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

➡️ 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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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

➡️ Sign up for your free trial: https://www.sigmacomputing.com/go/free-trial?utm_source=youtube&utm_medium=video&utm_campaign=free_trial&utm_content=free_trial

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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Traditional analytics platforms help you understand what happened, but what about acting on those insights? Sigma Data Apps bridges this gap, allowing you to create purpose-built applications that combine live data, human inputs, and automated workflows—all connected directly to your cloud data warehouse.

In this kickoff, the Sigma team demonstrates how the platform has evolved beyond dashboards to enable true end-to-end workflows:

Learn how Writeback allows you to collect and update data directly in your cloud warehouse See how the Actions Framework automates processes with a single click Explore application layouts with modals, popovers, and tab containers to create intuitive user experiences Discover how to integrate AI models from your data warehouse for advanced analytics

Whether you're looking to replace expensive departmental software, enhance spreadsheet-based processes, or create entirely new workflows, this session will show you how Sigma's Data Apps can help you build solutions that match your exact business needs—in days, not months, without writing a single line of code.

➡️ 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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Summary In this episode of the Data Engineering Podcast, host Tobias Macy welcomes back Shinji Kim to discuss the evolving role of semantic layers in the era of AI. As they explore the challenges of managing vast data ecosystems and providing context to data users, they delve into the significance of semantic layers for AI applications. They dive into the nuances of semantic modeling, the impact of AI on data accessibility, and the importance of business logic in semantic models. Shinji shares her insights on how SelectStar is helping teams navigate these complexities, and together they cover the future of semantic modeling as a native construct in data systems. Join them for an in-depth conversation on the evolving landscape of data engineering and its intersection with AI.

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.Your host is Tobias Macey and today I'm interviewing Shinji Kim about the role of semantic layers in the era of AIInterview IntroductionHow did you get involved in the area of data management?Semantic modeling gained a lot of attention ~4-5 years ago in the context of the "modern data stack". What is your motivation for revisiting that topic today?There are several overlapping concepts – "semantic layer," "metrics layer," "headless BI." How do you define these terms, and what are the key distinctions and overlaps?Do you see these concepts converging, or do they serve distinct long-term purposes?Data warehousing and business intelligence have been around for decades now. What new value does semantic modeling beyond practices like star schemas, OLAP cubes, etc.?What benefits does a semantic model provide when integrating your data platform into AI use cases?How is it different between using AI as an interface to your analytical use cases vs. powering customer facing AI applications with your data?Putting in the effort to create and maintain a set of semantic models is non-zero. What role can LLMs play in helping to propose and construct those models?For teams who have already invested in building this capability, what additional context and metadata is necessary to provide guidance to LLMs when working with their models?What's the most effective way to create a semantic layer without turning it into a massive project? There are several technologies available for building and serving these models. What are the selection criteria that you recommend for teams who are starting down this path?What are the most interesting, innovative, or unexpected ways that you have seen semantic models used?What are the most interesting, unexpected, or challenging lessons that you have learned while working with semantic modeling?When is semantic modeling the wrong choice?What do you predict for the future of semantic modeling?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 SelectStarSun MicrosystemsMarkov Chain Monte CarloSemantic ModelingSemantic LayerMetrics LayerHeadless BICubePodcast EpisodeAtScaleStar SchemaData VaultOLAP CubeRAG == Retrieval Augmented GenerationAI Engineering Podcast EpisodeKNN == K-Nearest NeighbersHNSW == Hierarchical Navigable Small Worlddbt Metrics LayerSoda DataLookMLHexPowerBITableauSemantic View (Snowflake)Databricks GenieSnowflake Cortex AnalystMalloyThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Amazon Redshift Cookbook provides practical techniques for utilizing AWS's managed data warehousing service effectively. With this book, you'll learn to create scalable and secure data analytics solutions, tackle data integration challenges, and leverage Redshift's advanced features like data sharing and generative AI capabilities. What this Book will help me do Create end-to-end data analytics solutions from ingestion to reporting using Amazon Redshift. Optimize the performance and security of Redshift implementations to meet enterprise standards. Leverage Amazon Redshift for zero-ETL ingestion and advanced concurrency scaling. Integrate Redshift with data lakes for enhanced data processing versatility. Implement generative AI and machine learning solutions directly within Redshift environments. Author(s) Shruti Worlikar, Harshida Patel, and Anusha Challa are seasoned data experts who bring together years of experience with Amazon Web Services and data analytics. Their combined expertise enables them to offer actionable insights, hands-on recipes, and proven strategies for implementing and optimizing Amazon Redshift-based solutions. Who is it for? This book is best suited for data analysts, data engineers, and architects who are keen on mastering modern data warehouse solutions using Redshift. Readers should have some knowledge of data warehousing and familiarity with cloud concepts. Ideal for professionals looking to migrate on-premises systems or build cloud-native analytics pipelines leveraging Redshift.

