Telenet, an affiliate of Liberty Global, is a market leading telecom known for its continuous customer-centric innovation using AI and data analytics. As an early adopter of Snowflake, they use data to drive cutting edge innovation such as hyper personalized customer services and privacy compliant data sharing with networking and broadcast partners. To further spur innovation, Telenet wants to make it easier for analysts and AI engineers to find and access data. In this session you will learn how Telenet is using Snowflake, AWS and Raito to give data analysts and AI engineers access to data in a fast and secure way.

Have you ever wondered how to build trusted data products without writing a single line of code? Do you know how to do that for a Snowflake Native App? Learn how to cut your development loop short with DataOps.live. Iterate on your implementation without ever setting up a local development environment, test it before sharing it with your team members, and finally, use CI/CD to operationalize the result as a data pipeline. Automated tests establish trust with your business stakeholders and catch data and schema drift over time in your scheduled data pipeline.

Learn how to cut your dev time with DataOps.live. Iterate without a local dev environment, test before sharing & use CI/CD to operationalize

Elysia provides battery manufacturers and owners with actionable battery insights using Snowflake's AI data platform.

Electric vehicles produce billions of diagnostic data points, combining these data with advanced cloud-based battery models we can use software to unlock longer battery lifetimes, increased range, faster charging and predict safety issues. 

Elysia Battery Intelligence from Fortescue Zero is built on over 15 years of experience in electric motorsports. Elysia provides battery manufacturers and owners with actionable battery insights using Snowflake’s data platform as the backbone of their AI-driven battery analytics. In this session, Pezhman Zarabadi and Matt Clarke will demonstrate how Elysia are leveraging Snowflake to scale Elysia, enabling seamless conversion of innovative battery research to new features. They will also discuss Elysia’s recently announced collaboration with Jaguar Land Rover to monitor their entire portfolio of electric vehicles.

See how Snowflake’s sales engineering team and field CTOs successfully build and deploy over 100 new data products a week using DataOps.live

Snowflake had a big challenge: How do you enable a team of 1,000 sales engineers and field CTOs to successfully deploy over 100 new data products per week and demonstrate every feature and capability in the Snowflake AI Data Cloud tailored to different customer needs?

In this session, Andrew Helgeson, Manager of Technology Platform Alliances at Snowflake, and Guy Adams, CTO at DataOps.live, will explain how Snowflake builds and deploys hundreds of data products using DataOps.live. Join us for a deep dive into Snowflake's innovative approach to automating complex data product deployment — and to learn how Snowflake Solutions Central revolutionizes solution discovery and deployment to drive customer success.

Discover Sainsbury's journey of data democratisation through Snowflake's AI Data Cloud and how they're transforming analytics.

Discover Sainsbury's journey of data democratisation through Snowflake's Data AI Cloud and how, by embracing the modern data platform, the team is transforming analytics, ML and access to data. From generating insightful reports to crafting machine learning models, we're personalising experiences for both our customers and colleagues. Every day, thousands of decisions are enhanced by this data-driven approach, showcasing Sainsbury's commitment to harnessing the power of data for retail excellence. Join us to learn how Sainsbury's leverages Snowflake to inform decisions and shape the future of retail.

Join us as we dive deep into the world of spatial analytics and discover how to leverage location data to its fullest potential. Whether you're working with massive datasets in a Spark environment, harnessing the power of cloud data warehouses like Snowflake, Redshift, or BigQuery, or analysing live data feeds in real time, this session will equip you with the tools and knowledge to:

• Uncover hidden patterns and trends in your data that traditional analytics might miss.

• Gain a deeper understanding of your customers and their behaviours.

• Optimise operations and improve efficiency.

• Make data-driven decisions with confidence.

Don't miss this opportunity to learn how spatial analytics can revolutionise your data platform and drive your business forward.

As organisations shift from generative AI proof of concepts to building production ready applications, the requirements for efficiency, monitoring, safety and governance become critical to both trust and success.

You will learn:

Key design patterns and methodology for evaluating, experimenting and monitoring enterprise gen AI apps to address common failure modes

The role of iteration and improvement as part of ongoing delivery

Practical considerations for implementation using examples from Snowflake’s Cortex Analyst, Cortex Search and TruLens, an open source project.

Welcome to the Data Engineering Central Podcast —— a no-holds-barred discussion on the Data Landscape. Welcome to Episode 01 In today’s episode we will talk about the following topics from the Data Engineering perspective … * Snowflake vs Databricks. * Is Apache Spark being replaced?? * Notebooks in Production. Bad.

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit dataengineeringcentral.substack.com/subscribe

Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that should flow as smoothly as your morning coffee (but don't), where industry insights meet laid-back banter. Whether you're a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let's get into the heart of data, unplugged style! In this episode, we dive deep into the fascinating world of AI predictions in sports, with a special focus on the Euro 2024 final between Spain and England. Join us as we explore: AI Predictions Revisited: Reflecting on the previous episode (listen here) about AI predictions and their accuracy, particularly Snowflake's prediction for Euro 2024.Challenges of Predictions: The complexities of predicting outcomes in football due to the group stage setup and other factors.National vs. Club Football: Differences in managing national teams versus club teams and the pressures of player selection.Valuing Players: Methods to measure the value of players, from ELO ratings to valuing actions by estimating probabilities.Psychological Pressure: How high-pressure situations impact player performance, referencing the study "Choke or Shine" with examples like Cristiano Ronaldo's goal and the importance of players who perform under pressure.Technology in Sports: The increasing role of technology in soccer, including goal line tech, offside simulations, and connected ball technology.Subjectivity of Offside Rules: The challenges of interpreting offside rules and the potential benefits and pitfalls of semi-automated offside technology. More info here.Technological Impact on Predictions: The influence of technological advancements on predicting outcomes in sports like NBA and soccer, and the potential future of AI in sports officiating.

