Be at the forefront of the gen AI revolution in 2025 by learning proven strategies to capitalize on opportunities with Google Cloud. Discover Google's go-to-market priorities, including Customer Engagement Suite, Search, AI agents, and Vertex AI Platform. Learn how to accelerate gen AI projects from proof-of-concept to production, with best practices for agentic AI & responsible AI development. Together, we can open up new revenue streams by building profitable AI offerings that engage customers, accelerate deals, and showcase real-world ROI.

Join this session to learn about Google Cloud's vision and investment priorities in EMEA, focus on key industry verticals for 2025, and emerging technology trends. Discover how fellow partners are capitalizing on opportunities to establish a footprint and build pipeline in multiple markets. We'll also discuss tackling generative AI implementation challenges, developing solutions, hiring and training, and the legal and ethical guidelines within the region.

Join us to discover the key solution plays driving Google Cloud's 2025 vision, and learn how to leverage cutting-edge AI to maximize your revenue. We'll outline key enablement sessions and training, empowering you to capitalize on the region's immense potential in 2025 and beyond.

Join our North America leadership team for a fireside chat on scaling your business with Google Cloud. They will reveal priorities for 2025, how to navigate the AI era, and how industry expertise will play a key role in unlocking and accelerating growth. Discover the latest trends shaping the region, and gain actionable insights to embrace unprecedented opportunities and exceed customer expectations in this transformative age.

Join APAC leaders as they discuss key priorities and go-to-market stratgies for the region. This transformative era provides unique opportunities for Google Cloud to differentiate ourselves in the market and collaborate with partners to drive AI-led business transformations for customers. Our strategic partners will share insights and success stories on why customers choose Google AI and clear steps to becoming a top partner.

Regional leader Yumi Ueno will share Japan's long-term strategic plan for 2025. She'll discuss the latest successes in the region and the ongoing efforts to help partners maximize Google Cloud's gen AI offerings to win customer deals. This session will be offered in Japanese, no English translation available.

In 2024, customers purchased billions through Google Cloud Marketplace, showcasing unprecedented spending power. Learn how to leverage the marketplace as your engine to drive engagement, scale growth, and maximize revenue. We'll discuss the 2025 roadmap highlighting partners who have already successfully monetized AI agents, data, and services. Don't miss this session to attain actionable go-to-market insights to boost your potential with Google Cloud Marketplace.

It feels like there’s a new advancement happening in AI every day – but how do these discoveries go from their nascent state in the research lab to full-scale enterprise deployment?  And how do we ensure that these new tools and capabilities remain secure? Our panelists will explore this cycle of innovation – the iterative process of curiosity-driven research, practical application, and real-world impact of AI and machine learning. We'll examine practical case studies and underscore the transformative power AI has in the realm of security.  

Topics will include: 

• How Google connects AI research with real-world solutions for some of the largest enterprises in the world
• Google Cloud’s AI roadmap
• Cybersecurity considerations for emerging AI tools

Artificial intelligence is no longer on the horizon – it’s the defining force shaping business today. During this fireside chat, Thomas Kurian, CEO of Google Cloud, will sit down with Google Cloud's VP of Marketing, Alison Wagonfeld for a candid conversation on navigating the AI revolution, unlocking new opportunities for innovation, and building a future-ready organization. They’ll explore Google Cloud’s strategic vision and delve into both the profound impact of AI across industries and actionable strategies for businesses to leverage this technology.

Topics will include: 

• AI as the competitive differentiator
• The role of Google Cloud in the AI era
• Navigating leadership in the age of AI

In this podcast episode, we talked with Eddy Zulkifly about From Supply Chain Management to Digital Warehousing and FinOps

About the Speaker: Eddy Zulkifly is a Staff Data Engineer at Kinaxis, building robust data platforms across Google Cloud, Azure, and AWS. With a decade of experience in data, he actively shares his expertise as a Mentor on ADPList and Teaching Assistant at Uplimit. Previously, he was a Senior Data Engineer at Home Depot, specializing in e-commerce and supply chain analytics. Currently pursuing a Master’s in Analytics at the Georgia Institute of Technology, Eddy is also passionate about open-source data projects and enjoys watching/exploring the analytics behind the Fantasy Premier League.

In this episode, we dive into the world of data engineering and FinOps with Eddy Zulkifly, Staff Data Engineer at Kinaxis. Eddy shares his unconventional career journey—from optimizing physical warehouses with Excel to building digital data platforms in the cloud.

