American Airlines, one of the largest airlines in the world, processes a tremendous amount of data every single minute. With a data estate of this scale, accountability for the data goes beyond the data team; the business organization has to be equally invested in championing the quality, reliability, and governance of data. In this session, Andrew Machen, Senior Manager, Data Engineering at American Airlines will share how his team maximizes resources to deliver reliable data at scale. He'll also outline his strategy for aligning business leadership with an investment in data reliability, and how leveraging Monte Carlo's data + AI observability platform enabled them to reduce time spent resolving data reliability issues from 10 weeks to 2 days, saving millions of dollars and driving valuable trust in the data.

Agentic AI is the next evolution in artificial intelligence, with the potential to revolutionize the industry. However, its potential is matched only by its risk: without high-quality, trustworthy data, agentic AI can be exponentially dangerous. Join Barr Moses, CEO and Co-Founder of Monte Carlo, to explore how to leverage Databricks' powerful platform to ensure your agentic AI initiatives are underpinned by reliable, high-quality data. Barr will share: How data quality impacts agentic AI performance at every stage of the pipeline Strategies for implementing data observability to detect and resolve data issues in real-time Best practices for building robust, error-resilient agentic AI models on Databricks. Real-world examples of businesses harnessing Databricks' scalability and Monte Carlo’s observability to drive trustworthy AI outcomes Learn how your organization can deliver more reliable agentic AI and turn the promise of autonomous intelligence into a strategic advantage.Audio for this session is delivered in the conference mobile app, you must bring your own headphones to listen.

Your model is trained. Your pilot is live. Your data looks AI-ready. But for most teams, the toughest part of building successful AI starts after deployment. In this talk, Shane Murray and Ethan Post share lessons from the development of Monte Carlo’s Troubleshooting Agent – an AI assistant that helps users diagnose and fix data issues in production. They’ll unpack what it really takes to build and operate trustworthy AI systems in the real world, including: The Illusion of Done – Why deployment is just the beginning, and what breaks in production; Lessons from the Field – A behind-the-scenes look at the architecture, integration, and user experience of Monte Carlo’s agent; Operationalizing Reliability – How to evaluate AI performance, build the right team, and close the loop between users and model. Whether you're scaling RAG pipelines or running LLMs in production, you’ll leave with a playbook for building data and AI systems you—and your users—can trust.

The data team at SurveyMonkey, the global leader in survey software, oversees heavy data transformation in dbt Cloud — both to power current business-critical projects, and also to migrate legacy workloads. Much of that transformation work is taking raw data — either from legacy databases or their cloud data warehouse (Snowflake) — and making it accessible and useful for downstream users. And to Samiksha Gour, Senior Data Engineering Manager at SurveyMonkey, each of these projects is not considered complete unless the proper checks, monitors, and alerts are in place.

Join Samiksha in this informative session as she walks through how her team uses dbt and their data observability platform Monte Carlo to ensure proper governance, gain efficiencies by eliminating duplicate testing and monitoring, and use data lineage to ensure upstream and downstream continuity for users and stakeholders.

Speaker: Samiksha Gour Senior Data Engineering Manager SurveyMonkey

Read the blog to learn about the latest dbt Cloud features announced at Coalesce, designed to help organizations embrace analytics best practices at scale https://www.getdbt.com/blog/coalesce-2024-product-announcements

TOCA Football, the largest operator of indoor soccer centers in North America, leverages accurate data to power analytics for over 30 training centers, providing everything from operational insights for executives to ball-by-ball analysis.

In 2020, the team adopted a cloud-native data stack with dbt to scale analytics enablement for the go-to-market org, including the company’s finance, strategy, operations, and marketing teams. By 2022, their lean team of four was struggling to gain visibility into the health and performance of their dbt models. So, what was the TOCA team to do? Two words: data observability.

In this talk, Sam Cvetkovski, Director, Data & Analytics discusses how TOCA built their larger data observability strategy to reduce model bloat, increase data accuracy, and boost stakeholder satisfaction with their team’s data products. She shares her biggest “aha!” moments, key challenges, and best practices for teams getting started on their dbt reliability journeys.

