This is a talk about how we thought we had Big Data, and we built everything planning for Big Data, but then it turns out we didn't have Big Data, and while that's nice and fun and seems more chill, it's actually ruining everything, and I am here asking you to please help us figure out what we are supposed to do now.

📓 Resources Big Data is Dead: https://motherduck.com/blog/big-data-... Small Data Manifesto: https://motherduck.com/blog/small-dat... Is Excel Immortal?: https://benn.substack.com/p/is-excel-immortal Small Data SF: https://www.smalldatasf.com/

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Mode founder David Wheeler challenges the data industry's obsession with "big data," arguing that most companies are actually working with "small data," and our tools are failing us. This talk deconstructs the common sales narrative for BI tools, exposing why the promise of finding game-changing insights through data exploration often falls flat. If you've ever built dashboards nobody uses or wondered why your analytics platform doesn't deliver on its promises, this is a must-watch reality check on the modern data stack.

We explore the standard BI demo, where an analyst uncovers a critical insight by drilling into event data. This story sells tools like Tableau and Power BI, but it rarely reflects reality, leading to a "revolving door of BI" as companies swap tools every few years. Discover why the narrative of the intrepid analyst finding a needle in the haystack only works in movies and how this disconnect creates a cycle of failed data initiatives and unused "trashboards."

The presentation traces our belief that "data is the new oil" back to the early 2010s, with examples from Target's predictive analytics and Facebook's growth hacking. However, these successes were built on truly massive datasets. For most businesses, analyzing small data results in noisy charts that offer vague "directional vibes" rather than clear, actionable insights. We contrast the promise of big data analytics with the practical challenges of small data interpretation.

Finally, learn actionable strategies for extracting real value from the data you actually have. We argue that BI tools should shift focus from data exploration to data interpretation, helping users understand what their charts actually mean. Learn why "doing things that don't scale," like manually analyzing individual customer journeys, can be more effective than complex models for small datasets. This talk offers a new perspective for data scientists, analysts, and developers looking for better data analysis techniques beyond the big data hype.

Power BI is one of the hottest data analysis tools in today's job market. In this episode, Aaron Parry will talk about why Power BI is in such high demand, the things companies are hiring Power BI experts for, and where you should focus if you want to add Power BI to your bag of tricks.  You'll leave the show with a deeper understanding of why Power BI is so valuable, what it can do, and where you should focus if you want to build Power BI skills that will advance your career. What You'll Learn: What makes Power BI such a valuable skill set for so many roles Some of the most valuable ways you can use Power BI on the job Where you should focus if you want to build job-ready Power BI skills   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   About our guests: Aaron is a professional analytics consultant and Microsoft Power BI expert, with 10+ years working in business intelligence and marketing analytics. He's an instructor, coach and mentor for aspiring analysts, and has deep experience helping companies develop and implement full-stack BI solutions. Follow Aaron on LinkedIn

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This book, "Power Apps Tips, Tricks, and Best Practices," is your guide to mastering the development of robust Power Apps solutions. Through its structured and practical approach, you'll gain insights into planning, building, and maintaining applications using the Power Platform. Master advanced techniques, integrate with other systems, and ensure governance and security for professional-grade outcomes. What this Book will help me do Create scalable and maintainable applications using Power Apps. Master the setup and integration of Power Apps with tools like Power Automate and Power BI. Apply governance and security measures effectively to your Power Apps solutions. Leverage advanced topics such as data connections, Copilot Studio, and error handling. Optimize application interfaces through best practices using filtering, formatting, and formulas. Author(s) Andrea Pinillos and Tim Weinzapfel, experienced practitioners in Power Platform technologies, bring their expertise to this book. With years of professional experience in design and implementation, they use their hands-on knowledge to explain complex topics simply and clearly. Their passion for teaching is evident in their step-by-step approach to help you build Power Apps effectively. Who is it for? This book is designed for business analysts, IT professionals, and developers aiming to improve their app development skills. It accommodates both technical and non-technical users with an interest in Power Apps and the Power Platform. Readers should have a basic understanding of Microsoft 365 and Power Apps, although advanced topics are explained for beginners to comprehend easily.