In today's rapidly evolving digital landscape, modernizing mainframe systems is crucial for maintaining competitive advantage. This joint solution explores the transformative potential of migrating data warehouse extracts to Google Cloud Platform (GCP) and BigQuery. Utilizing the flexible framework built, organizations can achieve a flexible, integrated solution that accelerates time to market, ensures accurate and timely real-time data for reporting and advanced analytics, and provides self-service access. This digital transformation not only empowers stakeholders with enhanced capabilities but also significantly improves the overall customer experience.

This Session is hosted by a Google Cloud Next Sponsor.
Visit your registration profile at g.co/cloudnext to opt out of sharing your contact information with the sponsor hosting this session.

Discover how one of Europe's largest data warehouses was seamlessly migrated from Teradata on-premises to Teradata Vantage on Google Cloud in just six months. Learn about the automated data reconciliation, parallel runs, and rigorous testing that reduced operational costs by 30% and ensured zero disruption. Explore the foundation laid for future data-driven innovations. Don't miss this session to gain valuable insights and strategies for your own data transformations.

This Session is hosted by a Google Cloud Next Sponsor.
Visit your registration profile at g.co/cloudnext to opt out of sharing your contact information with the sponsor hosting this session.

Uncover data's hidden connections using graph analytics in BigQuery. This session shows how to use BigQuery's scalable infrastructure for graph analysis directly in your data warehouse. Identify patterns, connections, and influences for fraud detection, drug discovery, social network analysis, and recommendation engines. Join us to explore the latest innovations in graphs and see real-world examples. Transform your data into actionable insights with BigQuery's powerful graph capabilities.

Today's data engineering teams are overwhelmed—juggling fire drills and endless requests while relying on manual, repetitive processes for building data pipelines. This much-needed tech guide from author Josh Hall introduces a practical approach to streamlining pipeline development, empowering teams to work smarter, not harder. Using Coalesce, a modern development platform, you'll learn to standardize workflows, apply reusable design patterns, and build faster, more efficient pipelines—all without piling on tech debt. Ideal for data engineers, architects, and analysts of all experience levels, the book offers clear explanations of Coalesce's core functionality including configuring environments, defining nodes, and connecting to data warehouses. Packed with workflows and useful takeaways, it's your guide to delivering high-quality, actionable data while reducing pipeline development time. Set up Coalesce and integrate with a data warehouse Use reusable nodes and design patterns for faster development Accelerate pipeline delivery with reduced manual effort Leverage Coalesce Marketplace for advanced functionality

Summary In this episode of the Data Engineering Podcast Bartosz Mikulski talks about preparing data for AI applications. Bartosz shares his journey from data engineering to MLOps and emphasizes the importance of data testing over software development in AI contexts. He discusses the types of data assets required for AI applications, including extensive test datasets, especially in generative AI, and explains the differences in data requirements for various AI application styles. The conversation also explores the skills data engineers need to transition into AI, such as familiarity with vector databases and new data modeling strategies, and highlights the challenges of evolving AI applications, including frequent reprocessing of data when changing chunking strategies or embedding models.

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. Your host is Tobias Macey and today I'm interviewing Bartosz Mikulski about how to prepare data for use in AI applicationsInterview IntroductionHow did you get involved in the area of data management?Can you start by outlining some of the main categories of data assets that are needed for AI applications?How does the nature of the application change those requirements? (e.g. RAG app vs. agent, etc.)How do the different assets map to the stages of the application lifecycle?What are some of the common roles and divisions of responsibility that you see in the construction and operation of a "typical" AI application?For data engineers who are used to data warehousing/BI, what are the skills that map to AI apps?What are some of the data modeling patterns that are needed to support AI apps?chunking strategies metadata managementWhat are the new categories of data that data engineers need to manage in the context of AI applications?agent memory generation/evolution conversation history managementdata collection for fine tuningWhat are some of the notable evolutions in the space of AI applications and their patterns that have happened in the past ~1-2 years that relate to the responsibilities of data engineers?What are some of the skills gaps that teams should be aware of and identify training opportunities for?What are the most interesting, innovative, or unexpected ways that you have seen data teams address the needs of AI applications?What are the most interesting, unexpected, or challenging lessons that you have learned while working on AI applications and their reliance on data?What are some of the emerging trends that you are paying particular attention to?Contact Info WebsiteLinkedInParting 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 SparkRayChunking StrategiesHypothetical document embeddingsModel Fine TuningPrompt CompressionThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

