Speaker: Alex Powers ⚠️ Start Date: Thu, Mar 20th 2025 · 7:00 PM EEST (5:00 PM UTC) Language: ENGLIGH Location: Online (link visible for attendees)

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Description:

TBC

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At the end of the Meetup we'll have a Raffle with prizes offered by Enterprise DNA: 2 (two) 1 year FREE Membership Licenses on EDNA Platform for two lucky winners from the Live attendees !

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Speaker: Alex Powers MSFT Fabric CAT Team

Alex is a Senior Program Manager at Microsoft on the Fabric Customer Advisory Team (Fabric CAT), Data Fabricator and Lord of the Links if you watch the brilliant Guy in a Cube. He is passionate about Community and Power Query. He has helped to develop Power Query knowledge by running a 30 Day Challenge on Query Folding which is still relevant now.

Connect with Alex here:

• LinkedIn
• Blog - https://itsnotaboutthecell.com/about/
• Twitter - https://x.com/notaboutthecell

Join us to explore how Power Query leverages AI-powered experiences to empower both citizen and professional data integrators.

By simplifying complex tasks, AI powered experiences and Copilot for Data Factory enhance productivity and streamlines your solutions workflow. Here’s how it can benefit you:

• Intelligent code generation: Copilot collaborates like a subject-matter expert (SME), transforming your data using natural language input. Articulate your requirements, and Copilot generates the code you need.
• Column from example: Specify a few output values for your new columns and Power Query generates the right column generation logic for you.
• Table from example: Extract data from a Text/CSV file. Just specify sample output values from the source.
• Fuzzy merge: Built-in fuzzy matching algorithm lets you to merge multiple tables using an approximate match to correlate things like slightly different versions of product names, customer names or address information—to name a few examples.
• and more...

Alex is a Senior Program Manager at Microsoft on the Fabric Customer Advisory Team (Fabric CAT), Data Fabricator and Lord of the Links if you watch the brilliant Guy in a Cube. He is passionate about Community and Power Query. He inspired me and helped me develop my Power Query knowledge by running a 30 Day Challenge on Query Folding which is still relevant now. https://youtu.be/9sV3hIn8VTY?si=T4a5FL4ZctQETpBv

Not only did it help me technically, but I made lots of friends that were also participating, including the one and only Cristian Angyal who has become such a dear friend. So thank you Alex and yay, I am super excited.

Summary

Data lakehouse architectures are gaining popularity due to the flexibility and cost effectiveness that they offer. The link that bridges the gap between data lake and warehouse capabilities is the catalog. The primary purpose of the catalog is to inform the query engine of what data exists and where, but the Nessie project aims to go beyond that simple utility. In this episode Alex Merced explains how the branching and merging functionality in Nessie allows you to use the same versioning semantics for your data lakehouse that you are used to from Git.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster today to get started. Your first 30 days are free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Join us at the top event for the global data community, Data Council Austin. From March 26-28th 2024, we'll play host to hundreds of attendees, 100 top speakers and dozens of startups that are advancing data science, engineering and AI. Data Council attendees are amazing founders, data scientists, lead engineers, CTOs, heads of data, investors and community organizers who are all working together to build the future of data and sharing their insights and learnings through deeply technical talks. As a listener to the Data Engineering Podcast you can get a special discount off regular priced and late bird tickets by using the promo code dataengpod20. Don't miss out on our only event this year! Visit dataengineeringpodcast.com/data-council and use code dataengpod20 to register today! Your host is Tobias Macey and today I'm interviewing Alex Merced, developer advocate at Dremio and co-author of the upcoming book from O'reilly, "Apache Iceberg, The definitive Guide", about Nessie, a git-like versioned catalog for data lakes using Apache Iceberg

Interview

Introduction How did you get involved in the area of data management? Can you describe what Nessie is and the story behind it? What are the core problems/complexities that Nessie is designed to solve? The closest analogue to Nessie that I've seen in the ecosystem is LakeFS. What are the features that would lead someone to choose one or the other for a given use case? Why would someone choose Nessie over native table-level branching in the Apache Iceberg spec? How do the versioning capabilities compare to/augment the data versioning in Iceberg? What are some of the sources of, and challenges in resolving, merge conflicts between table branches? Can you describe the architecture of Nessie? How have the design and goals of the project changed since it was first created? What is involved

Build new analytics and AI models and supercharge your intelligent app strategy across your organization. Increase developer velocity with Copilot in Fabric and empower your data scientists and data analysts with Semantic Link, bridging the world of business intelligence and AI. Train custom ML models with Azure ML and Fabric Data Science, democratizing AI across lines-of-business and increasing collaboration between data professionals and ML professionals.

