Master Microsoft Fabric from basics to advanced architectures with expert guidance to unify, secure, and scale analytics on real-world data platforms Key Features Build a complete data analytics platform with Microsoft Fabric Apply proven architectures, governance, and security strategies Gain real-world insights from five seasoned data experts Purchase of the print or Kindle book includes a free PDF eBook Book Description Microsoft Fabric is reshaping how organizations manage, analyze, and act on data by unifying ingestion, storage, transformation, analytics, AI, and visualization in a single platform. The Definitive Guide to Microsoft Fabric takes you from your very first workspace to building a secure, scalable, and future-proof analytics environment. You’ll learn how to unify data in OneLake, design data meshes, transform and model data, implement real-time analytics, and integrate AI capabilities. The book also covers advanced topics, such as governance, security, cost optimization, and team collaboration using DevOps and DataOps principles. Drawing on the real-world expertise of five seasoned professionals who have built and advised on platforms for startups, SMEs, and Europe’s largest enterprises, this book blends strategic insight with practical guidance. By the end of this book, you’ll have gained the knowledge and skills to design, deploy, and operate a Microsoft Fabric platform that delivers sustainable business value. What you will learn Understand Microsoft Fabric architecture and concepts Unify data storage and data governance with OneLake Ingest and transform data using multiple Fabric tools Implement real-time analytics and event processing Design effective semantic models and reports Integrate AI and machine learning into data workflows Apply governance, security, and compliance controls Optimize performance and costs at scale Who this book is for This book is for data engineers, analytics engineers, architects, and data analysts moving into platform design roles. It’s also valuable for technical leaders seeking to unify analytics in their organizations. You’ll need only a basic grasp of databases, SQL, and Python.

How can you maximize data collaboration across your organization without having to build integrations between individual applications, systems, and other data sources? Data collaboration architectures that don't depend on integrations aren't a new idea, but they've assumed greater urgency as organizations increasingly struggle to manage the ever-growing numbers of data sources that exist inside their IT estates. In this report, Cinchy cofounders Dan DeMers and Karanjot Jaswal show CIOs, CTOs, CDOs, and other IT leaders how to rethink their organization's approach to data architectures, data management, and data governance. You'll learn about different approaches to creating data platforms that liberate and autonomize data, enable agile data management, apply consistent data access controls, and maximize visibility without requiring application-specific integrations. With this report, you'll discover: Why data integration is often handled piecemeal—combining one app with another rather than integrating all apps together How data collaboration platforms enable data sharing across all apps, systems, and sources without application-specific integrations Four major platforms you can use to make data available to all applications and services: Cinchy, K2View, Microsoft Dataverse, and The Modern Data Company Principles and practices for deploying the data collaboration platform of your choice Dan DeMers is the CEO and cofounder of Cinchy. Karanjot Jaswal is cofounder and CTO of Cinchy.

The Data Mirage: Why Companies Fail to Actually Use Their Data is a business book for executives and leaders who want to unlock more insights from their data and make better decisions. The importance of data doesn’t need an introduction or a fancy pitch deck. Data plays a critical role in helping companies to better understand their users, beat out their competitors, and breakthrough their growth targets. However, despite significant investments in their data, most organizations struggle to get much value from it. According to Forrester, only 38% of senior executives and decision-makers “have a high level of confidence in their customer insights and only 33% trust the analytics they generate from their business operations.” This reflects the real world that I have experienced. In this book, I will help readers formulate an analytics strategy that works in the real world, show them how to think about KPIs and help them tackle the problems they are bound to come across as they try to use data to make better decisions.