With the proliferation of SaaS ELT tools many organizations don't realize that Google BigQuery offers many ways to ingest data from different platforms for free. This presentation will walk through the most important native export and data transfer mechanisms and will show how data from these platforms can be integrated to enable a comprehensive view on digital marketing efforts for an organization. Various use cases will be presented as well to generate tangible insights from this integrated data that help increase the bottom line.

As we look back at 2024, we're highlighting some of our favourite episodes of the year, and with 100 of them to choose from, it wasn't easy! The four guests we'll be recapping with are: Lea Pica - A celebrity in the data storytelling and visualisation space. Richie and Lea cover the full picture of data presentation, how to understand your audience, how to leverage hollywood storytelling and more. Out December 19.Alex Banks - Founder of Sunday Signal. Adel and Alex cover Alex’s journey into AI and what led him to create Sunday Signal, the potential of AI, prompt engineering at its most basic level, chain of thought prompting, the future of LLMs and more. Out December 23.Don Chamberlin - The renowned co-inventor of SQL. Richie and Don explore the early development of SQL, how it became standardized, the future of SQL through NoSQL and SQL++ and more. Out December 26.Tom Tunguz - general Partner at Theory Ventures, a $235m VC firm. Richie and Tom explore trends in generative AI, cloud+local hybrid workflows, data security, the future of business intelligence and data analytics, AI in the corporate sector and more. Out December 30. Rapid change seems to be the new norm within the data and AI space, and due to the ecosystem constantly changing, it can be tricky to keep up. Fortunately, any self-respecting venture capitalist looking into data and AI will stay on top of what’s changing and where the next big breakthroughs are likely to come from. We all want to know which important trends are emerging and how we can take advantage of them, so why not learn from a leading VC.  Tomasz Tunguz is a General Partner at Theory Ventures, a $235m early-stage venture capital firm. He blogs sat tomtunguz.com & co-authored Winning with Data. He has worked or works with Looker, Kustomer, Monte Carlo, Dremio, Omni, Hex, Spot, Arbitrum, Sui & many others. He was previously the product manager for Google's social media monetization team, including the Google-MySpace partnership, and managed the launches of AdSense into six new markets in Europe and Asia. Before Google, Tunguz developed systems for the Department of Homeland Security at Appian Corporation.  In the episode, Richie and Tom explore trends in generative AI, the impact of AI on professional fields, cloud+local hybrid workflows, data security, and changes in data warehousing through the use of integrated AI tools, the future of business intelligence and data analytics, the challenges and opportunities surrounding AI in the corporate sector. You'll also get to discover Tom's picks for the hottest new data startups. Links Mentioned in the Show: Tom’s BlogTheory VenturesArticle: What Air Canada Lost In ‘Remarkable’ Lying AI Chatbot Case[Course] Implementing AI Solutions in BusinessRelated Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision ScientistSign up to RADAR: AI...

🌟 Session Overview 🌟

Session Name: The State of Data Engineering Speaker: Jesse Anderson Session Description: The data landscape is fickle, and once-coveted roles like 'DBA' and 'Data Scientist' have faced challenges. Now, the spotlight shines on Data Engineers, but will they suffer the same fate? This talk dives into historical trends.

In the early 2010s, DBA/Data Warehouse was the sexiest job. Data Warehouse became the 'No Team.'

In the mid-2010s, Data Scientist was the sexiest job. Data Science became the 'mistaken for' team.

Now, Data Engineering is the sexiest job. Data Engineering has become the 'confused team.' The confusion runs rampant with questions about the industry: What is a data engineer? What do they do? Should we have all kinds of nuanced titles for variations? Just how technical should they be?

Together, let's go back in history and look for ways that data engineering can avoid the same fate as data warehousing and data science. This talk provides a thought-provoking discussion on navigating the exciting yet challenging world of data engineering. Let's avoid the pitfalls of the past and shape a future where data engineers thrive as essential drivers of innovation and success.

