Connected vehicle telemetry has data that can be used to gain insights into vehicle performance, driver behavior, and fleet operations using AI technology. We will discuss how Ford uses Bigtable to collect, store, and analyze connected vehicle telemetry data in conjunction with BigQuery, Pub/Sub and Dataflow, a recipe applicable to many time series use cases. Get some of the insights we have gained from this data, how we have used these insights to improve our fleet operations and also some new Bigtable features we‘re most excited about.

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BigQuery offers a comprehensive solution for migrating your data warehouse, including no-cost tools that help you accelerate migrations, reduce risk, and speed up the time to value. Join this session to discover the new migration tools and best practices.

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With BigQuery and AlloyDB you can run high-performance transactional and analytical workloads and tap into world-class AI models on Vertex AI. Learn how to create the next generation of data-driven experiences with intelligence built in.

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​​Discover how customers leverage unified capabilities with BigQuery to enable rapid and seamless connection of data with AI, empowering them to extract greater value from their information.

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In this game you will create and manage permissions for Google Cloud resources, run structured queries on BigQuery and Cloud SQL, create several VPC networks and VM instances and test connectivity across networks, and monitor a Google Compute Engine VM instance with Cloud Monitoring.

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Supercharge your processes with Gemini for Google Workspace. Build no-code solutions easily using AppSheet and create custom solutions integrated with BigQuery and Vertex AI. Learn how generative AI is evolving to help users tackle common workflow scenarios with ease.

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Explore how BigQuery customers can use Gemini to extract value from their data.

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Accelerate marketing analysis, tailored personalization, and impactful content creation with Vertex AI Search, Vertex AI Agents, Dialogflow CX, Gemini, and BigQuery ML

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In this mini course you will learn how to optimize performance and cost in BigQuery. You will learn how to optimize your queries and lower cost by optimizing your storage using partitioning and clustering. You will explore these techniques in a hands-on lab environment by exploring data inspired by a real customer use case.

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Master the art and science of analytics engineering with 'Fundamentals of Analytics Engineering.' This book takes you on a comprehensive journey from understanding foundational concepts to implementing end-to-end analytics solutions. You'll gain not just theoretical knowledge but practical expertise in building scalable, robust data platforms to meet organizational needs. What this Book will help me do Design and implement effective data pipelines leveraging modern tools like Airbyte, BigQuery, and dbt. Adopt best practices for data modeling and schema design to enhance system performance and develop clearer data structures. Learn advanced techniques for ensuring data quality, governance, and observability in your data solutions. Master collaborative coding practices, including version control with Git and strategies for maintaining well-documented codebases. Automate and manage data workflows efficiently using CI/CD pipelines and workflow orchestrators. Author(s) Dumky De Wilde, alongside six co-authors-experienced professionals from various facets of the analytics field-delivers a cohesive exploration of analytics engineering. The authors blend their expertise in software development, data analysis, and engineering to offer actionable advice and insights. Their approachable ethos makes complex concepts understandable, promoting educational learning. Who is it for? This book is a perfect fit for data analysts and engineers curious about transitioning into analytics engineering. Aspiring professionals as well as seasoned analytics engineers looking to deepen their understanding of modern practices will find guidance. It's tailored for individuals aiming to boost their career trajectory in data engineering roles, addressing fundamental to advanced topics.

With GA4 putting web and behavioural data in a data warehouse into the hands of more analysts than ever before, you may be wondering how to get the best from your data in BigQuery (or any data warehouse), keep costs manageable, and how to give your users the best performance possible. This talk will cover different approaches to data modelling, the trade-offs associated with each approach, and how the dashboard/BI tool you’re using (whether it be Looker or Looker Studio, Tableau, Power BI etc) impacts your data modelling.

The usage of GA4 and BigQuery real-time reports features can be quite challenging, especially in high-traffic volume websites and other demanding environments. For example, assuming that you find a viable solution for a specific project, it is crucial to determine in advance the projected BigQuery expenses, in order to avoid unpleasant surprises. Architecture, data management, limits and quotas on API Requests are also part of this complex equation. Matteo and Roberto will share some real-world solutions for GA4 and BigQuery real-time needs tested with several clients in different industries.

