Name: How Fullstory makes every click count with Google Cloud
Start: 2024-07-11T15:00:00Z
End: 2024-07-11T15:00:51.000Z
Location: BrightTALK

Google’s Agentic Data Cloud turns your data foundation into a real-time System of Action while delivering leading total-cost-of-ownership (TCO). Grounded in universal business context, our open, AI-native solution fuels autonomous workflows across your entire data team—from app developers, data engineers, and analysts to data scientists, ML engineers, and business leaders. Discover how Google Cloud can help you transform your business for the Agentic Era.

Enterprise data leaders face an aggressive mandate: deploy generative AI and autonomous agents that drive real operational efficiency. Yet, as teams move past isolated pilots into production, they run into a hard architectural barrier. That barrier? Data gravity.

For years, enterprises invested heavily in decoupled analytics stacks—separating underlying storage from complex, third-party compute clusters. While this approach offered initial flexibility, it has introduced a steep operational debt that must be paid. Highly skilled data engineers spend their working hours sizing compute clusters, managing idle capacity, and building fragile pipelines to shuffle data across security boundaries. Autonomous AI agents cannot reason effectively over stale batch pipelines, and moving massive datasets to external processing layers inflates cloud spend while compromising data governance.

Drawing from fresh enterprise survey data and real-world customer migration benchmarks, this session breaks down:

The hidden financial and human toll of running decoupled compute layers over static storage, from network egress fees to engineering burnout.

Why the agentic era requires in situ processing, executing analytical queries, vector search, and model reasoning directly where data natively lives.

Practical architectural patterns for modernizing Apache Spark workloads, embracing open formats like Apache Iceberg, and leveraging AI-assisted tooling to compress migration timelines from years to weeks.

Escaping Data Gravity and Infrastructure Debt: Why the AI Era Demands an Agentic Data Cloud

In the agentic era, data comes from many different sources—operational data, analytical data, vector data, and more. Integrating all that data for your AI projects can be hard and expensive work, but it doesn’t have to be. With the power of vibe coding and agentic AI, your operational database might be all you need as your AI foundation.



We’ll explore how AlloyDB for PostgreSQL brings you the best of Google and 100% PostgreSQL compatibility to help you build AI apps and agents directly on live, operational data. AlloyDB offers accelerated query performance plus hybrid search, natural language processing, and AI model integration, while keeping your data in one place and avoiding complex data pipelines.



We’ll demonstrate how a unified PostgreSQL layer with the power of Gemini models lets your team bypass architectural bloat, keep transactional, analytical,

and vector search workloads on one platform, and build secure, highly responsive applications and agents.

Power intelligent apps and agents where your operational data already lives

As we move beyond simple "human-to-AI" chats into the era of agentic AI and autonomous systems, we must embrace a new agentic data foundation paradigm for enterprise intelligence. Because AI systems reason differently than human analysts, they don’t just need metrics presented in charts; they need concepts, relationships, and inference capabilities. To support these new reasoning engines, your underlying data platform must transform from fragmented silos into a unified "intelligent central nervous system".

Join us for an exclusive webinar as we dive into Google's Agentic Data Cloud. We will explore how to build the core data foundation necessary to seamlessly combine foundational model knowledge with your unique enterprise knowledge.

In this session, we'll cover:

- Witnessing Agentic AI in Real-Time
- Executing Autonomous Processes
- Connecting Your Entire Data Ecosystem
- Establishing a Single Source of Truth
- Extracting Value from Unstructured Data

The Intelligent Enterprise - Building the foundation for agentic consumption

Many enterprise data teams are held back by the operational overhead and licensing costs of legacy on-premises Cloudera environments. Managing physical hardware, tuning YARN, patching software, and maintaining rigid HDFS structures consume engineering hours that should be spent on innovation. As businesses enter the agentic era, these static platforms cannot handle the query concurrency and real-time processing demands of autonomous AI agents.



This webinar outlines the technical pathway to migrate your Cloudera workloads to Google Cloud. By transitioning to a borderless lakehouse powered by Managed Service for Apache Spark and Lakehouse for Apache Iceberg, you can decouple storage from compute and eliminate administrative complexity. We will show you how to convert legacy pipelines into an elastic, zero-ops data foundation designed to ground AI agents in real-time business context without data duplication.