Every organization today is exploring generative AI to drive value and push their business forward. But a common pitfall is that AI strategies often don’t align with business objectives, leading companies to chase flashy tools rather than focusing on what truly matters. How can you avoid these traps and ensure your AI efforts are not only innovative but also aligned with real business value?  Leon Gordon, is a leader in data analytics and AI. A current Microsoft Data Platform MVP based in the UK, founder of Onyx Data. During the last decade, he has helped organizations improve their business performance, use data more intelligently, and understand the implications of new technologies such as artificial intelligence and big data. Leon is an Executive Contributor to Brainz Magazine, a Thought Leader in Data Science for the Global AI Hub, chair for the Microsoft Power BI – UK community group and the DataDNA data visualization community as well as an international speaker and advisor. In the episode, Adel and Leon explore aligning AI with business strategy, building AI use-cases, enterprise AI-agents, AI and data governance, data-driven decision making, key skills for cross-functional teams, AI for automation and augmentation, privacy and AI and much more.  Links Mentioned in the Show: Onyx DataConnect with LeonLeon’s Linkedin Course - How to Build and Execute a Successful Data StrategySkill Track: AI Business FundamentalsRelated Episode: Generative AI in the Enterprise with Steve Holden, Senior Vice President and Head of Single-Family Analytics at Fannie MaeRewatch sessions from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

In today's data-centric business landscape, robust governance, comprehensive auditing, and resilient disaster recovery are paramount for ensuring data integrity, availability, and compliance. This session will explore best practices and advanced strategies for managing and securing your Power BI and Microsoft Fabric environments. Discover how to mitigate risks, optimize operational efficiency, and derive maximum value from your data assets.

The sheer volume of business data has reached an all-time high. Using visualizations to transform this data into useful and understandable information can facilitate better decision-making. This practical book shows data analysts as well as professionals in finance, sales, and marketing how to quickly create visualizations and build savvy dashboards. Alex Kolokolov from Data2Speak and Maxim Zelensky from Intelligent Business explain in simple and clear language how to create brilliant charts with Microsoft Power BI and follow best practices for corporate reporting. No technical background is required. Step-by-step guides help you set up any chart in a few clicks and avoid common mistakes. Also, experienced data analysts will find tips and tricks on how to enrich their reports with advanced visuals. This book helps you understand: The basic rules for classic charts that are used in 90% of business reports Exceptions to general rules based on real business cases Best practices for dashboard design How to properly set up interactions How to prepare data for advanced visuals How to avoid pitfalls with eye-catching charts

This comprehensive guide will teach you how to build an income statement semantic model, also known as the profit and loss (P&L) statement. Author Chris Barber— a business intelligence (BI) consultant, Microsoft MVP, and chartered accountant (ACMA, CGMA)—helps you master everything from designing conceptual models to building semantic models based on these designs. You will learn how to build a re-usable solution based on the trial balance and how to expand upon this to build enterprise-grade solutions. If you want to leverage the Microsoft BI platform to understand profit within your organization, this is the resource you need. What You Will Learn Modeling and the income statement: Learn what modelling the income statement entails, why it is important, and how income statements are constructed Calculating account balances: Learn how to optimally calculate account balances using a Star Schema Producing external income statement semantic models: Learn how to produce external income statement semantic models as they enable income statements to be analyzed from a range of perspectives and can be explored to reveal the underlying accounts and journal entries Producing internal income statement semantic models: Learn how to create multiple income statement layouts and further contextualize financial information by including percentages and non-financial information, and learn about the various security and self-service considerations Who This Book Is For Technical users (solution architects, Microsoft Fabric developers, Power BI developers) who require a comprehensive methodology for income statement semantic models because of the modeling complexities and knowledge needed of the accounting process; and finance (management accountants) who have hit the limits of Excel and have started using Power BI, but are unsure how income statement semantic models are built

Microsoft Power BI Performance Best Practices is your comprehensive guide to designing, optimizing, and scaling Power BI solutions. By understanding data modeling, DAX formulation, and report design, you will be able to enhance the efficiency and performance of your Power BI systems, ensuring that they meet the demands of modern data-driven decision-making. What this Book will help me do Understand and apply techniques for high-efficient data modeling to enhance Power BI performance and manage large datasets. Identify and resolve performance bottlenecks in Power BI reports and dashboards using tools like DAX Studio and VertiPaq Analyzer. Implement governance and monitoring strategies for Power BI performance to ensure robust and scalable systems. Gain expertise in leveraging Power BI Premium and Azure for handling larger scale data and integrations. Adopt best practices for designing, implementing row-level security, and optimizing queries for efficient operations. Author(s) Thomas LeBlanc and Bhavik Merchant are experienced professionals in the field of Business Intelligence and Power BI. Thomas brings over 30 years of IT expertise as a Business Intelligence Architect, ensuring practical and effective solutions for BI challenges. Bhavik is a recognized expert in enterprise-grade Power BI implementation. Together, they share actionable insights and strategies to make Power BI solutions advanced and highly performant. Who is it for? This book is ideal for data analysts, BI developers, and data professionals seeking to elevate their Power BI implementations. If you are proficient with the essentials of Power BI and aim to excel in optimizing its performance and scalability, this book will guide you to achieve those goals efficiently and effectively.

