pipeline on Google Cloud—covering video ingestion, transcription, translation, captioning, voice cloning, voice synthesis, and quality checks. We’ll explore an architecture that seamlessly integrates GCP products and best practices, highlight market opportunities, and address key challenges in AI dubbing. Attendees will learn how to streamline workflows so users can simply submit their video and receive fully dubbed results. Join us to see how Google tackles dubbing hurdles to maintain its leading edge in global content localization.