What you will learn:

Eliminate legacy licensing and cluster management: Discover how to transition to a serverless deployment mode that starts in under 60 seconds with zero-scale worker pools, reducing costs by 18% to 60% compared to alternative managed clusters.
Boost query performance with Lightning Engine: Learn how to bypass JVM bottlenecks by compiling Spark physical plans directly into native C++ instructions, delivering up to 4.9x faster execution with zero code changes.
Unify metadata with the Lakehouse runtime catalog: See how to replace Hive Metastore with a standalone Iceberg REST Catalog, establishing secure, zero-copy, multi-engine interoperability between Spark and BigQuery to prevent AI hallucinations.

Modernizing from Cloudera to the Google Data Cloud

Discover how to build agents with the ADK that accesses BigQuery for analysis. Join us to learn about Agent Development Kit (ADK) and Model Context Protocol (MCP) integration methods that standardize how agents connect to your data while removing the need to build custom database connectors from scratch. Learn how to implement agent operations (AgentOps) for deep observability into both agent performance and cost with a single line of code.

Agent development and AgentOps with BigQuery, ADK, and MCP

The next generation of AI is moving beyond simple chatbots and basic prompting to become truly agentic. The next generation of AI is moving beyond simple chatbots and basic prompting to become truly agentic.

But for autonomous AI agents to reason effectively and execute complex tasks, they need more than just a powerful foundation model —they require deep, highly relevant, and real-time context.

Join us for a deep dive into building agentic AI applications using Spanner. In this webinar, we will explore how Spanner’s unified, multi-model capabilities eliminate data silos and provide the ultimate foundation for intelligent agents. Discover how to leverage hybrid search (combining full-text and semantic search) alongside advanced graph capabilities to surface the exact context your agents need to deliver accurate, grounded results.

We’ll also showcase how seamlessly Spanner integrates into your AI development stack. Learn how Knowledge Catalog and the Model Context Protocol (MCP) allow your agent applications to effortlessly and securely tap into Spanner's rich data. Finally, we will cover how Spanner Omni, a downloadable version of Spanner, gives you the flexibility to run this multi-model, distributed database in your data centers, other clouds, and your laptop.

Delivering Deep Context for Agentic AI with Spanner’s Multi-Model Capabilities

Join us for a deep-dive session into the future of enterprise AI. We will unveil our most recent launches and share our upcoming roadmap for the Gemini Enterprise Agent Platform, designed specifically to help organizations of all sizes build a more efficient, secure, and intelligent organization.

Gemini Enterprise Product Roadmap: Collaborative, Flexible, and In Your Control

Organizations have too many tools that don’t work together. How can you remove the barriers between tools and finally connect the data in your organization to bring together data across sales, product, supply chain and marketing? The Gemini Enterprise app connects your data, wherever it lives, enabling you to create, work with, and govern AI agents in a secure and scalable way.

Gemini Enterprise app: AI for Every Employee and Workflow

Legacy architectures designed for human speed can’t survive the shift to agent scale. You must evolve from a system of intelligence to a system of action. Andi Gutmans and Yasmeen Ahmad will introduce the agentic data cloud—the world’s first architecture built to power the physics of autonomy. Spanning AI-powered analytics, data science, and agentic development, we’ll show you how to activate data anywhere in an open ecosystem, enforce universal knowledge to close the trust gap, and deploy an AI-native stack that solves the cost of scale.

The agentic data cloud: Powering a system of action

Join us for a live webinar to explore advancements in Looker, Google Cloud's business intelligence (BI) platform, and discover how we are reimagining data-driven decision-making for the agentic era, transforming Looker into the intelligent experience layer that powers trusted AI across the enterprise.

In this session, we will dive into our 2026 product vision and roadmap, including all the latest announcements from Google NEXT' 26, showcasing how Looker is empowering organizations to eliminate the BI backlog and democratize expertise. We’ll demonstrate how Conversational Analytics, high-reasoning agents, and an open, governed platform are moving BI beyond the dashboard and directly into the flow of work.

