Fawaz Ghali
Snowflake, Lead Developer Advocate - EMEA
London, United Kingdom
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Fawaz Ghali is a technologist specializing in AI, Data Engineering, Open Source, and Developer Relations. Passionate about community-driven innovation, he creates technical content, delivers talks, and engages with developer communities to drive the adoption of modern technologies in a rapidly evolving landscape.
With over two decades of experience and a PhD in Computer Science, Fawaz has published 45+ peer-reviewed papers and delivered 200+ talks worldwide. He is also the author of several books and reports and actively shares insights, empowering developers and data engineers through knowledge-sharing and collaboration.
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Build Data Lakes using Apache Iceberg with Snowflake and AWS Glue
Apache Iceberg is an open table format for huge analytical datasets that enables high performance analytics on open data formats with ACID compliance. Snowflake and AWS both support Iceberg format that enables customers to drastically improve data interoperability, speed of implmentation and peformance for integrated data lakes.
This session will take you through the steps of converting existing parquet data to Iceberg and using it to build open analytic environments using Snowflake and AWS Glue.
Building End-to-End AI and ML Workflows in Python: From Data to Production
Operationalizing machine learning isn’t just about building a model—it’s about creating a reliable, scalable pipeline from raw data to real-time inference. In this talk, we’ll walk through an end-to-end ML workflow in Python designed for developers, data scientists, and ML engineers who want to move fast and build production-ready systems without reinventing the wheel.
You’ll learn how to:
1- Prepare data and engineer features using consistent, reusable patterns to avoid duplication and drift across training and inference.
2- Train and tune models with popular open-source libraries like scikit-learn, XGBoost, and LightGBM, on CPU or GPU.
3- Package and deploy models for real-time or batch inference with minimal ops overhead.
4- Track experiments, monitor performance, and debug issues with built-in observability, lineage tracking, and model explainability.
We’ll show how all of this can be done within a unified workflow using Python, with the help of containerized runtimes and built-in versioning, orchestration, and deployment tools—so you can focus on solving problems, not managing infrastructure. This is a practical, hands-on session for developers who want to go from notebook to production without duct tape. By the end, you’ll walk away with a practical framework for building resilient ML systems that scale.
End-to-End Scalable and Secure AI Solutions with LLMs and Streamlit
Unlock the power of open-source AI with Snowflake Arctic LLM, Streamlit, and other leading models like Llama 4 and Mistral to build high-performance, customizable AI solutions—without the constraints of proprietary software. This session showcases how these tools enable secure, interactive AI development for everything from chatbots to workflow automation, delivering flexibility, scalability, and cost efficiency.
We’ll explore how financial institutions can leverage open-source AI to drive innovation while maintaining full transparency and control. If you're looking to enhance AI capabilities with cutting-edge, cost-effective solutions, this session is your guide to building smarter, more efficient AI-driven applications!
Data Lakes Unlocked – Apache Iceberg with Python and SQL
Big data demands big solutions—but traditional data lakes often fall short on performance, consistency, and scalability. Enter Apache Iceberg: an open-source table format designed to bring ACID transactions, schema evolution, and high-performance querying to modern cloud-native data architectures.
For Python developers and data engineers, Iceberg offers powerful capabilities for managing massive datasets efficiently. Whether you're ingesting, querying, or evolving data, Iceberg ensures transactional integrity, optimized performance, and seamless integration with various compute engines.
From time travel and partitioning strategies to best practices for scalable data lakes, this session will demystify Apache Iceberg and provide the insights you need to build future-ready, cloud-native data pipelines.
Scalable Python and SQL Data Engineering without Migraines
Data is growing. Complexity is rising. Performance can't be an afterthought. Snowflake’s cloud-native architecture delivers unmatched scalability, real-time analytics, and cost-efficient performance—but to truly unlock its power, you need the right tools and strategies. Enter Python: the go-to language for data engineers, analysts, and AI practitioners.
This session will bridge the gap between raw data and actionable insights, showing how Python developers can seamlessly integrate with Snowflake to build scalable data pipelines, optimize performance, and harness advanced features like Snowpark, time travel, and zero-copy cloning.
From fast querying and automation to AI-driven transformations, this talk will provide the strategies and best practices you need to scale your analytics and maximize the full potential of Snowflake and Python.
Build AI-Powered Data Pipelines with LLMs and Agents
Unlock the power of serverless LLMs to transform how teams interact with data—no complex SQL required. This session explores how to integrate models like Snowflake Arctic, Llama 2, and Mistral within modern data platforms to enable natural language-driven analytics. You'll learn how to optimize prompt engineering, generate SQL dynamically, and build interactive data apps with Streamlit.
Go beyond traditional query methods with AI-powered tools that simplify data exploration and decision-making. Whether you're a data engineer or business analyst, this session equips you with practical strategies to democratize data access and enhance analytics with intelligent automation.
Practical Applications and Trends in AI for Java Developers
AI is reshaping how developers build and optimize applications, and Java developers need to stay ahead. This session covers integrating AI and machine learning into Java applications using open-source tools, Snowpark, and Java UDFs for efficient data processing. You'll also learn how to run ML models natively, leverage Arctic LLM for generative AI, and build interactive AI-powered apps with Streamlit—all within a seamless, high-performance environment.
We’ll explore best practices, common pitfalls, and real-world strategies for deploying AI-powered Java applications in the cloud. If you're looking to enhance performance, streamline workflows, and future-proof your skills with open-source AI, this session is your gateway to building smarter, AI-driven Java applications!
Fawaz Ghali
Snowflake, Lead Developer Advocate - EMEA
London, United Kingdom
Actions
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