CDAOs and AI leaders are grappling with two crucial questions: 1. What public cloud provider should we choose for AI and GenAI initiatives, and 2. how do we assemble the right cloud architecture to scale and deploy AI more effectively?
This session compares public cloud AI and Generative AI architectures from AWS, Azure and GCP and provides insights on their points of differentiation.

AI is having a huge impact, but is not the only thing with societal, technological, and organizational implications driving change in data and analytics. We examine trends in areas such as complexity, trust, and empowerment facing leaders and teams as they make decisions in all aspects of their bet-the-business D&A strategy.

Summary In this episode of the Data Engineering Podcast Mai-Lan Tomsen Bukovec, Vice President of Technology at AWS, talks about the evolution of Amazon S3 and its profound impact on data architecture. From her work on compute systems to leading the development and operations of S3, Mylan shares insights on how S3 has become a foundational element in modern data systems, enabling scalable and cost-effective data lakes since its launch alongside Hadoop in 2006. She discusses the architectural patterns enabled by S3, the importance of metadata in data management, and how S3's evolution has been driven by customer needs, leading to innovations like strong consistency and S3 tables.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.This is a pharmaceutical Ad for Soda Data Quality. Do you suffer from chronic dashboard distrust? Are broken pipelines and silent schema changes wreaking havoc on your analytics? You may be experiencing symptoms of Undiagnosed Data Quality Syndrome — also known as UDQS. Ask your data team about Soda. With Soda Metrics Observability, you can track the health of your KPIs and metrics across the business — automatically detecting anomalies before your CEO does. It’s 70% more accurate than industry benchmarks, and the fastest in the category, analyzing 1.1 billion rows in just 64 seconds. And with Collaborative Data Contracts, engineers and business can finally agree on what “done” looks like — so you can stop fighting over column names, and start trusting your data again.Whether you’re a data engineer, analytics lead, or just someone who cries when a dashboard flatlines, Soda may be right for you. Side effects of implementing Soda may include: Increased trust in your metrics, reduced late-night Slack emergencies, spontaneous high-fives across departments, fewer meetings and less back-and-forth with business stakeholders, and in rare cases, a newfound love of data. Sign up today to get a chance to win a $1000+ custom mechanical keyboard. Visit dataengineeringpodcast.com/soda to sign up and follow Soda’s launch week. It starts June 9th.Your host is Tobias Macey and today I'm interviewing Mai-Lan Tomsen Bukovec about the evolutions of S3 and how it has transformed data architectureInterview IntroductionHow did you get involved in the area of data management?Most everyone listening knows what S3 is, but can you start by giving a quick summary of what roles it plays in the data ecosystem?What are the major generational epochs in S3, with a particular focus on analytical/ML data systems?The first major driver of analytical usage for S3 was the Hadoop ecosystem. What are the other elements of the data ecosystem that helped shape the product direction of S3?Data storage and retrieval have been core primitives in computing since its inception. What are the characteristics of S3 and all of its copycats that led to such a difference in architectural patterns vs. other shared data technologies? (e.g. NFS, Gluster, Ceph, Samba, etc.)How does the unified pool of storage that is exemplified by S3 help to blur the boundaries between application data, analytical data, and ML/AI data?What are some of the default patterns for storage and retrieval across those three buckets that can lead to anti-patterns which add friction when trying to unify those use cases?The age of AI is leading to a massive potential for unlocking unstructured data, for which S3 has been a massive dumping ground over the years. How is that changing the ways that your customers think about the value of the assets that they have been hoarding for so long?What new architectural patterns is that generating?What are the most interesting, innovative, or unexpected ways that you have seen S3 used for analytical/ML/Ai applications?What are the most interesting, unexpected, or challenging lessons that you have learned while working on S3?When is S3 the wrong choice?What do you have planned for the future of S3?Contact Info LinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links AWS S3KinesisKafkaSQSEMRDrupalWordpressNetflix Blog on S3 as a Source of TruthHadoopMapReduceNasa JPLFINRA == Financial Industry Regulatory AuthorityS3 Object VersioningS3 Cross RegionS3 TablesIcebergParquetAWS KMSIceberg RESTDuckDBNFS == Network File SystemSambaGlusterFSCephMinIOS3 MetadataPhotoshop Generative FillAdobe FireflyTurbotax AI AssistantAWS Access AnalyzerData ProductsS3 Access PointAWS Nova ModelsLexisNexis ProtegeS3 Intelligent TieringS3 Principal Engineering TenetsThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