Join Damian Filonowicz as he shares 'Lessons Learned from the GCP Data Migration.' 🌐 Discover how PAYBACK tackled challenges in shifting data to the cloud, navigated privacy regulations, and uncovered insights about Google Cloud services like Cloud Dataflow, Cloud DLP, BigQuery, and more. Gain valuable suggestions for future endeavors in this enlightening presentation! 🚀🔍 #DataMigration #GCP #lessonslearned

✨ H I G H L I G H T S ✨

🙌 A huge shoutout to all the incredible participants who made Big Data Conference Europe 2023 in Vilnius, Lithuania, from November 21-24, an absolute triumph! 🎉 Your attendance and active participation were instrumental in making this event so special. 🌍

Don't forget to check out the session recordings from the conference to relive the valuable insights and knowledge shared! 📽️

Once again, THANK YOU for playing a pivotal role in the success of Big Data Conference Europe 2023. 🚀 See you next year for another unforgettable conference! 📅 #BigDataConference #SeeYouNextYear

Karim Wadie: Don’t Worry About BigQuery PII: How Google Cloud Helped a Large Telco Automate Data Governance at Scale

Discover how Google Cloud helped a large Telco automate data governance at scale in BigQuery with Karim Wadie. 📊🔒 Learn about the technical solution, GCP concepts, and see a live demo to fast-track your cloud journey. 🌐💡 #DataGovernance #BigQuery #googlecloud

✨ H I G H L I G H T S ✨

🙌 A huge shoutout to all the incredible participants who made Big Data Conference Europe 2023 in Vilnius, Lithuania, from November 21-24, an absolute triumph! 🎉 Your attendance and active participation were instrumental in making this event so special. 🌍

Don't forget to check out the session recordings from the conference to relive the valuable insights and knowledge shared! 📽️

Once again, THANK YOU for playing a pivotal role in the success of Big Data Conference Europe 2023. 🚀 See you next year for another unforgettable conference! 📅 #BigDataConference #SeeYouNextYear

Join Angelika Postaremczak in an enlightening session on 'Best Practices for Storing Data in BigQuery' and discover the keys to optimizing data storage for lightning-fast queries without breaking the bank! 🚀💾 Explore table design strategies, data partitioning, clustering, and resource management through Infrastructure as Code for maximizing the potential of cloud data storage. ☁️📊 #BigQuery #datastorage

✨ H I G H L I G H T S ✨

🙌 A huge shoutout to all the incredible participants who made Big Data Conference Europe 2023 in Vilnius, Lithuania, from November 21-24, an absolute triumph! 🎉 Your attendance and active participation were instrumental in making this event so special. 🌍

Don't forget to check out the session recordings from the conference to relive the valuable insights and knowledge shared! 📽️

Once again, THANK YOU for playing a pivotal role in the success of Big Data Conference Europe 2023. 🚀 See you next year for another unforgettable conference! 📅 #BigDataConference #SeeYouNextYear

In "Data Exploration and Preparation with BigQuery," Michael Kahn provides a hands-on guide to understanding and utilizing Google's powerful data warehouse solution, BigQuery. This comprehensive book equips you with the skills needed to clean, transform, and analyze large datasets for actionable business insights. What this Book will help me do Master the process of exploring and assessing the quality of datasets. Learn SQL for performing efficient and advanced data transformations in BigQuery. Optimize the performance of BigQuery queries for speed and cost-effectiveness. Discover best practices for setting up and managing BigQuery resources. Apply real-world case studies to analyze data and derive meaningful insights. Author(s) Michael Kahn is an experienced data engineer and author specializing in big data solutions and technologies. With years of hands-on experience working with Google Cloud Platform and BigQuery, he has assisted organizations in optimizing their data pipelines for effective decision-making. His accessible writing style ensures complex topics become approachable, enabling readers of various skill levels to succeed. Who is it for? This book is tailored for data analysts, data engineers, and data scientists who want to learn how to effectively use BigQuery for data exploration and preparation. Whether you're new to BigQuery or looking to deepen your expertise in working with large datasets, this book provides clear guidance and practical examples to achieve your goals.

