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The ‘Cloud’, hailed as a new digital commons, a utopia of collaborative expression and constant connection, actually constitutes a strategy of vitalist post-hegemonic power, which moves to dominate immanently and intensively, organizing our affective political involvements, instituting new modes of enclosure, and, crucially, colonizing the future through a new temporality of control. The virtual is often claimed as a realm of invention through which capitalism might be cracked, but it is precisely here that power now thrives. Cloud time, in service of security and profit, assumes all is knowable. We bear witness to the collapse of both past and future virtuals into a present dedicated to the exploitation of the spectres of both. ,
The clouds will put on a pageant for you with their ever-changing shapes and sizes. This book reveals some fascinating science behind these beautiful displays.
Despite many uncertainties in cloud computing, one truth is evident: costs will always tend to go up unless you’re actively engaged in the process. Whether you’re new to managing cloud spend or a seasoned pro, this book will clarify the often misunderstood workings of cloud billing fundamentals and provide expert strategies on creating a culture of cloud cost management in your organization. Drawing on real-world examples of successes and failures of large-scale cloud spenders, this book outlines a road map for building a culture of FinOps in your organization. Beginning with the fundamental concepts required to understand cloud billing concepts, you’ll learn how to enable an efficient and effective FinOps machine. Learn how the cloud works when it comes to financial management Set up a FinOps team and build a framework for making spend efficiency a priority Examine the anatomy of a cloud bill and learn how to manage it Get operational recipes for maximizing cloud efficiency Understand how to motivate engineering teams to take cost-saving actions Explore the FinOps lifecycle: Inform, Optimize, and Operate Learn the DNA of a highly functional cloud FinOps culture
This fascinating book will stay with children every time they gaze up at the night sky. Through vivid pictures and engaging explanations, children will learn about many of the Moon’s mysteries: what makes it look like a silvery crescent one time and a chalk-white ball a few nights later, why it sometimes appears in the daytime, where it gets its light, and how scientists can predict its shape on your birthday a thousand years from now. Next Time You See the Moon is an ideal way to explain the science behind the shape of the Moon and bring about an evening outing no child—or grown-up—will soon forget. Awaken a sense of wonder in a child with the Next Time You See series from NSTA Kids. The books will inspire elementary-age children to experience the enchantment of everyday phenomena such as sunsets, seashells, fireflies, pill bugs, and more. Free supplementary activities are available on the NSTA website. Especially designed to be experienced with an adult—be it a parent, teacher, or friend—Next Time You See books serve as a reminder that you don’t have to look far to find something remarkable in nature.
#1 INTERNATIONAL BESTSELLER • A timeless, structure-bending classic that explores how actions of individual lives impact the past, present and future—from a postmodern visionary and one of the leading voices in fiction Featuring a new afterword by David Mitchell and a new introduction by Gabrielle Zevin, author of Tomorrow, and Tomorrow, and Tomorrow One of the New York Times’s 100 Best Books of the 21st Century • Shortlisted for the International Booker Prize Cloud Atlas begins in 1850 with Adam Ewing, an American notary voyaging from the Chatham Isles to his home in California. Ewing is befriended by a physician, Dr. Goose, who begins to treat him for a rare species of brain parasite. The novel careens, with dazzling virtuosity, to Belgium in 1931, to the West Coast in the 1970s, to an inglorious present-day England, to a Korean superstate of the near future where neocapitalism has run amok, and, finally, to a postapocalyptic Iron Age Hawaii in the last days of history. But the story doesn’t end even there. The novel boomerangs back through centuries and space, returning by the same route, in reverse, to its starting point. Along the way, David Mitchell reveals how his disparate characters connect, how their fates intertwine, and how their souls drift across time like clouds across the sky. As wild as a video game, as mysterious as a Zen koan, Cloud Atlas is an unforgettable tour de force that, like its incomparable author, has transcended its cult classic status to become a worldwide phenomenon.
"Noah Keller has a pretty normal life until one wild afternoon when his parents pick him up from school and head straight for the airport, telling him on the ride that his name isn't really Noah and he didn't really just turn eleven in March ... As Noah, now 'Jonah Brown,' and his parents head behind the Iron Curtain into East Berlin, the rules and secrets begin to pile up so quickly that he can hardly keep track of the questions bubbling up inside him: who, exactly, is listening--and why?"