Discover how to harness the full potential of Microsoft Power BI in "Microsoft Power BI Cookbook". Through its recipe-based structure, this book offers step-by-step guidance on mastering data integration, crafting impactful visualizations, and utilizing Power BI's latest features like Hybrid tables and enhanced scorecards. This edition equips you with the skills to transform raw data into actionable insights for your organization. What this Book will help me do Turn business data into actionable insights by utilizing Microsoft Data Fabric effectively. Create engaging and clear visualizations through Hybrid tables and advanced reporting techniques. Gain competence in managing real-time data accuracy and implementing dynamic analytics in Power BI. Ensure robust data compliance and governance integrated seamlessly into business reporting workflows. Leverage cutting-edge Power BI features to prepare for emerging trends in data intelligence. Author(s) Greg Deckler and None Powell, both esteemed professionals in the Power BI and data analytics domain, co-author this comprehensive guide. With decades of experience, they bring vast knowledge and practical skills to this work, presenting it in a structured and approachable manner. Both are dedicated to empowering learners of all levels to excel with Power BI. Who is it for? This book is ideal for professionals like data analysts, business intelligence developers, and IT specialists focused on reporting. It suits readers with a basic familiarity with Power BI, looking to deepen their understanding. If you aim to stay current with Power BI's most modern practices and features, this book will help you achieve that. Additionally, it supports those aiming to enhance business decision-making through better visualizations and advanced analysis.

Summary

Data lakehouse architectures have been gaining significant adoption. To accelerate adoption in the enterprise Microsoft has created the Fabric platform, based on their OneLake architecture. In this episode Dipti Borkar shares her experiences working on the product team at Fabric and explains the various use cases for the Fabric service.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. 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. Your host is Tobias Macey and today I'm interviewing Dipti Borkar about her work on Microsoft Fabric and performing analytics on data withou

Interview

Introduction How did you get involved in the area of data management? Can you describe what Microsoft Fabric is and the story behind it? Data lakes in various forms have been gaining significant popularity as a unified interface to an organization's analytics. What are the motivating factors that you see for that trend? Microsoft has been investing heavily in open source in recent years, and the Fabric platform relies on several open components. What are the benefits of layering on top of existing technologies rather than building a fully custom solution?

What are the elements of Fabric that were engineered specifically for the service? What are the most interesting/complicated integration challenges?

How has your prior experience with Ahana and Presto informed your current work at Microsoft? AI plays a substantial role in the product. What are the benefits of embedding Copilot into the data engine?

What are the challenges in terms of safety and reliability?

What are the most interesting, innovative, or unexpected ways that you have seen the Fabric platform used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on data lakes generally, and Fabric specifically? When is Fabric the wrong choice? What do you have planned for the future of data lake analytics?

Contact Info

LinkedIn

Parting 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 Machine Learning Podcast helps you go from idea to production with machine learning. 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

Microsoft Fabric Ahana episode DB2 Distributed Spark Presto Azure Data MAD Landscape

Podcast Episode ML Podcast Episode

Tableau dbt Medallion Architecture Microsoft Onelake ORC Parquet Avro Delta Lake Iceberg

Podcast Episode

Hudi

Podcast Episode

Hadoop PowerBI

Podcast Episode

Velox Gluten Apache XTable GraphQL Formula 1 McLaren

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Sponsored By: Starburst:

This episode is brought to you by Starburst - an end-to-end data lakehouse platform for data engineers who are battling to build and scale high quality data pipelines on the data lake. Powered by T