Key highlights will include:

- Next-Gen Conversational Analytics: Unlocking instant data fluency with agents providing naturally grounded answers, superior reasoning, and new admin tools for observability and accuracy
- AI-Powered Self-Service: Democratizing data through a modern interface and AI assistants that empower every user to share insights globally
- Open Platform & Trust: Ensuring LookML remains the governed source of truth. Introducing native MCP integration and vibe coding to extend insights into any application or AI agent
- Latest Google NEXT’ 26 Announcements: Highlighting our key launches from Google Next such as Dashboard Agents, Agentic workflows, integration with Gemini Enterprise, Agentic IDE development with LookML and much more
- Product Demos: Live showcases of how Looker is moving BI beyond the dashboard and directly into the flow of work

Looker’s Product Roadmap: Agentic BI for the Enterprise

The biggest hurdle to scaling enterprise AI isn't the models themselves - it’s the underlying business context. AI agents are only as reliable as the data they can understand. When enterprise data is fragmented and lacks clear lineage, AI initiatives inevitably suffer from hallucinations, cost overruns, and compliance bottlenecks.

- To move from pilot to production, organizations must build an intelligent data foundation where assets are easily discoverable, inherently trusted, and automatically governed.

-Join this executive session to explore how leading organizations are bridging the gap between raw data and AI readiness Google Cloud’s Knowledge Catalog. We will discuss strategic frameworks to:

-Empower your AI with rich business context and clear data provenance so users have greater confidence in AI-generated insights.

-Reduce the financial drain of redundant data pipelines and inefficient discovery by creating a unified, searchable view of your data estate.

-Turn your data into AI-ready intelligence by enriching every asset with the business context agents need to act.

The context advantage: Driving AI trust, governance, and ROI at scale

Enterprise data teams are increasingly stretched, demanding a shift to greater automation and efficiency. AI agents have evolved from single-task models into goal-oriented systems capable of complex reasoning and orchestrating multi-agent workflows to automate entire processes. In fact, Gartner predicts that by 2027, augmented analytics will become autonomous, managing 20% of business processes.


In this live webinar, we will explore Google Cloud’s approach to data agents, which is anchored in the core principles of power and choice. We will cover how organizations can achieve immediate value using Google Cloud's persona-specific, out-of-the-box data agents—including the Data Engineering Agent, Data Science Agent, and Conversational Analytics Agent–while also showing you how to design, build, and deploy your own custom agents tailored to your unique business logic. 

- Understand the Core Capabilities of Data Agents: Learn how data agents act as autonomous partners to understand deep context, gather and synthesize information 
- Leverage Pre-Built, Persona-Specific Agents: See how Google Cloud's out-of-the-box agents immediately accelerate workflows
- Build Custom Data Agents Using a 5-Step Blueprint: Discover the structured path to designing bespoke agents: Context, Setup, Deploy, Publish, and Analyze
- Capitalize on an Integrated Stack: Understand why building on Google Cloud's AI Hypercomputer architecture and the Gemini family of models reduces technical debt, minimizes latency, and guarantees enterprise-ready, grounded responses

Step Into the Agentic Era: Google Cloud Data Agents

Building transformative agents begins with selecting the right operational database, which serves as the essential engine that powers your AI application. Google Cloud databases are specifically engineered to help organizations not just keep pace, but actively pioneer the next wave of AI innovation. 

You will hear directly from the team shaping the future, learning how to revolutionize customer interactions and unlock the full, game-changing potential of agentic AI.

Join us live to dive deep into our vision and product roadmap, including all the latest announcements from NEXT' 26:

- How Google Cloud databases enable you to quickly build AI apps and agents and unify operational and analytical workloads for real-time insights
- Frictionless development and simplified database management with AI
- Google NEXT' 26 announcements in next-generation relational databases—Cloud SQL, AlloyDB, and Spanner
- Enhancements to non-relational databases, including Bigtable, Memorystore and Firestore
- Innovations in Oracle Database at Google Cloud, including expanded regional availability

Databases Roadmap: The Foundation for Your Agentic AI Future

While the first wave of AI accelerated individual task completion, the agentic era is redefining entire business operations through autonomous workflows that perceive, reason, and act on our behalf. Yet, a massive hurdle remains: according to a recent MIT study, 95% of generative AI pilots are at risk of failure. Modern data stacks cannot support the scale and velocity of autonomous agents. To bridge this gap and safely scale, organizations must evolve their infrastructure from static repositories into proactive systems of action.

Join Greg Khairallah, Director Global GTM Practice and Suda Srinivasan, Group Outbound Product Manager from Google Cloud to discover how the Agentic Data Cloud  powers accurate, secure, and autonomous operations at scale.

