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​If it's the 2nd Monday of the month in NYC, it's CryptoMondays Decentralized AI! This month we're hosted by PubKey, where innovation meets revolution. The evening will include a fireside chat moderated by Calanthia Mei, co-founder of MASA )a leading real-time data network for AI agents) and creator of AI Agent TAO Cat, and features Dele Atanda, co-founder of CryptoMondays Decentralized AI and CEO of the metaProof Group, as they dive into all things DeAI/AI Agents. Don’t miss this chance to network and gain insights from top experts in Decentralized AI at the coolest place in NYC.

CryptoMondays is a Meetup that started in NYC on Jan. 8, 2018. Today there are more than 50 active chapters around the world!​

Starting on Jan 13, 2025, the second Monday of every month in NYC is CryptoMondays AI Agents, featuring builders and thought leaders at the intersection of AI Agents & Web3. We've got two great guests tonight for a 30 minute fireside chat.

Our first guest is Calanthia Mei, Co-founder of Masa, a leading real-time data network powering AI agents and applications. Masa raised $20 million from prominent investors including DCG. The company’s March 2024 public launch on CoinList Launchpad was a viral success, with the token sale completing in just 17 minutes. Calanthia is also a core contributor to TAO Cat, Bittensor’s first self-improving AI agent. Born from a strategic partnership between Virtuals and Masa, TAO Cat is a top-10 AI agent in the Virtuals ecosystem and got listed on Gate days after launch.

​The second guest is Ash Ahmed, Founder of Axal, which provides verifiable AI agents for any task. Axal is backed by CMT Digital and the a16z crypto accelerator.Ash Ahmed, Founder of Axal, which provides verifiable AI agents for any task. Axal is backed by CMT Digital and the a16z crypto accelerator.r.

​Thanks to our hosts Hadron Founders Club and Station3.

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/.

Summary Data assets and the pipelines that create them have become critical production infrastructure for companies. This adds a requirement for reliability and management of up-time similar to application infrastructure. In this episode Francisco Alberini and Mei Tao share their insights on what incident management looks like for data platforms and the teams that support them.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder. Are you looking for a structured and battle-tested approach for learning data engineering? Would you like to know how you can build proper data infrastructures that are built to last? Would you like to have a seasoned industry expert guide you and answer all your questions? Join Pipeline Academy, the worlds first data engineering bootcamp. Learn in small groups with likeminded professionals for 9 weeks part-time to level up in your career. The course covers the most relevant and essential data and software engineering topics that enable you to start your journey as a professional data engineer or analytics engineer. Plus we have AMAs with world-class guest speakers every week! The next cohort starts in April 2022. Visit dataengineeringpodcast.com/academy and apply now! Your host is Tobias Macey and today I’m interviewing Francisco Alberini and Mei Tao about patterns and practices for incident management in data teams

Interview

Introduction How did you get involved in the area of data management? Can you start by describing some of the ways that an "incident" can manifest in a data system?

At a high level, what are the steps and participants required to bring an incident to resolution?

The principle of incident management is familiar to application/site reliability teams. What is the current state of the art/adoption for these practices among data teams? What are the signals that teams should be monitoring to identify and alert on potential incidents?

Alerting is a subjective and nuanced practice, regardless of the context. What are some useful practices that you have seen and enacted to reduce alert fatigue