🕒 TIMECODES 0:00 Eddy’s career journey: From supply chain to data engineering 8:18 Tools & learning: Excel, Docker, and transitioning to data engineering 21:57 Physical vs. digital warehousing: Analogies and key differences 31:40 Introduction to FinOps: Cloud cost optimization and vendor negotiations 40:18 Resources for FinOps: Certifications and the FinOps Foundation 45:12 Standardizing cloud cost reporting across AWS/GCP/Azure 50:04 Eddy’s master’s degree and closing thoughts

🔗 CONNECT WITH EDDY Twitter - https://x.com/eddarief Linkedin - https://www.linkedin.com/in/eddyzulkifly/ Github: https://github.com/eyzyly/eyzyly ADPList: https://adplist.org/mentors/eddy-zulkifly

🔗 CONNECT WITH DataTalksClub Join the community - https://datatalks.club/slack.html Subscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/r?cid=ZjhxaWRqbnEwamhzY3A4ODA5azFlZ2hzNjBAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ

Check other upcoming events - https://lu.ma/dtc-events LinkedIn - https://www.linkedin.com/company/datatalks-club/ Twitter - https://twitter.com/DataTalksClub Website - https://datatalks.club/

Kir Titievsky, Product Manager at Google Cloud with extensive experience in streaming and storage infrastructure, joined Yuliia and Dumky to talk about streaming. Drawing from his work with Apache Kafka, Cloud PubSub, Dataflow and Cloud Storage since 2015, Kir explains the fundamental differences between streaming and micro-batch processing. He challenges common misconceptions about streaming costs, explaining how streaming can be significantly less expensive than batch processing for many use cases. Kir shares insights on the "service bus architecture" revival, discussing how modern distributed messaging systems have solved historic bottlenecks while creating new opportunities for business and performance needs.Kir's medium - https://medium.com/@kir-gcpKir's Linkedin page - https://www.linkedin.com/in/kir-titievsky-%F0%9F%87%BA%F0%9F%87%A6-7775052/

AI is revolutionizing industries, but many organizations struggle to translate their AI investments into measurable business value. In this session, Eva Dong, Lead of AI Monetization at Google Cloud, will demystify AI monetization and provide a practical framework for unlocking value while optimizing costs.

Serhii Sokolenko, founder at Tower Dev and former product manager at tech giants like Google Cloud, Snowflake, and Databricks, joined Yuliia to discuss his journey building a next-generation compute platform. Tower Dev aims to simplify data processing for data engineers who work with Python. Serhii explains how Tower addresses three key market trends: the integration of data engineering with AI through Python, the movement away from complex distributed processing frameworks, and users' desire for flexibility across different data platforms. He explains how Tower makes Python data applications more accessible by eliminating the need to learn complex frameworks while automatically scaling infrastructure. Sergei also shares his perspective on the future of data engineering, noting in which ways AI will transform the profession.Tower Dev - https://tower.dev/Serhii's Linkedin - https://www.linkedin.com/in/ssokolenko/

Get ready to dive into the world of DevOps & Cloud tech! This session will help you navigate the complex world of Cloud and DevOps with confidence. This session is ideal for new grads, career changers, and anyone feeling overwhelmed by the buzz around DevOps. We'll break down its core concepts, demystify the jargon, and explore how DevOps is essential for success in the ever-changing technology landscape, particularly in the emerging era of generative AI. A basic understanding of software development concepts is helpful, but enthusiasm to learn is most important.

Vishakha is a Senior Cloud Architect at Google Cloud Platform with over 8 years of DevOps and Cloud experience. Prior to Google, she was a DevOps engineer at AWS and a Subject Matter Expert (SME) for the IaC offering CloudFormation in the NorthAm region. She has experience in diverse domains including Financial Services, Retail, and Online Media. She primarily focuses on Infrastructure Architecture, Design & Automation (IaC), Public Cloud (AWS, GCP), Kubernetes/CNCF tools, Infrastructure Security & Compliance, CI/CD & GitOps, and MLOPS.

Deepti Srivastava, Founder of Snow Leopard AI and former Spanner Product Lead at Google Cloud, joined Yuliia to chat what's wrong with current approaches to AI integration. Deepti introduces a paradigm shift away from ETL pipelines towards federated, real-time data access for AI applications. She explains how Snow Leopard's intelligent data retrieval platform enables enterprises to connect AI systems directly to operational data sources without compromising security or freshness. Through practical examples Deepti explains why conventional RAG approaches with vector stores are not good enough for business-critical AI applications, and how a systems thinking approach to AI infrastructure can unlock greater value while reducing unnecessary data movement.Deepti's linkedin - https://www.linkedin.com/in/thedeepti/Snowleopard.ai - http://snowleopard.ai/

Richard He, Founder at Fundamenta – Data Consultancy, former Engineering Director at Virgin Media O2  and creator of Practical GCP YouTube channel, joined Yuliia to discuss the critical concept of "cost of inaction" in data infrastructure modernization. Based on his two decades of experience, including several successful migrations, Richard emphasized the importance of proactive platform evolution over reactive large-scale migrations. He shared valuable insights on measuring ROI for platform teams, and bridging the gap between technical execution and business strategy. Richard's Linkedin - https://www.linkedin.com/in/shenghuahe/