This book is your practical and comprehensive guide to learning Google Cloud Platform (GCP) for data science, using only the free tier services offered by the platform. Data science and machine learning are increasingly becoming critical to businesses of all sizes, and the cloud provides a powerful platform for these applications. GCP offers a range of data science services that can be used to store, process, and analyze large datasets, and train and deploy machine learning models. The book is organized into seven chapters covering various topics such as GCP account setup, Google Colaboratory, Big Data and Machine Learning, Data Visualization and Business Intelligence, Data Processing and Transformation, Data Analytics and Storage, and Advanced Topics. Each chapter provides step-by-step instructions and examples illustrating how to use GCP services for data science and big data projects. Readers will learn how to set up a Google Colaboratory account and run Jupyternotebooks, access GCP services and data from Colaboratory, use BigQuery for data analytics, and deploy machine learning models using Vertex AI. The book also covers how to visualize data using Looker Data Studio, run data processing pipelines using Google Cloud Dataflow and Dataprep, and store data using Google Cloud Storage and SQL. What You Will Learn Set up a GCP account and project Explore BigQuery and its use cases, including machine learning Understand Google Cloud AI Platform and its capabilities Use Vertex AI for training and deploying machine learning models Explore Google Cloud Dataproc and its use cases for big data processing Create and share data visualizations and reports with Looker Data Studio Explore Google Cloud Dataflow and its use cases for batch and stream data processing Run data processing pipelines on Cloud Dataflow Explore Google Cloud Storageand its use cases for data storage Get an introduction to Google Cloud SQL and its use cases for relational databases Get an introduction to Google Cloud Pub/Sub and its use cases for real-time data streaming Who This Book Is For Data scientists, machine learning engineers, and analysts who want to learn how to use Google Cloud Platform (GCP) for their data science and big data projects

In this session, Vida Health’s senior director of data, mobile, and web engineering shares a story that can help other data and business leaders capitalize on the opportunities being created by current technology innovations, market realities, and real-world problems. This includes a playbook on how Vida Health uses modern data technologies like dbt Cloud, Fivetran, Looker, BigQuery, BigQueryML/dbtML, Vertex AI, LLMs, and more to put data in the driver’s seat to solve meaningful problems in complex industries like healthcare.

Speaker: Trenton Huey, Senior Director, Data and Frontend Engineering, Vida Health

Register for Coalesce at https://coalesce.getdbt.com

Learn how dv01 uses dbt Cloud and BigQuery to create a scalable and modern data pipeline for offerings in loan warehousing analytics. These products serve an esoteric niche of finance and are run by a team of financial analysts with deep industry expertise.

With the challenge of tracking the performance of millions of loans from various sources and file structures, the team initially relied on Excel-based workflows. However, as the client base grew, they needed a reliable solution: a scalable data pipeline with dbt Cloud and BigQuery that allows the team to scale into a growing market and provide innovative new products and services.

Explore the transformative power of dbt Cloud in modernizing unscalable data processes, fostering skill development, and driving success in the specialized world of loan warehousing finance.

Speaker: David Maguire, Data Engineer, dv01

Register for Coalesce at https://coalesce.getdbt.com

Lauren Benezra has been volunteering with a local cat rescue since 2018. She recently took on the challenge of rebuilding their data stack from scratch, replacing a Jenga tower of incomprehensible Google Sheets with a more reliable system backed by the Modern Data Stack. By using Airtable, Airbyte, BigQuery, dbt Cloud and Census, her role as Foster Coordinator has transformed: instead of digging for buried information while wrangling cats, she now serves up accurate data with ease while... well... wrangling cats.

Viewers will learn that it's possible to run an extremely scalable and reliable stack on a shoestring budget, and will come away with actionable steps to put Lauren's hard-won lessons into practice in their own volunteering projects or as the first data hire in a tiny startup.

Speakers: Lauren Benezra, Senior Analytics Engineer, dbt Labs

Register for Coalesce at https://coalesce.getdbt.com/