In this deeply personal episode, I share the story of a chaotic week, mounting pressure, and a craving for Sonic fries. But more importantly, I take you inside the quiet tool I built—an AI Thought Organizer. It didn’t coach me. It didn’t solve anything. It just… reflected what I was already feeling. Inside the episode: The emotional spiral that led to this toolHow I built it (yes, with code—but it’s not technical)Why the laugh that followed changed everythingWhat this experience taught me about overwhelm, clarity, and AIWhether you're a builder, creator, or someone looking for a new kind of peace—you’ll want to hear this. 🛠 Full blog and source code: 1] Medium: https://medium.com/data-science-collective/i-fed-my-thoughts-to-an-ai-and-it-helped-me-breathe-again-c9796f17c791 2] Website: https://mukundansankar.substack.com/p/i-fed-my-thoughts-to-an-ai-and-it 🍟 Craving fries after listening? You’re not alone.

In today’s conversation, host Kirk Offel sits down with Ojonimi Bako, CEO and co-founder of Kaya AI, to dive deep into the intersection of technology, labor, and the rapidly-evolving construction and data center industries. Ojonimi shares his personal journey from Nigeria to the United States, building a unique perspective that blends tech innovation with hands-on construction experience.For more about us: https://linktr.ee/overwatchmissioncritical

As a data consulting firm helping clients solve their data challenges, Maverick Data faced its own operational inefficiencies with fragmented systems for time tracking, client management, project staffing, and financial reporting. Instead of continuing to juggle multiple disconnected applications, they decided to practice what they preach and build a unified solution.

In this session, Spencer Baucke (Co-founder) will demonstrate how Maverick Data built a comprehensive business operations app in Sigma to:

Centralize employee, client, and project management with appropriate role-based security controls Streamline time entry and automated invoice generation to eliminate manual processes Integrate financial data to create real-time projections and business insights Automate reporting with scheduled emails to ensure timely updates for team members By consolidating operations into a single data app, Maverick Data reduced software spend, gained unprecedented visibility into their business performance, and dramatically improved decision-making processes between leadership. The solution has inspired new client solutions based on their internal success.

➡️ Learn more about Data Apps: https://www.sigmacomputing.com/product/data-applications?utm_source=youtube&utm_medium=organic&utm_campaign=data_apps_conference&utm_content=pp_data_apps

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What can you build with Sigma Data Apps in just two hours? In this friendly competition, three contestants tackle a real-world challenge: creating a rent-versus-buy calculator using Denver metro real estate data.

Each contestant was given the same dataset and requirements:

Feature data warehouse writeback to build a workflow Use a scoring model to make a clear buy vs. rent recommendation Demonstrate Sigma’s capabilities and ease of app development See the final app demos, hear direct feedback from the judges, and witness the crowning of the first-ever Sigma Data Apps Golden GOAT Champion. Whether you're a seasoned data pro or just getting started, this competition proves that with Sigma, anyone can build powerful applications—fast.

Participants Ashley Bennett Senior Associate, Spaulding Ridge Eric Heidbreder Owner & Principal Consultant, Chicago Analytics Services Katrina Menne Alliance and Tech Lead, Aimpoint Digital

Judges David Porter Head of Partner Engineering, Sigma Donny Alfano SI Partner Solutions Director, Sigma Alfonso Franklin VP of Solution Engineering, Sigma

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Managing cloud costs requires accurate resource tagging, but maintaining completeness and accuracy is a challenge. In this session, Mitchell Ertle (Senior Partner Solutions Architect) and Josue Bogran (Data & AI Architect) demonstrate how Sigma and Databricks combine to streamline FinOps and resource management with AI-driven cost attribution and workflow automation.

Through a practical demonstration, you'll see:

Identify and classify untagged Databricks pipelines with a cost attribution app Use GenAI from Databricks to suggest tags with human-in-the-loop approval Enable bidirectional data flow between Sigma and Databricks for real-time updates Automate workflows with Sigma’s actions framework Ensure security and governance by inheriting Unity Catalog permissions Discover why this combination is powerful—Sigma provides intuitive application building while Databricks delivers computation, AI/ML capabilities, and data storage. These platforms create solutions business users can interact with directly, without technical expertise.