Explore Snowflake’s core concepts and unique features that differentiates it from industry competitors, such as, Azure Synapse and Google BigQuery. This book provides recipes for architecting and developing modern data pipelines on the Snowflake data platform by employing progressive techniques, agile practices, and repeatable strategies. You’ll walk through step-by-step instructions on ready-to-use recipes covering a wide range of the latest development topics. Then build scalable development pipelines and solve specific scenarios common to all modern data platforms, such as, data masking, object tagging, data monetization, and security best practices. Throughout the book you’ll work with code samples for Amazon Web Services, Microsoft Azure, and Google Cloud Platform. There’s also a chapter devoted to solving machine learning problems with Snowflake. Authors Dillon Dayton and John Eipe are both Snowflake SnowPro Core certified, specializing in data and digital services, and understand the challenges of finding the right solution to complex problems. The recipes in this book are based on real world use cases and examples designed to help you provide quality, performant, and secured data to solve business initiatives. What You’ll Learn Handle structured and un- structured data in Snowflake. Apply best practices and different options for data transformation. Understand data application development. Implement data sharing, data governance and security. Who This book Is For Data engineers, scientists and analysts moving into Snowflake, looking to build data apps. This book expects basic knowledge in Cloud (AWS or Azure or GCP), SQL and Python

We’re improving DataFramed, and we need your help! We want to hear what you have to say about the show, and how we can make it more enjoyable for you—find out more here. Integrating generative AI with robust databases is becoming essential. As organizations face a plethora of database options and AI tools, making informed decisions is crucial for enhancing customer experiences and operational efficiency. How do you ensure your AI systems are powered by high-quality data? And how can these choices impact your organization's success? Gerrit Kazmaier is the VP and GM of Data Analytics at Google Cloud. Gerrit leads the development and design of Google Cloud’s data technology, which includes data warehousing and analytics. Gerrit’s mission is to build a unified data platform for all types of data processing as the foundation for the digital enterprise. Before joining Google, Gerrit served as President of the HANA & Analytics team at SAP in Germany and led the global Product, Solution & Engineering teams for Databases, Data Warehousing and Analytics. In 2015, Gerrit served as the Vice President of SAP Analytics Cloud in Vancouver, Canada. In this episode, Richie and Gerrit explore the transformative role of AI in data tools, the evolution of dashboards, the integration of AI with existing workflows, the challenges and opportunities in SQL code generation, the importance of a unified data platform, leveraging unstructured data, and much more. Links Mentioned in the Show: Google CloudConnect with GerritThinking Fast and Slow by Daniel KahnemanCourse: Introduction to GCPRelated Episode: Not Only Vector Databases: Putting Databases at the Heart of AI, with Andi Gutmans, VP and GM of Databases at GoogleRewatch sessions from RADAR: Forward Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

"Apache Airflow Best Practices" is your go-to guide for mastering data workflow orchestration using Apache Airflow. This book introduces you to core concepts and features of Airflow and helps you efficiently design, deploy, and manage workflows. With detailed examples and hands-on tutorials, you'll learn how to tackle real-world challenges in data engineering. What this Book will help me do Understand and utilize the features and updates introduced in Apache Airflow 2.x. Design and implement robust, scalable, and efficient data pipelines and workflows. Learn best practices for deploying Apache Airflow in cloud environments such as AWS and GCP. Extend Airflow's functionality with custom plugins and advanced configuration. Monitor, maintain, and scale your Airflow deployment effectively for high availability. Author(s) Dylan Intorf, Dylan Storey, and Kendrick van Doorn are seasoned professionals in data engineering, data strategy, and software development. Between them, they bring decades of experience working in diverse industries like finance, tech, and life sciences. They bring their expertise into this practical guide to help practitioners understand and master Apache Airflow. Who is it for? This book is tailored for data professionals such as data engineers, scientists, and system administrators, offering valuable insights for new learners and experienced users. If you're starting with workflow orchestration, seeking to optimize your current Airflow implementation, or scaling efforts, this book aligns with your goals. Readers should have a basic knowledge of Python programming and data engineering principles.

Generative AI and data are more interconnected than ever. If you want quality in your AI product, you need to be connected to a database with high quality data. But with so many database options and new AI tools emerging, how do you ensure you’re making the right choices for your organization? Whether it’s enhancing customer experiences or improving operational efficiency, understanding the role of your databases in powering AI is crucial.  Andi Gutmans is the General Manager and Vice President for Databases at Google. Andi’s focus is on building, managing, and scaling the most innovative database services to deliver the industry’s leading data platform for businesses. Prior to joining Google, Andi was VP Analytics at AWS running services such as Amazon Redshift. Prior to his tenure at AWS, Andi served as CEO and co-founder of Zend Technologies, the commercial backer of open-source PHP. Andi has over 20 years of experience as an open source contributor and leader. He co-authored open source PHP. He is an emeritus member of the Apache Software Foundation and served on the Eclipse Foundation’s board of directors. He holds a bachelor’s degree in computer science from the Technion, Israel Institute of Technology. In the episode, Richie and Andi explore databases and their relationship with AI and GenAI, key features needed in databases for AI, GCP database services, AlloyDB, federated queries in Google Cloud, vector databases, graph databases, practical use cases of AI in databases and much more.  Links Mentioned in the Show: GCPConnect with AndiAlloyDB for PostgreSQLCourse: Responsible AI Data ManagementRelated Episode: The Power of Vector Databases and Semantic Search with Elan Dekel, VP of Product at PineconeSign up to RADAR: Forward Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business