Speakers: Sam Cvetkovski, Director, Data & Analytics, TOCA Football; Barr Moses, Co-Founder & CEO, Monte Carlo

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

Comcast Effectv, the 2,000-employee advertising wing of Comcast, America’s largest telecommunications company, provides custom video ad solutions powered by aggregated viewership data. As a global technology and media company connecting millions of customers to personalized experiences and processing billions of transactions, Comcast Effectv was challenged with handling massive loads of data, monitoring hundreds of data pipelines, and managing timely coordination across data teams.

In this session, we will discuss Comcast Effectv’s journey to building a more scalable, reliable lakehouse and driving data observability at scale with Monte Carlo. This has enabled Effectv to have a single pane of glass view of their entire data environment to ensure consumer data trust across their entire AWS, Databricks, and Looker environment.

Talk by: Scott Lerner and Robinson Creighton

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When Prateek Chawla, founding engineer, joined Monte Carlo in 2019, he was responsible for spinning up our data platform from scratch. He was more of a backend/cloud engineer, but like with any startup had to wear many hats, so got the opportunity to play the role of data engineer too. In this talk, we’ll walk through how we spun up Monte Calro’s data stack with Snowflake, Looker, and dbt, touching on how and why we implemented dbt (and later, dbt Cloud), key use cases, and handy tricks for integrating dbt with other popular tools, like Airflow, and Spark. We’ll discuss what worked, what didn’t work, and other lessons learned along the way, as well as share how our data stack evolved over time to scale to meet the demands of our growing startup. We’ll also touch on a very critical component of the dbt value proposition, data quality testing, and discuss some of our favorite tests and what we’ve done to automate and integrate them with other elements of our stack.

Coalesce 2023 is coming! Register for free at https://coalesce.getdbt.com/.

Lineage is a critical component of any root cause, impact analysis, and overall analytics heath assessment workflow. But it hasn’t always been easy to create, particularly at the field level. In this session, Mei Tao, Helena Munoz, and Xuanzi Han (Monte Carlo) tackle this challenge head-on by leveraging some of the most popular tools in the modern data stack, including dbt, Airflow, Snowflake, and ANother Tool for Language Recognition (ANTLR). Learn how they designed the data model, query parser, and larger database design for field-level lineage—highlighting learnings, wrong turns, and best practices developed along the way.

Coalesce 2023 is coming! Register for free at https://coalesce.getdbt.com/.

"Why did our dashboard break?" "What happened to my data?" "Why is this column missing?" If you've been on the receiving end of these messages (and many others!) from downstream stakeholders, you're not alone. Data engineering teams spend 40 percent or more of their time tackling data downtime, or periods of time when data is missing, erroneous, or otherwise inaccurate, and as data systems become increasingly complex and distributed, this number will only increase. To address this problem, data observability is becoming an increasingly important part of the cloud data stack, helping engineers and analysts reduce time to detection and resolution for data incidents caused by faulty data, code, and operational environments. But what does data observability actually look like in practice? During this presentation, Barr Moses, CEO and co-founder of Monte Carlo, will present on how some of today's best data leaders implement observability across their data lake ecosystem and share best practices for data teams seeking to achieve end-to-end visibility into their data at scale. Topics addressed will include: building automated lineage for Apache Spark, applying data reliability workflows, and extending beyond testing and monitoring to solve for unknown unknowns in your data pipelines.

Connect with us: Website: https://databricks.com Facebook: https://www.facebook.com/databricksinc Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/data... Instagram: https://www.instagram.com/databricksinc/

The time and attention of skilled engineers are some of the most constrained, valuable resources at Red Digital, a marketing agency embedded within Red Ventures. Acknowledging that constraint, the team at Red Digital has taken a deliberate, product-first approach to modernize and democratize their data platform. With the help of modern tools like Databricks, Fivetran, dbt, Monte Carlo, and Airflow, Red Digital has increased its development velocity and the size of the available talent pool to continue to grow the business.

This talk will walk through some of the key challenges, decisions, and solutions that the Red Digital team has made to build a suite of parallel data stacks capable of supporting its growing business.

Connect with us: Website: https://databricks.com Facebook: https://www.facebook.com/databricksinc Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/data... Instagram: https://www.instagram.com/databricksinc/