Managing data at scale within a monolithic architecture poses challenges for organizations aiming to modernize. When databases with differing scaling needs — like ledger-style systems, high-transaction accounts, and reporting stores — share infrastructure, performance bottlenecks and governance issues arise. This webcast explores how a financial data services firm tackled these issues by adopting a cloud-based data lake. Topics include: Centralizing siloed data sources in a unified data lake Migrating and replicating data with ETL workflows Enabling near-real-time streaming for advanced analytics Watch the full webcast for a detailed technical walkthrough.

As organizations deploy agentic AI systems, connecting agents to enterprise data securely and at scale is a key challenge. From managing memory to enforcing access controls, the infrastructure demands of agentic AI are evolving rapidly. This webcast offers a guide to tackling these challenges, covering the data layer powering agentic AI workflows. Topics include: ReAct loop, context management, and multi-tier memory architecture Model Context Protocol (MCP) for tool discovery across data sources Data governance, identity propagation, and access control Watch to learn how to build secure, governed data foundations for agentic AI.

Building a data strategy that delivers business value remains a key challenge for modern organizations. Despite widespread investment in data initiatives, few companies see meaningful returns — and the rise of agentic AI is raising the stakes. This webcast provides a practical framework for rethinking data strategy through Formula One racing, highlighting parallels between motorsport and enterprise data operations. Topics include: Why focusing on data volume over value leads to waste Structuring teams and governance to enable innovation Lessons from CarGurus on driving change through sociotechnical approaches Watch to explore strategies for building a data foundation ready for agentic AI.

Large-scale database migrations challenge enterprise teams managing diverse ecosystems with varying downtime tolerances, data sizes, and technology stacks. Without automation, migrations risk data corruption, outages, and burdens on application teams—issues that grow with hundreds of databases. This webcast explores a migration framework addressing these complexities. Key insights include: Pre-flight validation to detect data issues early Near-zero read downtime during cutover via proxy orchestration Dual-mode validation using batch and real-time comparisons Watch the webcast for the full methodology and lessons learned.

Data teams face a productivity crisis as engineers spend most of their time on repetitive tasks, while knowledge gaps and growing data volumes strain capacity. Existing AI coding assistants often lack awareness of an organization’s environment, producing code that requires manual rework. This webcast highlights purpose-built AI agents tackling these challenges: An automated Spark upgrade agent for migrations, from planning to data quality validation A data-aware agent creating executable queries and ML pipelines without manual edits A multi-agent ecosystem enabling seamless IDE integration via MCP-based interoperability Watch the full webcast to learn more.

Extracting insights from structured data in enterprise data lakes has traditionally required analysts to search catalogs, write SQL queries, and translate results — a slow, resource-intensive process limiting data accessibility. This webcast explores how natural language querying automates this workflow using generative AI and vector search. Topics include: How vector databases enable semantic search across catalog metadata How large language models generate and execute SQL from plain-language questions How an AI pipeline delivers human-readable answers without SQL expertise Watch the full webcast to explore this approach in depth.

As cyber threats grow more sophisticated and cloud environments become increasingly complex, traditional SIEM platforms are struggling to keep pace. Legacy architectures often create operational bottlenecks, increase costs, and limit organizations’ ability to leverage AI-driven security capabilities. The next generation of security operations demands a cloud-native, intelligence-powered approach. Google Security Operations (SecOps) empowers organizations to modernize their SOC by combining hyperscale cloud infrastructure, integrated threat intelligence, and advanced AI capabilities into a unified security operations platform. With native access to Google Threat Intelligence, automated threat detection, natural language-driven investigations, and AI-assisted alert triage, Google SecOps helps security teams reduce analyst fatigue, accelerate investigations, and improve response outcomes. This guide explores how organizations can successfully migrate from legacy SIEM solutions to an AI-ready security operations platform. Learn key strategies for modernizing detection and response workflows, implementing telemetry pipelines, reducing operational complexity, and leveraging AI as a force multiplier across security operations. Discover how Google SecOps enables faster threat detection, richer context, improved scalability, and measurable improvements in security effectiveness—helping organizations move beyond simply managing alerts to proactively defending against emerging threats in the AI era.

To gain insight into practical use of AI in SecOps, Google commissioned Omdia to survey 300 security practitioners and SOC managers on their use of AI-enabled technologies within security. Learn how teams are actively adopting agentic AI technologies, early use cases and learnings, how Agentic AI is reshaping security roles and day-to-day activities, and the value and measurable outcomes from early adopters who report a 96% improvement in SecOps capabilities.

Advertisers today want two things: better performance and faster actionable insights. Customers, on the other hand, desire hyper-personalized advertising that meets them where they are – from their phones, tablets, and CTV to the newest apps and assistants like ChatGPT, Gemini, and Microsoft Copilot. But for many advertisers, keeping pace with evolving customer interests across channels has felt out of reach, until now. AI has created a pathway to real-time insights and recommendations, powering the tools advertisers need to predict what customers want and where they’ll show up next.

Customer loyalty has become increasingly fragile. A single disappointment can now be enough to trigger churn. Yet consumers expect fast, seamless and personalised experiences across an ever-growing number of touchpoints, while AI is reshaping how they discover and interact with brands. In many cases it has already removed the need for direct engagement altogether.