Gain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making. Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data producers, data consumers, and the rest of the organization through the adoption of lean thinking and agile software development principles. This book helps you to improve the speed and accuracy of analytical application development through data management and DevOps practices that securely expand data access, and rapidly increase the number of reproducible data products through automation, testing, and integration. The book also shows how to collect feedback and monitor performance to manage and continuously improve your processes and output. What You Will Learn Develop a data strategy for your organization to help it reach its long-term goals Recognize and eliminate barriers to delivering data to users at scale Work on the right things for the right stakeholders through agile collaboration Create trust in data via rigorous testing and effective data management Build a culture of learning and continuous improvement through monitoring deployments and measuring outcomes Create cross-functional self-organizing teams focused on goals not reporting lines Build robust, trustworthy, data pipelines in support of AI, machine learning, and other analytical data products Who This Book Is For Data science and advanced analytics experts, CIOs, CDOs (chief data officers), chief analytics officers, business analysts, business team leaders, and IT professionals (data engineers, developers, architects, and DBAs) supporting data teams who want to dramatically increase the value their organization derives from data. The book is ideal for data professionals who want to overcome challenges of long delivery time, poor data quality, high maintenance costs, and scaling difficulties in getting data science output and machine learning into customer-facing production.

The data-driven revolution is finally hitting the media and entertainment industry. For decades, broadcast television and print media relied on traditional delivery channels for solvency and growth, but those channels fragmented as cable, streaming, and digital devices stole the show. In this ebook, you’ll learn about the trends, challenges, and opportunities facing players in this industry as they tackle big data, advanced analytics, and DataOps. You’ll explore best practices and lessons learned from three real-world media companies—Sling TV, Turner Broadcasting, and Comcast—as they proceed on their data-driven journeys. Along the way, authors Ashish Thusoo and Joydeep Sen Sarma explain how DataOps breaks down silos and connects everyone who handles data, including engineers, data scientists, analysts, and business users. Big-data-as-a-service provider Qubole provides a five-step maturity model that outlines the phases that a company typically goes through when it first encounters big data. Case studies include: Sling TV: this live streaming content platform delivers live TV and on-demand entertainment instantly to a variety of smart televisions, tablets, game consoles, computers, smartphones, and streaming devices Turner Broadcasting System: this Time Warner division recently created the Turner Data Cloud to support direct-to-consumer services, including FilmStruck, Boom (for kids), and NBA League Pass Comcast: the largest broadcasting and cable TV company is building a single integrated big data platform to deliver internet, TV, and voice to more than 28 million customers

Many companies are busy collecting massive amounts of data, but few are taking advantage of this treasure horde to build a truly data insights-driven organization. To do so, the data team must democratize both data and the insights in a way that provides real-time access to all employees in the organization. This report explores DataOps, the process, culture, tools, and people required to scale big data pervasively across the enterprise. Just as DevOps has enabled organizations to improve coordination between developers and the operations team, DataOps closely connects everyone who handles data, including engineers, data scientists, analysts, and business users. Democratizing data with this approach requires removing barriers typical of siloed data, teams, and systems. In this report, Apache Hive creators Ashish Thusoo and Joydeep Sen Sarma examine the characteristics of a data-driven organization that supports a self-service model. Explore related topics such as data lakes, metadata, cloud architecture, and data-infrastructure-as-a-service Examine conclusions from a survey of more than 400 senior executives whose companies are in various stages of data maturity Learn how data pioneers at Facebook, Uber, LinkedIn, Twitter, and eBay created data-driven cultures and self-service data infrastructures for their organizations

How well prepared is your organization to innovate, using data science? In this report, two leading data scientists at the consulting firm Booz Allen Hamilton describe ten characteristics of a mature data science capability. After spending years helping clients such as the US government and commercial organizations worldwide build innovative data science capabilities, Peter Guerra and Dr. Kirk Borne identified these characteristics to help you measure your company’s competence in this area. This report provides a detailed discussion of each of the 10 signs of data science maturity, which—among many other things—encourage you to: Give members of your organization access to all your available data Use Agile and leverage "DataOps"—DevOps for data product development Help your data science team sharpen its skills through open or internal competitions Personify data science as a way of doing things, and not a thing to do