What You Will Learn:

- Conquer the Data Readiness Gap: Unpack the four major bottlenecks stalling AI initiatives
- Bring AI Directly to Your Data: Learn how to embed vector search natively to eliminate costly and complex data movement
- Eliminate Ambiguity and Hallucinations: Discover how to reduce generative AI hallucinations by up to two-thirds while securing organization-wide governance
- Realize Proven Business Value: Learn from the real-world success of pioneering organizations that have successfully moved beyond the prompt

Beyond the Prompt: Architecting the Agentic Data Cloud

We are entering a new Agentic AI era where AI doesn't just deliver intelligence, it acts. As you move from reactive dashboards to proactive, autonomous workflows, the underlying data platform must evolve to be more open, integrated, and agent-ready.

Join us for an exclusive live webinar as we unveil Google Cloud’s vision and product roadmap for the next generation of data and AI. We will explore our latest investments in our data analytics platform  for the agentic era.

In this session, we’ll dive deep into our roadmap across our core pillars, including all the latest announcements from NEXT:

- BigQuery: Learn about advancements in price-performance, advanced analytics, and built-in AI co-processing for structured and unstructured data
- Lakehouse: Discover how we are creating a lakehouse foundation on Apache Iceberg that is cross-cloud, open, and AI-ready
- Managed Service for Apache Spark: Fast and cost-effective Spark for Data Science
- Knowledge Catalog: Learn how we are evolving Dataplex into the knowledge catalog -  an always-on source of truth for agents to execute complex tasks with precision
- Google NEXT’ 26 Agentic Experiences Announcements: Explore specialized agents for your analytical workflows and discover tools designed to ground your custom AI agents in enterprise truth
- Streaming: See how we are transforming real-time streaming data into agentic intelligence for agents to act on live business events

Navigating the agentic era: Google Cloud’s Data Analytics innovation roadmap

AI is creating significant opportunities for innovation; however, data teams are facing a new set of challenges when it comes to powering these AI experiences. Agentic AI requires high quality data, but data engineering teams are struggling to keep up with the growing demand. Data agents can help streamline repetitive tasks and boost your data teams’ productivity. Data engineers benefit from automated data preparation and pipeline management, while ML engineers can deploy and monitor models more effectively. Join this session to learn how AI is changing the data engineering landscape by bringing automation, intelligence, and natural language capabilities into daily workflows.   Hear how Google Cloud is building data agents directly in its AI-powered data platform and how you can increase productivity and automate mundane data engineering tasks so your team can focus on innovation.

Delivering AI-Ready Data: Pipelines, Agents, and Automation at Scale

As organizations scale agentic AI, evolve enterprise architectures, and operationalize multimodal data, infrastructure choice dictates success. This fast-paced session highlights how Google Cloud addresses these 2026 priorities through an open, unified data ecosystem. Learn how a cross-cloud lakehouse architecture eliminates data silos, allowing teams to analyze disparate data engines securely without costly data movement. By decoupling storage and compute across multi-cloud environments, this framework delivers the extreme performance and cost-efficiencies required to fuel resource-intensive LLMs and semantic layers.
Whether you are navigating AI governance, optimizing total cost of ownership, or building AI literacy, this briefing provides the actionable strategies data leaders need to execute with confidence in the second half of 2026.

Next-Gen Data & AI: Driving Performance with an Open Lakehouse

Natural language analytics capabilities available in cutting-edge tools and applications are enabling all kinds of users to solve business questions and gain timely, contextually rich insights. TDWI research finds that organizations are increasingly interested in deploying tools and applications that offer natural language search, analytics, and other data interaction. Such capabilities are critical to enabling frustrated users to get beyond the limitations of legacy business intelligence tools and maximize the value of all data.

Join this webinar to learn the latest trends in natural language analytics and examples of how conversational AI and generative interfaces are changing how business users can interact with data. Panelists will additionally discuss how organizations can take steps to improve trust in natural language analytics and conversational AI and adhere to governance rules.

Conversational Analytics 2026: Talking Business to Your Data

FullStory is a leader in digital experience analytics, capturing user events and signals that help its customers understand the sentiment implied by every user action in real-time and use AI to deliver better personalization outcomes and improved user experiences. Join us to learn how Fullstory weaves large data streams into actionable insights and for take-home some inspiration on how to take your digital customer experiences to the next level.

How Fullstory makes every click count with Google Cloud

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How Fullstory makes every click count with Google Cloud

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