Step-by-step guide to different data movement and processing techniques, using Google Cloud Platform Services Key Featuresa- Learn the basic concept of Cloud Computing along with different Cloud service provides with their supported Models (IaaS/PaaS/SaaS)a- Learn the basics of Compute Engine, App Engine, Container Engine, Project and Billing setup in the Google Cloud Platforma- Learn how and when to use Cloud DataFlow, Cloud DataProc and Cloud DataPrep a- Build real-time data pipeline to support real-time analytics using Pub/Sub messaging servicea- Setting up a fully managed GCP Big Data Cluster using Cloud DataProc for running Apache Spark and Apache Hadoop clusters in a simpler, more cost-efficient mannera- Learn how to use Cloud Data Studio for visualizing the data on top of Big Querya- Implement and understand real-world business scenarios for Machine Learning, Data Pipeline EngineeringDescriptionModern businesses are awash with data, making data driven decision-making tasks increasingly complex. As a result, relevant technical expertise and analytical skills are required to do such tasks. This book aims to equip you with enough knowledge of Cloud Computing in conjunction with Google Cloud Data platform to succeed in the role of a Cloud data expert.Current market is trending towards the latest cloud technologies, which is the need of the hour. Google being the pioneer, is dominating this space with the right set of cloud services being offered as part of GCP (Google Cloud Platform). At this juncture, this book will be very vital and will be cover all the services that are being offered by GCP, putting emphasis on Data services.What will you learnBy the end of the book, you will have come across different data services and platforms offered by Google Cloud, and how those services/features can be enabled to serve business needs. You will also see a few case studies to put your knowledge to practice and solve business problems such as building a real-time streaming pipeline engine, Scalable Datawarehouse on Cloud, fully managed Hadoop cluster on Cloud and enabling TensorFlow/Machine Learning API's to support real-life business problems. Remember to practice additional examples to master these techniques. Who this book is forThis book is for professionals as well as graduates who want to build a career in Google Cloud data analytics technologies. One stop shop for those who wish to get an initial to advance understanding of the GCP data platform. The target audience will be data engineers/professionals who are new, as well as those who are acquainted with the tools and techniques related to cloud and data space. a- Individuals who have basic data understanding (i.e. Data and cloud) and have done some work in the field of data analytics, can refer/use this book to master their knowledge/understanding.a- The highlight of this book is that it will start with the basic cloud computing fundamentals and will move on to cover the advance concepts on GCP cloud data analytics and hence can be referred across multiple different levels of audiences. Table of Contents1. GCP Overview and Architecture2. Data Storage in GCP 3. Data Processing in GCP with Pub/Sub and Dataflow 4. Data Processing in GCP with DataPrep and Dataflow5. Big Query and Data Studio6. Machine Learning with GCP7. Sample Use cases and ExamplesAbout the Author Murari Ramuka is a seasoned Data Analytics professional with 12+ years of experience in enabling data analytics platforms using traditional DW/BI and Cloud Technologies (Azure, Google Cloud Platform) to uncover hidden insights and maximize revenue, profitability and ensure efficient operations management. He has worked with several multinational IT giants like Capgemini, Cognizant, Syntel and Icertis.His LinkedIn Profile: https://www.linkedin.com/in/murari-ramuka-98a440a/
Imaginative picture book series by award winning artist Hanako Wakiyama
The conventional wisdom on how technology will change the future is wrong. Mark Mills lays out a radically different and optimistic vision for what’s really coming. The mainstream forecasts fall into three camps. One considers today as the “new normal,” where ordering a ride or food on a smartphone or trading in bitcoins is as good as it’s going to get. Another foresees a dystopian era of widespread, digitally driven job- and business-destruction. A third believes that the only technological revolution that matters will be found with renewable energy and electric cars. But according to Mills, a convergence of technologies will instead drive an economic boom over the coming decade, one that historians will characterize as the “Roaring 2020s.” It will come not from any single big invention, but from the confluence of radical advances in three primary technology domains: microprocessors, materials, and machines. Microprocessors are increasingly embedded in everything. Materials, from which everything is built, are emerging with novel, almost magical capabilities. And machines, which make and move all manner of stuff, are undergoing a complementary transformation. Accelerating and enabling all of this is the Cloud, history’s biggest infrastructure, which is itself based on the building blocks of next-generation microprocessors and artificial intelligence. We’ve seen this pattern before. The technological revolution that drove the great economic expansion of the twentieth century can be traced to a similar confluence, one that was first visible in the 1920s: a new information infrastructure (telephony), new machines (cars and power plants), and new materials (plastics and pharmaceuticals). Single inventions don’t drive great, long-cycle booms. It always takes convergent revolutions in technology’s three core spheres—information, materials, and machines. Over history, that’s only happened a few times. We have wrung much magic from the technologies that fueled the last long boom. But the great convergence now underway will ignite the 2020s. And this time, unlike any previous historical epoch, we have the Cloud amplifying everything. The next long boom starts now.
Data in the genomics field is booming. In just a few years, organizations such as the National Institutes of Health (NIH) will host 50+ petabytes—or over 50 million gigabytes—of genomic data, and they’re turning to cloud infrastructure to make that data available to the research community. How do you adapt analysis tools and protocols to access and analyze that volume of data in the cloud? With this practical book, researchers will learn how to work with genomics algorithms using open source tools including the Genome Analysis Toolkit (GATK), Docker, WDL, and Terra. Geraldine Van der Auwera, longtime custodian of the GATK user community, and Brian O’Connor of the UC Santa Cruz Genomics Institute, guide you through the process. You’ll learn by working with real data and genomics algorithms from the field. This book covers: Essential genomics and computing technology background Basic cloud computing operations Getting started with GATK, plus three major GATK Best Practices pipelines Automating analysis with scripted workflows using WDL and Cromwell Scaling up workflow execution in the cloud, including parallelization and cost optimization Interactive analysis in the cloud using Jupyter notebooks Secure collaboration and computational reproducibility using Terra