🚀 About Big Data and RPA 2024 🚀

Unlock the future of innovation and automation at Big Data & RPA Conference Europe 2024! 🌟 This unique event brings together the brightest minds in big data, machine learning, AI, and robotic process automation to explore cutting-edge solutions and trends shaping the tech landscape. Perfect for data engineers, analysts, RPA developers, and business leaders, the conference offers dual insights into the power of data-driven strategies and intelligent automation. 🚀 Gain practical knowledge on topics like hyperautomation, AI integration, advanced analytics, and workflow optimization while networking with global experts. Don’t miss this exclusive opportunity to expand your expertise and revolutionize your processes—all from the comfort of your home! 📊🤖✨

📅 Yearly Conferences: Curious about the evolution of QA? Check out our archive of past Big Data & RPA sessions. Watch the strategies and technologies evolve in our videos! 🚀 🔗 Find Other Years' Videos: 2023 Big Data Conference Europe https://www.youtube.com/playlist?list=PLqYhGsQ9iSEpb_oyAsg67PhpbrkCC59_g 2022 Big Data Conference Europe Online https://www.youtube.com/playlist?list=PLqYhGsQ9iSEryAOjmvdiaXTfjCg5j3HhT 2021 Big Data Conference Europe Online https://www.youtube.com/playlist?list=PLqYhGsQ9iSEqHwbQoWEXEJALFLKVDRXiP

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Don’t miss out on any updates or upcoming event information from Big Data & RPA Conference Europe. Follow us on our social media channels and visit our website to stay in the loop!

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Join this session to learn about the newest innovations in data warehousing and SQL analytics with AWS analytics services. Amazon Redshift is the AI-powered, cloud-based data warehousing solution used by tens of thousands of AWS customers to modernize data analytics workloads and generate business insights with the best price performance. Learn more about the latest capabilities launched for Amazon Redshift to further drive quick decision-making with lower costs for your organization.

Learn more: AWS re:Invent: https://go.aws/reinvent. More AWS events: https://go.aws/3kss9CP

Subscribe: More AWS videos: http://bit.ly/2O3zS75 More AWS events videos: http://bit.ly/316g9t4

About AWS: Amazon Web Services (AWS) hosts events, both online and in-person, bringing the cloud computing community together to connect, collaborate, and learn from AWS experts. AWS is the world's most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster.

AWSreInvent #AWSreInvent2024

Bill Inmon is considered the father of the data warehouse. I just got back from spending a couple of days with Bill, and we discussed the history of the data industry and the data warehouse. On my flight back, I realized people could benefit from a short version of our conversation.

In this short chat, we discuss what a data warehouse is (and is not), Kimball and Inmon, the origins of the data warehouse, and much more.

Amazon Redshift Serverless uses AI and ML to automatically provision and scale data warehouse capacity and deliver fast performance for demanding and unpredictable workloads. It delivers a first-class data sharing experience where you can isolate workloads and read from and write to the same Redshift databases to build multi-warehouse, data mesh architectures and simplify SQL analytics. Join this session to learn best practices and key considerations for data sharing and SQL analytics with Redshift Serverless to optimize price-performance for your most demanding analytics workloads.

Learn more: AWS re:Invent: https://go.aws/reinvent. More AWS events: https://go.aws/3kss9CP

Subscribe: More AWS videos: http://bit.ly/2O3zS75 More AWS events videos: http://bit.ly/316g9t4

About AWS: Amazon Web Services (AWS) hosts events, both online and in-person, bringing the cloud computing community together to connect, collaborate, and learn from AWS experts. AWS is the world's most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster.

AWSreInvent #AWSreInvent2024

Are you spending too much time on data warehouse management tasks like hardware provisioning, software patching, and performance tuning and not enough time building your applications and innovating with data? Tens of thousands of AWS customers rely on Amazon Redshift every day to run and scale analytics in seconds on all their data without managing data warehouse infrastructure. In this session, hear from AWS customers who share their journeys moving to a modern cloud data warehouse and analytics with Amazon Redshift. Learn best practices for building powerful analytics and machine learning applications and operating at scale while keeping costs low.

Learn more: AWS re:Invent: https://go.aws/reinvent. More AWS events: https://go.aws/3kss9CP

Subscribe: More AWS videos: http://bit.ly/2O3zS75 More AWS events videos: http://bit.ly/316g9t4

About AWS: Amazon Web Services (AWS) hosts events, both online and in-person, bringing the cloud computing community together to connect, collaborate, and learn from AWS experts. AWS is the world's most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster.

AWSreInvent #AWSreInvent2024