Where I express personal thoughts in words
Julia Schottenstein is a Product Manager at dbt Labs, the maker of the popular open-source project dbt. Before joining dbt Labs, Julia was an investor at New Enterprise Associates, where she invested in infrastructure, developer tools, open-source, and data startups. She currently sits on the boards of Sentry and Metabase while being an active angel investor. Julia graduated from Stanford University with degrees in Computer Science and Management Science & Engineering.
The end of the year is usually a time for reflection, gratitude, and planning. With our inboxes quiet and our schedules free of meetings, we can afford to take a moment and think about what we achieved and what’s next. This is my 8th straight year writing an annual review, and I want to switch up the format this time by looking at critical areas such as work, productivity, health, relationships, and learning. Because it’s important to both acknowledge what went well and what could have gone better, I’ll make sure to highlight areas where I want to improve.
Einat Orr is the CEO and Co-founder of Treeverse, the company behind lakeFS, an open-source platform that delivers resilience and manageability to object-storage-based data lakes. She received her PhD. in Mathematics from Tel Aviv University in optimization in graph theory. Einat previously led several engineering organizations, most recently as the CTO at SimilarWeb.
Last September, I attended Snorkel AI’s The Future of Data-Centric AI. This summit connects experts on data-centric AI from academia, research, and industry to explore the shift from a model-centric practice to a data-centric approach to building AI. There were talks discussing the challenges, solutions, and ideas to make AI practical, both now and in the future.
In this blog recap, I will dissect content from the conference’s session talks, covering a wide range of topics from weak supervision and fine-grained error analysis to MLOps design principles and data-centric AI case studies.
I attended the Open-Source Data Stack Conference in late September 2021, the first-ever conference dedicated to building a modern data stack using open-source data solutions. The emergence of the modern data stack has seen a rapid spike in the number of data tools an organization can use to drive better decision-making. Open-source software helps you control the end-to-end flow of customer data throughout your organization to guarantee data auditability, allow data governance, support consumer data privacy, and enable productive engineer workflows.
In this blog recap, I will dissect content from the conference’s session talks, each being a building block of the open-source data stack to demonstrate how teams can build a data stack that reflects their needs.
Prukalpa Sankar is the Co-founder of Atlan. Atlan is a modern data collaboration workspace (like Github for engineering or Figma for design). By acting as a virtual hub for data assets ranging from tables and dashboards to models & 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 Slack, BI tools, data science tools and more. As a pioneer in the space, Atlan was recognized by Gartner as a Cool Vendor in DataOps, as one of the top 3 companies globally.
Prukalpa previously co-founded SocialCops, a world-leading data-for-good company recognized as a New York Times Global Visionary and a World Economic Forum Tech Pioneer. SocialCops is behind landmark data projects, including India’s National Data Platform and SDGs global monitoring in collaboration with the United Nations.
Prukalpa was awarded the Economic Times Emerging Entrepreneur for the Year and recognized in multiple lists such as Forbes 30u30, Fortune 40u40, and Top 10 CNBC Young Business Women 2016.
Michel Tricot has been working in data engineering for 15 years. Originally from France, Michel came to the US in 2011 to join a small startup named LiveRamp. As the company grew, he became the Head of Integrations and Director of Engineering, where his team built and scaled over 1,000 data ingestion and distribution connectors to replicate hundreds of TB worth of data every day.
After LiveRamp’s acquisition and later IPO (NYSE:RAMP), he wanted to return to an early-stage startup. So he joined rideOS as Director of Engineering, again deep in data engineering. While there, he realized that companies were always trying to solve the same problem repeatedly, which should be solved once and for all.
This was when he decided to start a new company, and Airbyte was born.
Cindi Howson, the Chief Data Strategy Officer at ThoughtSpot, is an analytics and Business Intelligence expert with more than 20 years of experience and a flair for bridging business needs with technology.
Cindi was previously the Vice President in data and analytics at Gartner, where she was the lead author of the Analytics and BI Magic Quadrant. Additionally, she led the data and analytics maturity model, as well as research initiatives in data and AI for good, NLP/BI Search, and augmented analytics.
Prior to this, she was the founder of BI Scorecard, a contributor to Information Week, and the author of several books, including (1) Successful Business Intelligence: Unlock the Value of BI & Big Data and (2) SAP BusinessObjects BI 4.0: The Complete Reference. She has an MBA from Rice University and a BA from the University of Maryland.
Last month, I attended apply(), Tecton’s follow-up virtual event of their ML data engineering conference series. I’ve previously written a recap of their inaugural event, a whirlwind tour of wide-ranging topics such as feature stores, ML platforms, and research on data engineering. In this shorter post, I would like to share content from the main talks and lightning talks presented at the community meetup. Topics include ML systems research, ML observability, streaming architecture, and more.