Data modeling is the single most overlooked feature in Power BI Desktop, yet it's what sets Power BI apart from other tools on the market. This practical book serves as your fast-forward button for data modeling with Power BI, Analysis Services tabular, and SQL databases. It serves as a starting point for data modeling, as well as a handy refresher. Author Markus Ehrenmueller-Jensen, founder of Savory Data, shows you the basic concepts of Power BI's semantic model with hands-on examples in DAX, Power Query, and T-SQL. If you're looking to build a data warehouse layer, chapters with T-SQL examples will get you started. You'll begin with simple steps and gradually solve more complex problems. This book shows you how to: Normalize and denormalize with DAX, Power Query, and T-SQL Apply best practices for calculations, flags and indicators, time and date, role-playing dimensions and slowly changing dimensions Solve challenges such as binning, budget, localized models, composite models, and key value with DAX, Power Query, and T-SQL Discover and tackle performance issues by applying solutions in DAX, Power Query, and T-SQL Work with tables, relations, set operations, normal forms, dimensional modeling, and ETL

Reynold Xin, Co-founder and Chief Architect at Databricks, presented during Data + AI Summit 2024 on Databricks SQL and its advancements and how to drive performance improvements with the Databricks Data Intelligence Platform.

Speakers: Reynold Xin, Co-founder and Chief Architect, Databricks Pearl Ubaru, Technical Product Engineer, Databricks

Main Points and Key Takeaways (AI-generated summary)

Introduction of Databricks SQL: - Databricks SQL was announced four years ago and has become the fastest-growing product in Databricks history. - Over 7,000 customers, including Shell, AT&T, and Adobe, use Databricks SQL for data warehousing.

Evolution from Data Warehouses to Lakehouses: - Traditional data architectures involved separate data warehouses (for business intelligence) and data lakes (for machine learning and AI). - The lakehouse concept combines the best aspects of data warehouses and data lakes into a single package, addressing issues of governance, storage formats, and data silos.

Technological Foundations: - To support the lakehouse, Databricks developed Delta Lake (storage layer) and Unity Catalog (governance layer). - Over time, lakehouses have been recognized as the future of data architecture.

Core Data Warehousing Capabilities: - Databricks SQL has evolved to support essential data warehousing functionalities like full SQL support, materialized views, and role-based access control. - Integration with major BI tools like Tableau, Power BI, and Looker is available out-of-the-box, reducing migration costs.

Price Performance: - Databricks SQL offers significant improvements in price performance, which is crucial given the high costs associated with data warehouses. - Databricks SQL scales more efficiently compared to traditional data warehouses, which struggle with larger data sets.

Incorporation of AI Systems: - Databricks has integrated AI systems at every layer of their engine, improving performance significantly. - AI systems automate data clustering, query optimization, and predictive indexing, enhancing efficiency and speed.

Benchmarks and Performance Improvements: - Databricks SQL has seen dramatic improvements, with some benchmarks showing a 60% increase in speed compared to 2022. - Real-world benchmarks indicate that Databricks SQL can handle high concurrency loads with consistent low latency.

User Experience Enhancements: - Significant efforts have been made to improve the user experience, making Databricks SQL more accessible to analysts and business users, not just data scientists and engineers. - New features include visual data lineage, simplified error messages, and AI-driven recommendations for error fixes.

AI and SQL Integration: - Databricks SQL now supports AI functions and vector searches, allowing users to perform advanced analysis and query optimizations with ease. - The platform enables seamless integration with AI models, which can be published and accessed through the Unity Catalog.

Conclusion: - Databricks SQL has transformed into a comprehensive data warehousing solution that is powerful, cost-effective, and user-friendly. - The lakehouse approach is presented as a superior alternative to traditional data warehouses, offering better performance and lower costs.

To build a successful career in data, you need to focus on the right skills. You need to focus on skills that will open doors, make you job-ready, help you attract employers, and put you on the right track toward growing your career. But where should you start? In this episode, Matt Mike will give his thoughts on the most important skills you'll need in your career, and will give you a roadmap for adding them to your tool kit and putting them on display to attract the right kind of attention. You'll leave this session with a solid understanding of which skills you should focus on first, how to tackle them, and with a plan to take action and make progress today. What You'll Learn: Which technical tools you should focus on to build a great data career Why soft skills can be just as important and which ones matter the most How you can build these skills, and put them on display to accelerate your career   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   About our guest: Matt Mike is a multi-passionate Data Analyst and BI Developer who fancies Power BI.