Whether in data engineering, finance, or operations, learn how Sigma + Databricks can automate workflows, optimize costs, and drive business impact.

➡️ Learn more about Data Apps: https://www.sigmacomputing.com/product/data-applications?utm_source=youtube&utm_medium=organic&utm_campaign=data_apps_conference&utm_content=pp_data_apps

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This regional bank needed a robust system for funds transfer pricing and profitability analysis across branches, products, and customers. After discovering Sigma's capabilities for complex financial calculations and data modeling, the bank's team built a comprehensive solution for modern banking needs.

In this session, the Senior Financial Analyst and IT Reporting Analyst will showcase how they developed a Sigma-powered solution to:

Streamline expense allocation workflows Calculate and apply sophisticated funds transfer pricing Enable detailed variance analysis and research Create foundation for branch, product, and customer-level profitability insights Watch this session to see how this regional bank leveraged Sigma Data Apps to build a scalable, data-driven financial reporting system that enhances decision-making and operational efficiency.

➡️ Learn more about Data Apps: https://www.sigmacomputing.com/product/data-applications?utm_source=youtube&utm_medium=organic&utm_campaign=data_apps_conference&utm_content=pp_data_apps

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In today's rapidly changing markets, businesses in Energy, Manufacturing, and Retail CPG face constant supply and demand shifts—from seasonal patterns to unexpected disruptions. Traditional planning approaches with static reports and slow update cycles leave companies struggling to adapt quickly enough to maintain optimal operations.

In this session, Anupam Gupta and Shivang Ganjoo from Celebal Technologies will reveal how they've transformed planning processes with a Sigma-powered solution that:

Replaces manual forecasting with AI-driven models that continuously learn and adapt, improving forecast accuracy by 20% Enables dynamic scenario planning with real-time simulations to evaluate multiple business paths simultaneously Automates previously manual workflows, cutting planning cycle times by 50% Allows write-back of modified plans directly to the data warehouse for immediate execution Join this session to learn how Sigma Data Apps are transforming business planning from a periodic, backward-looking exercise into a continuous, forward-looking competitive advantage.

➡️ Learn more about Data Apps: https://www.sigmacomputing.com/product/data-applications?utm_source=youtube&utm_medium=organic&utm_campaign=data_apps_conference&utm_content=pp_data_apps

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Managing sales commissions can be complex, especially as teams scale and compensation structures grow more intricate. phData previously relied on spreadsheets to handle quotas, accelerators, SPIFF bonuses, and deal splits—but as the sales team expanded, manual tracking became inefficient, error-prone, and difficult to validate.

In this session, the phData team will demonstrate how they built a comprehensive commission management system using Sigma Data Apps to:

Handle complex commission structures including quota attainment, accelerator gates, and SPIFF bonuses Provide real-time commission visibility that gamifies seller performance Enable flexible deal attribution and splits through input tables Support rapid validation and refinement before finalizing configurations in Salesforce and Snowflake Watch the demo to see how phData replaced spreadsheet chaos with a powerful commission tracking system that not only streamlines operations but also drives sales momentum.

➡️ Learn more about Data Apps: https://www.sigmacomputing.com/product/data-applications?utm_source=youtube&utm_medium=organic&utm_campaign=data_apps_conference&utm_content=pp_data_apps

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Sales pipeline forecasting is essential for revenue planning, but traditional approaches rely on either unstructured spreadsheets or rigid SaaS applications like Clari—creating data silos, limiting customization, and forcing teams to switch between multiple tools for complete pipeline visibility.

In this session, Oscar Bashaw (Solution Architect) will demonstrate how to:

Create a unified sales forecasting app with role-specific views for both reps and managers Implement structured data capture with input tables for consistent deal-level forecasting Consolidate multiple data sources (CRM, call recordings, product usage) into a single tool Leverage AI models from your data warehouse to provide intelligent deal insights without leaving the workflow Build dynamic visualizations with real-time pipeline coverage and attainment tracking Use AI to surface risk signals by analyzing call sentiment, deal history, and activity trends from connected data sources With Sigma, sales teams can move beyond disconnected spreadsheets and inflexible SaaS tools to create a dynamic, AI-powered forecasting solution that scales with your business. Join this session for a complete walkthrough of the app's architecture and learn how to build similar capabilities for your organization—reducing costs while improving forecast accuracy and sales team productivity.