Balyasny Asset Management (BAM) is a diversified global investment firm founded in 2001 with over $20 billion in assets under management. As dbt took hold at BAM, we had multiple teams building dbt projects against Snowflake, Redshift, and SQL Server. The common question was: How can we quickly and easily productionise our projects? Airflow is the orchestrator of choice at BAM, but our dbt users ranged from Airflow power users to people who’d never heard of Airflow before. We built a single solution on top of Cosmos that allowed us to: Decouple the dbt project from the Airflow repository Have each dbt node run as a separate Airflow task Allow users to run dbt with little to no Airflow knowledge Enable users to have fine-grained control over how dbt is run and to combine it with other Airflow tasks Provide observability, monitoring, and alerting.

Cost management is a continuous challenge for our data teams at Astronomer. Understanding the expenses associated with running our workflows is not always straightforward, and identifying which process ran a query causing unexpected usage on a given day can be time-consuming. In this talk, we will showcase an Airflow Plugin and specific DAGs developed and used internally at Astronomer to track and optimize the costs of running DAGs. Our internal tool monitors Snowflake query costs, provides insights, and sends alerts for abnormal usage. With it, Astronomer identified and refactored its most costly DAGs, resulting in an almost 25% reduction in Snowflake spending. We will demonstrate how to track Snowflake-related DAG costs and discuss how the tool can be adapted to any database supporting query tagging like BigQuery, Oracle, and more. This talk will cover the implementation details and show how Airflow users can effectively adopt this tool to monitor and manage their DAG costs.

Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that should flow as smoothly as your morning coffee (but don’t), where industry insights meet laid-back banter. Whether you’re a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let’s get into the heart of data, unplugged style! In this episode, join us along with guests Vitale and David as we explore: Euro 2024 Predictions with AI: Using Snowflake's machine learning models for data-driven predictions and sharing our own predictions. Can animals predict wins better than ML models?Tech in football: From VAR to connected ball technology, is it all a good idea?Nvidia overtaking Apple and Microsoft as the biggest tech corporation? Discussing Nvidia's leap to surpass Apple and Microsoft, and the implications for the GPU market and AI development.Unity Catalog vs. Polaris: Comparing Unity+Delta with Polaris+Iceberg and their roles in data cataloging and management. Explore the details on GitHub Unity Catalog, YouTube, and insights on LinkedIn. Databricks Data and AI Summit recap: Discussing the biggest announcements from the summit, including Mosaic AI integration, serverless options, and the open-source unity catalog.Exploring BM25: Discussing the BM25 algorithm and its advancements over traditional TF-IDF for document classification.

Speaker: Jessica Larson (Data Engineer & Author of “Snowflake Access Control”)

This tech talk is a part of the Data Engineering Open Forum at Netflix 2024. The requirements for creating a new data warehouse in the post-GDPR world are significantly different from those of the pre-GDPR world, such as the need to prioritize sensitive data protection and regulatory compliance over performance and cost. In this talk, Jessica Larson shares her takeaways from building a new data platform post-GDPR.

If you are interested in attending a future Data Engineering Open Forum, we highly recommend you join our Google Group (https://groups.google.com/g/data-engineering-open-forum) to stay tuned to event announcements.

The security and privacy of data during the training, fine-tuning, and inferencing phases of generative AI are paramount. This lightning talk introduces a reference architecture designed to use the security of AWS PrivateLink with generative AI applications. Explore the importance of protecting proprietary data in applications that leverage both AWS native LLMs and ISV-supplied external data stores. Learn about the secure movement and usage of data, particularly for RAG processes, across various data sources like Amazon S3, vector databases, and Snowflake. Learn how this reference architecture not only meets today’s security demands but also sets the stage for the future of secure generative AI development.

Learn more about AWS re:Inforce at https://go.aws/reinforce.

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The Ultimate Guide to Snowpark serves as a comprehensive resource to help you master the Snowflake Snowpark framework using Python. You'll learn how to manage data engineering, data science, and data applications in Snowpark, coupled with practical implementations and examples. By following this guide, you'll gain the skills needed to efficiently process and analyze data in the Snowflake Data Cloud. What this Book will help me do Master Snowpark with Python for data engineering, data science, and data application workloads. Develop and deploy robust data pipelines using Snowpark in Python. Design, implement, and produce machine learning models using Snowpark. Learn to monetize and operationalize Snowflake-native applications. Effectively adopt Snowpark in production for scalable, efficient data solutions. Author(s) Shankar Narayanan SGS and Vivekanandan SS are experienced professionals in data engineering and Snowflake technologies. Shankar has extensive experience in utilizing Snowflake Snowpark to manage and enhance data solutions. Vivekanandan brings expertise in the intersection of Python programming and cloud-based data processing. Together, their combined knowledge and approachable writing style make this book an invaluable resource to readers. Who is it for? This book is designed for data engineers, data scientists, developers, and seasoned data practitioners. Ideal candidates are those looking to expand their skills in implementing Snowpark solutions using Python. A prior understanding of SQL, Python programming, and familiarity with Snowflake is beneficial for readers to fully leverage the techniques presented.