He has been a data analyst for 2 years now and also transitioned into the data field from a non-technical background. This has given him a unique understanding of what it takes to be an analyst from the ground up. Check out Matt's YouTube channel for more! : https://www.youtube.com/@MattMike     Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

Install data analytics into your brain with this comprehensive introduction Data Analytics & Visualization All-in-One For Dummies collects the essential information on mining, organizing, and communicating data, all in one place. Clocking in at around 850 pages, this tome of a reference delivers eight books in one, so you can build a solid foundation of knowledge in data wrangling. Data analytics professionals are highly sought after these days, and this book will put you on the path to becoming one. You’ll learn all about sources of data like data lakes, and you’ll discover how to extract data using tools like Microsoft Power BI, organize the data in Microsoft Excel, and visually present the data in a way that makes sense using a Tableau. You’ll even get an intro to the Python, R, and SQL coding needed to take your data skills to a new level. With this Dummies guide, you’ll be well on your way to becoming a priceless data jockey. Mine data from data sources Organize and analyze data Use data to tell a story with Tableau Expand your know-how with Python and R New and novice data analysts will love this All-in-One reference on how to make sense of data. Get ready to watch as your career in data takes off.

The Complete Power BI Interview Guide is your companion to mastering Power BI roles and acing data analyst interviews. With hands-on skills, expert tips, and targeted preparation strategies, this resource equips you to excel in interviews and certifications while navigating the competitive job market. What this Book will help me do Create a powerful professional brand to optimize your resume and online presence. Master essential Power BI skills including data modeling, DAX programming, and visualization. Prepare effectively for interviews with industry-relevant questions, answers, and insights. Gain an edge in the market by understanding hiring procedures and negotiation tactics. Develop comprehensive analytics solutions exemplified with real-world case studies. Author(s) Sandielly Ortega Polanco, Gogula Aryalingam, and Abu Bakar Nisar Alvi bring years of collective experience in data analytics, Power BI, and career mentorship. Their insights are drawn from extensive professional practice and their passion for empowering future data analysts. Together, they provide an approachable and practical guide to securing roles in the competitive landscape of data analytics. Who is it for? This book is ideal for aspiring data analysts, business intelligence developers, or those shifting into Power BI roles who wish to enhance their knowledge and refine their strategies for interview success. It speaks to both newcomers to the field and seasoned professionals aiming to elevate their expertise.

Advance your Power BI skills by adding AI to your repertoire at a practice level. With this practical book, business-oriented software engineers and developers will learn the terminologies, practices, and strategy necessary to successfully incorporate AI into your business intelligence estate. Jen Stirrup, CEO of AI and BI leadership consultancy Data Relish, and Thomas Weinandy, research economist at Upside, show you how to use data already available to your organization. Springboarding from the skills that you already possess, this book adds AI to your organization's technical capability and expertise with Microsoft Power BI. By using your conceptual knowledge of BI, you'll learn how to choose the right model for your AI work and identify its value and validity. Use Power BI to build a good data model for AI Demystify the AI terminology that you need to know Identify AI project roles, responsibilities, and teams for AI Use AI models, including supervised machine learning techniques Develop and train models in Azure ML for consumption in Power BI Improve your business AI maturity level with Power BI Use the AI feedback loop to help you get started with the next project

In "Extending Power BI with Python and R," you'll learn how to enhance your Power BI reports and analyses by leveraging the advanced analytical capabilities of Python and R. From working with large datasets to creating sophisticated visuals, this book provides practical instructions on powerful techniques that unlock new possibilities in Power BI. What this Book will help me do Configure and optimize Python and R integration in Power BI for enhanced performance. Implement advanced data transformation techniques to overcome Power BI limitations. Develop advanced visualizations using the Grammar of Graphics in Python and R. Analyze data leveraging powerful Python and R algorithms, including machine learning models. Secure your Power BI data with anonymization and pseudonymization techniques. Author(s) None Zavarella is a data analytics expert with years of practical experience in business intelligence and data analytics. With a passion for enhancing data tools with programming languages like Python and R, they bring practical knowledge and technical acumen to this comprehensive resource. They aim to make complex concepts approachable to their readers. Who is it for? This book is aimed at professionals such as business analysts, business intelligence specialists, and data scientists who leverage Power BI for their data solutions. Readers should have a working knowledge of Power BI basics and a desire to extend its capabilities. A familiarity with Python and R programming basics is also beneficial for following the advanced techniques presented.

Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that should flow as smoothly as your morning coffee (but don't), where industry insights meet laid-back banter. Whether you're a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let's get into the heart of data, unplugged style! In this episode #42, titled "Unraveling the Fabric of Data: Microsoft's Ecosystem and Beyond," we're joined once again by the tech maestro and newly minted Microsoft MVP, Sam Debruyn. Sam brings to the table a bevy of updates from his recent accolades to the intricacies of Microsoft's data platforms and the world of SQL.