➡️ Learn more about Data Apps: https://www.sigmacomputing.com/product/data-applications?utm_source=youtube&utm_medium=organic&utm_campaign=data_apps_conference&utm_content=pp_data_apps

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Managing performance reviews, calibrations, and compensation adjustments across thousands of employees at DoorDash was becoming increasingly complex—especially after the Wolt acquisition 2x the employee base. Teams struggled with spreadsheet chaos, security risks, and inefficient manual processes.

In this session, Ashwin Murugappan (People Applications & Intelligence Engineer) will share how DoorDash built the Cycle Management Hub using Sigma Data Apps to:

Eliminate spreadsheet versioning issues with real-time, governed collaboration Improve efficiency and accuracy by integrating directly with Workday & Snowflake Enhance security & compliance with role-based access controls (RLS) Watch the demo and learn how Sigma’s input tables, write-back capabilities, and real-time data processing helped DoorDash modernize its HR data workflows at scale.

➡️ Learn more about Data Apps: https://www.sigmacomputing.com/product/data-applications?utm_source=youtube&utm_medium=organic&utm_campaign=data_apps_conference&utm_content=pp_data_apps

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The enterprise software landscape is at a pivotal turning point. For decades, organizations have been trapped in a cycle of siloed applications—first in on-premise data centers, and then repackaged as cloud SaaS solutions. While infrastructure has become more flexible, scalable, and cost-effective, the applications running on top remain frustratingly rigid, expensive, and disconnected.

In this session, Mike Palmer (CEO of Sigma) and Carter Busse (CIO of Workato) discuss the shift from "best-of-breed" point solutions to an "end-to-end" approach powered by data apps. They'll explore:

Why traditional SaaS applications force organizations to adapt their workflows to software limitations rather than the other way around How the centralization of data in cloud warehouses creates the foundation for building custom, integrated workflows Real-world examples of organizations replacing expensive, disconnected tools with purpose-built data apps The future of enterprise software, including predictions on how AI will reshape application development and data accessibility Practical strategies for starting your data apps journey without creating new technology sprawl Learn how forward-thinking organizations are using data apps to create workflows that better match their business needs, increase decision-making velocity, boost accuracy, and dramatically reduce software costs—all while maintaining enterprise-grade governance and security.

➡️ Learn more about Data Apps: https://www.sigmacomputing.com/product/data-applications?utm_source=youtube&utm_medium=organic&utm_campaign=data_apps_conference&utm_content=pp_data_apps

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sigma #sigmacomputing #dataanalytics #dataanalysis #businessintelligence #cloudcomputing #clouddata #datacloud #datastructures #datadriven #datadrivendecisionmaking #datadriveninsights #businessdecisions #datadrivendecisions #embeddedanalytics #cloudcomputing #SigmaAI #AI #AIdataanalytics #AIdataanalysis #GPT #dataprivacy #python #dataintelligence #moderndataarchitecture

Managing facilities maintenance operations presents a complex logistical challenge: how to efficiently handle service requests, match technicians with appropriate skills to the right jobs, and minimize travel time between locations. Traditional approaches often result in underutilized staff, delayed responses, and poor visibility into operations.

In this session, the Aimpoint Digital team will showcase their Facilities Management Data App built with Sigma that transforms this critical business function through:

Improve workforce efficiency with an advanced scheduling engine that reduces downtime Enhance real-time visibility with live status updates for managers and customers Streamline operations by integrating service requests, dispatching, and data warehouse write-back Scale across industries with a flexible system adaptable for healthcare, retail, logistics, and more Join this session to see how Sigma Data Apps can transform complex operational workflows into streamlined, intelligent systems that boost productivity and customer satisfaction.