To learn more, please check out these resources: * https://aka.ms/Ignite23CollectionsBRK225H * https://info.microsoft.com/ww-landing-contact-me-for-events-m365-in-person-events.html?LCID=en-us&ls=407628-contactme-formfill * https://aka.ms/azure-ignite2023-dataaiblog

𝗦𝗽𝗲𝗮𝗸𝗲𝗿𝘀: * Justyna Lucznik * Nellie Gustafsson * Misha Desai * Thasmika Gokal * Abhishek Narain * Alex Powers * Alex van Grootel * Ed Donahue * Lukasz Pawlowski * Raj RIkhy * Wilson Lee

𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻: This video is one of many sessions delivered for the Microsoft Ignite 2023 event. View sessions on-demand and learn more about Microsoft Ignite at https://ignite.microsoft.com

BRK225 | English (US) | Data

MSIgnite

In this session, you’ll learn how to get started with Microsoft Fabric by bringing your existing talents with Power BI into the age of data analytics for the era of AI. You’ll explore Data Factory for ingesting data, Lakehouses for flexibility with data lake storage with data warehousing analytics and the groundbreaking Direct Lake dataset mode for analyzing very large data volumes in Power BI reports. By the end, you’ll understand how to use Microsoft Fabric to create end-to-end solutions at limitless scales.

Hello everyone! We will host Alex Powers, Microsoft Senior Program Manager, Power BI CAT.

This a collaboration with Power BI DC \| Meetup

Session title: Microsoft Fabric for Power BI users

Description: In this session, you’ll learn how to get started with Microsoft Fabric by bringing your existing talents with Power BI into the age of data analytics for the era of AI. You’ll explore Data Factory for ingesting data, Lakehouses for flexibility with data lake storage with data warehousing analytics and the groundbreaking Direct Lake dataset mode for analyzing very large data volumes in Power BI reports. By the end, you’ll understand how to use Microsoft Fabric to create end-to-end solutions at limitless scales.

Speaker bio: From financial services to felines, the World Wide Web to professional wrestling - Alex Powers has an affinity for the conventional and unconventional when it comes to information. A self-proclaimed Microsoft data & AI evangelist; Alex Powers enjoys contributing to online forums, in-person training and sharing his passion for empowering others using Microsoft technologies.

This online meeting will start at 11:30AM EST and be recorded.

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Hadoop in Practice collects 85 Hadoop examples and presents them in a problem/solution format. Each technique addresses a specific task you'll face, like querying big data using Pig or writing a log file loader. You'll explore each problem step by step, learning both how to build and deploy that specific solution along with the thinking that went into its design. As you work through the tasks, you'll find yourself growing more comfortable with Hadoop and at home in the world of big data. About the Technology Hadoop is an open source MapReduce platform designed to query and analyze data distributed across large clusters. Especially effective for big data systems, Hadoop powers mission-critical software at Apple, eBay, LinkedIn, Yahoo, and Facebook. It offers developers handy ways to store, manage, and analyze data. About the Book Hadoop in Practice collects 85 battle-tested examples and presents them in a problem/solution format. It balances conceptual foundations with practical recipes for key problem areas like data ingress and egress, serialization, and LZO compression. You'll explore each technique step by step, learning how to build a specific solution along with the thinking that went into it. As a bonus, the book's examples create a well-structured and understandable codebase you can tweak to meet your own needs. What's Inside Conceptual overview of Hadoop and MapReduce 85 practical, tested techniques Real problems, real solutions How to integrate MapReduce and R About the Reader This book assumes you've already started exploring Hadoop and want concrete advice on how to use it in production. About the Author Alex Holmes is a senior software engineer with extensive expertise in solving big data problems using Hadoop. He has presented at JavaOne and Jazoon and is a technical lead at VeriSign. Quotes Interesting topics that tickle the creative brain. - Mark Kemna, Brillig Ties together the Hadoop ecosystem technologies. - Ayon Sinha, Britely Comprehensive … high-quality code samples. - Chris Nauroth, The Walt Disney Company Covers all of the variants of Hadoop, not just the Apache distribution. - Ted Dunning, MapR Technologies Charts a path to the future. - Alexey Gayduk, Grid Dynamics