Data Modeling & Design 1

With the surge in big data and AI, organizations can rapidly create data products. However, the effectiveness of their analytics and machine learning models depends on the data's quality. Delta Lake……続きを見る

Quickly detect, troubleshoot, and prevent a wide range of data issues through data observability, a set of best practices that enables data teams to gain greater visibility of data and its usage. If……続きを見る

The world's businesses ingest a combined 2.5 quintillion bytes of data every day. But how much of this vast amount of data--used to build products, power AI systems, and drive business decisions--is……続きを見る

You want increased customer satisfaction, faster development cycles, and less wasted work. Domain-driven design (DDD) combined with functional programming is the innovative combo that will get you t……続きを見る

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. With ……続きを見る

If you want to attract and retain users in the booming mobile services market, you need a quick-loading app that won’t churn through their data plans. The key is to compress multimedia and other dat……続きを見る

Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition,……続きを見る

In this "important and comprehensive" guide to statistical thinking (New Yorker), discover how data literacy is changing the world and gives you a better understanding of life’s biggest problems. St……続きを見る

Everything we need to know about metadata, the usually invisible infrastructure for information with which we interact every day. When “metadata” became breaking news, appearing in stories about sur……続きを見る

As AI, deep learning and data science have become the hot topics in I.T. industry, learning some powerful data manipulating tools would be essential for your technical skill building, career develop……続きを見る

Leverage the power of Tableau 2019.1's new features to create impactful data visualization Key Features Get up and running with the newly released features of Tableau 2019.1 Create enterprise-grade……続きを見る

Want to build APIs like Facebook? Since Facebook's framework for building APIs, GraphQL, has become publicly available, this ambition seems to be within reach for many companies. And that is great. ……続きを見る

This O’Reilly cookbook provides more than 150 recipes to help scientists, engineers, programmers, and data analysts generate high-quality graphs quicklyーwithout having to comb through all the detai……続きを見る

Implement decentralized blockchain applications to build scalable Dapps Key Features Understand the blockchain ecosystem and its terminologies Implement smart contracts, wallets, and consensus prot……続きを見る

Get more from your data by creating practical machine learning systems with Python Key Features Develop your own Python-based machine learning system Discover how Python offers multiple algorithms ……続きを見る

Deep learning doesn’t have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a ba……続きを見る

Data analysis expressions (DAX) is the formula language of Power Pivot. Learning the DAX language is key to empower Excel users so they can take advantage of these new Business Intelligence (BI) cap……続きを見る

Leverage Splunk's operational intelligence capabilities to unlock new hidden business insights and drive success About This Book • Tackle any problems related to searching and analyzing your data wi……続きを見る

This volume celebrates the career of Prof. Ricardo de Almeida Falbo on the occasion of his formal retirement. The volume includes reflections from collaborators and former students, casting light on……続きを見る

Academic book turns into a popular book read. Artificial, linkage and conversion analysis. One of the three is demonstrated to be the most important. 30 day tracking visitor period versus 24 hour pe……続きを見る

The widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time finan……続きを見る

Whether you have some experience with Tableau software or are just getting started, this manual goes beyond the basics to help you build compelling, interactive data visualization applications. Auth……続きを見る

You're sitting on a pile of interesting data. How do you transform that into money? It's easy to focus on the contents of the data itself, and to succumb to the (rather unimaginative) idea of simply……続きを見る

In the early days of the 20th century, department store magnate JohnWanamaker famously said, "I know that half of my advertising doesn'twork. The problem is that I don't know which half." That remai……続きを見る

En este libro se introduce el concepto de Software Gestor Documental, se describen las principales características y se ofrecen ejemplos de la configuración y parametrización que puede realizarse pa……続きを見る

This report examines the important shifts in data products. Drawing from diverse examples, including iTunes, Google's self-driving car, and patient monitoring, author Mike Loukides explores the "dis……続きを見る

We've all heard it: according to Hal Varian, statistics is the next sexy job. Five years ago, in What is Web 2.0, Tim O'Reilly said that "data is the next Intel Inside." But what does that statement……続きを見る

This collection represents the full spectrum of data-related content we’ve published on O’Reilly Radar over the last year. Mike Loukides kicked things off in June 2010 with “What is data science?” a……続きを見る

As data science evolves to become a business necessity, the importance of assembling a strong and innovative data teams grows. In this in-depth report, data scientist DJ Patil explains the skills, p……続きを見る

Despite the excitement around "data science," "big data," and "analytics," the ambiguity of these terms has led to poor communication between data scientists and organizations seeking their help. In……続きを見る