Biz Buzz: From Reddit's IPO to the performance versus utility debate in database selection, we dissect the big moves shaking up the business side of tech. Read about Reddit's IPO.Microsoft's Fabric Unraveled: Get the lowdown on Microsoft's Fabric, the one-stop AI platform, as Sam Debruyn gives us a deep dive into its capabilities and integration with Azure Databricks and Power BI. Discover more about Fabric and dive into Sam's blog.dbt Developments: Sam talks dbt and the exciting new SQL tool for data pipeline building with upcoming unit testing capabilities.Polaris Project: Delving into Microsoft's internal storage projects, including insights on Polaris and its integration with Synapse SQL. Read the paper here.AI Advances: From the release of Grok-1 and Apple's MM1 AI model to GPT-4's trillion parameters, we discuss the leaps in artificial intelligence.Stability in Motion: After OpenAI's Sora, we look at Stability AI's new venture into motion with Stable Video. Check out Stable Video.Benchmarking Debate: A critical look at performance benchmarks in database selection and the ongoing search for the 'best' database. Contemplate benchmarking perspectives.Versioning Philosophy: Hot takes on semantic versioning and what stability really means in software development. Dive into Semantic Versioning.

Delve into the powerful world of data cleaning with Microsoft Power BI in this detailed guide. You'll learn how to connect, transform, and optimize data from various sources, setting a strong foundation for insightful data-driven decisions. Equip yourself with the skills to master data quality, leverage DAX and Power Query, and produce actionable insights with improved efficiency. What this Book will help me do Master connecting to various data sources and importing data effectively into Power BI. Learn to use the Query Editor to clean and transform data efficiently. Understand how to use the M language to perform advanced data transformations. Gain expertise in creating optimized data models and handling relationships within Power BI. Explore insights-driven exploratory data analysis using Power BI's powerful tools. Author(s) None Frazer is an experienced data professional with a deep knowledge of business intelligence tools and analytics processes. With a strong background in data science and years of hands-on experience using Power BI, Frazer brings practical advice to help users improve their data preparation and analysis skills. Known for creating resources that are both comprehensive and approachable, Frazer is dedicated to empowering readers in their data journey. Who is it for? This book is ideal for data analysts, business intelligence professionals, and business analysts who work regularly with data. If you are someone with a basic understanding of BI tools and concepts looking to deepen their skills, especially in Power BI, this book will guide you effectively. It will also help data scientists and other professionals interested in data cleaning to build a robust basis for data quality and analysis. Whether you're addressing common data challenges or seeking to enhance your BI capabilities, this guide is tailored to accommodate your needs.

Learn and explore the capabilities of Microsoft Fabric, the latest evolution in cloud analytics suites. This book will help you understand how users can leverage Microsoft Office equivalent experience for performing data management and advanced analytics activity. The book starts with an overview of the analytics evolution from on premises to cloud infrastructure as a service (IaaS), platform as a service (PaaS), and now software as a service (SaaS version) and provides an introduction to Microsoft Fabric. You will learn how to provision Microsoft Fabric in your tenant along with the key capabilities of SaaS analytics products and the advantage of using Fabric in the enterprise analytics platform. OneLake and Lakehouse for data engineering is discussed as well as OneLake for data science. Author Ghosh teaches you about data warehouse offerings inside Microsoft Fabric and the new data integration experience which brings Azure Data Factory and Power Query Editor of Power BI together in a single platform. Also demonstrated is Real-Time Analytics in Fabric, including capabilities such as Kusto query and database. You will understand how the new event stream feature integrates with OneLake and other computations. You also will know how to configure the real-time alert capability in a zero code manner and go through the Power BI experience in the Fabric workspace. Fabric pricing and its licensing is also covered. After reading this book, you will understand the capabilities of Microsoft Fabric and its Integration with current and upcoming Azure OpenAI capabilities. What You Will Learn Build OneLake for all data like OneDrive for Microsoft Office Leverage shortcuts for cross-cloud data virtualization in Azure and AWS Understand upcoming OpenAI integration Discover new event streaming and Kusto query inside Fabric real-time analytics Utilize seamless tooling for machine learning and data science Who This Book Is For Citizen users and experts in the data engineering and data science fields, along with chief AI officers