➡️ Learn more about Data Apps: https://www.sigmacomputing.com/product/data-applications?utm_source=youtube&utm_medium=organic&utm_campaign=data_apps_conference&utm_content=pp_data_apps

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Sales forecasting and demand planning are critical business processes, but most organizations still rely on spreadsheets—leading to version control issues, fragmented approvals, and lack of historical tracking. Traditional BI tools struggle to solve these problems because they don’t allow for cell-level edits, inline comments, and structured approval workflows in a governed way.

In this session, Ian Reed will demonstrate how to:

Enable real-time forecasting by replacing manual spreadsheets with a structured, cloud-based data app Allow for cell-level edits and inline commentary so teams can capture assumptions behind forecast changes Implement an automated approval workflow with proper governance Integrate seamlessly with live data sources for continuous updates and visibility into actual vs. forecasted performance Track historical changes and maintain audit trails of all modifications

With Sigma, demand planning is no longer a fragmented, error-prone process—it’s a seamless, governed workflow that scales with business growth. Join this session for a demo and a step-by-step walkthrough of how this app was built, proving that anyone can create a highly customizable, enterprise-grade demand planning system in Sigma without deep technical expertise.

➡️ Learn more about Data Apps: https://www.sigmacomputing.com/product/data-applications?utm_source=youtube&utm_medium=organic&utm_campaign=data_apps_conference&utm_content=pp_data_apps

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Managing contracts in spreadsheets was creating significant challenges for TEKSystems - crashes, version control issues, and no connection to enterprise data were impacting 20+ users trying to collaborate on thousands of records.

In this session, Riley Owens (Controller) and Chase Brookmyer (Business Reporting Analyst) will demonstrate how they:

Built a contract lifecycle management app with no formal development background Transformed from an unstable spreadsheet to a centralized data app Created streamlined workflows and reporting capabilities Accomplished the build in approximately 10 hours of learning and development By leveraging Sigma’s flexible architecture, TEKSystems was able to build this solution in a matter of hours, demonstrating how teams without formal development experience can create powerful workflow automation tools. Watch this session to see how they transformed contract management and what’s next for their data-driven approach.

➡️ Learn more about Data Apps: https://www.sigmacomputing.com/product/data-applications?utm_source=youtube&utm_medium=organic&utm_campaign=data_apps_conference&utm_content=pp_data_apps

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AdventHealth’s investment team needed a better way to merge the need for real-time reporting for portfolio metrics and managing portfolio adjustments, moving beyond outdated, static reports that slowed decision-making and introduced inefficiencies.

In this session, Kelly Booth (Director of Data Strategy & Analytics, AdventHealth) and Jared Flores (Founder & Managing Director, Data Flow) will share how they partnered to modernize investment portfolio metrics reporting with a data app that enables real-time value adjustments, audit logging, and vendor tracking. This tool has helped:

Reduce manual errors by enabling real-time portfolio adjustments Save hours of work by eliminating back-and-forth data corrections Increase confidence in reporting with a fully auditable, write-back-enabled workflow Join us to see a live demo of the app in action and hear how AdventHealth uses Sigma Data Apps to drive efficiency and accuracy in investment data management.

➡️ Learn more about Data Apps: https://www.sigmacomputing.com/product/data-applications?utm_source=youtube&utm_medium=organic&utm_campaign=data_apps_conference&utm_content=pp_data_apps

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Managing customer interactions across multiple disconnected platforms creates inefficiencies and delays in resolving support tickets. At WHOOP, support agents had to manually navigate through siloed data across payments, ERP, and ticketing systems, slowing down response times and impacting customer satisfaction.In this session, Matt Luizzi (Director of Business Analytics, WHOOP) and Brendan Farley (Sales Engineer, Snowflake) will showcase how WHOOP:

Consolidated fragmented data from multiple systems into a unified customer support app. Enabled real-time access to customer history, allowing agents to quickly surface relevant insights. Eliminated the need for custom engineering by leveraging Sigma’s no-code interface to build interactive workflows. Accelerated ticket resolution by allowing support teams to take action directly within Sigma, reducing dependency on multiple SaaS tools. Improved forecasting and decision-making by implementing AI-powered analytics on top of Snowflake. Before Sigma, getting a full view of customer issues required navigating across multiple tools—now, WHOOP’s customer support team can access, analyze, and act on real-time data in a single interface. Join us for an inside look at how WHOOP and Snowflake partnered to build a modern customer support data app that enhances efficiency and customer experience.

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