Lambda enables users to develop code that executes in response to events - API calls, file uploads, schedules, etc - and upload it without worrying about managing traditional server metrics such as disk space, memory, or CPU usage. With its "per execution" cost model, Lambda can enable organizations to save hundreds or thousands of dollars on computing costs. With in-depth walkthroughs, large screenshots, and complete code samples, the reader is guided through the step-by-step process of creating new functions, responding to infrastructure events, developing API backends, executing code at specified intervals, and much more. Introduction to AWS Computing Evolution of the Computing Workload Lambda Background The Internals The Basics Functions Languages Resource Allocation Getting Set Up Hello World Uploading the Function Working with Events AWS Events Custom Events The Context Object Properties Methods Roles and Permissions Policies Trust Relationships Console Popups Cross Account Access Dependencies and Resources Node Modules OS Dependencies OS Resources OS Commands Logging Searching Logs Testing Your Function Lambda Console Tests Third-Party Testing Libraries Simulating Context Hello S3 Object The Bucket The Role The Code The Event The Trigger Testing When Lambda Isn’t the Answer Host Access Fine-Tuned Configuration Security Long-Running Tasks Where Lambda Excels AWS Event-Driven Tasks Scheduled Events (Cron) Offloading Heavy Processing API Endpoints Infrequently Used Services Real-World Use Cases S3 Image Processing Shutting Down Untagged Instances Triggering CodeDeploy with New S3 Uploads Processing Inbound Email Enforcing Security Policies Detecting Expiring Certificates Utilizing the AWS API Execution Environment The Code Pipeline Cold vs. Hot Execution What is Saved in Memory Scaling and Container Reuse From Development to Deployment Application Design Development Patterns Testing Deployment Monitoring Versioning and Aliasing Costs Short Executions Long-Running Processes High-Memory Applications Free Tier Calculating Pricing CloudFormation Reusable Template with Minimum Permissions Cross Account Access CloudWatch Alerts AWS API Gateway API Gateway Event Creating the Lambda Function Creating a New API, Resource, and Method Initial Configuration Mapping Templates Adding a Query String Using HTTP Request Information Within Lambda Deploying the API Additional Use Cases Lambda Competitors Iron.io StackHut WebTask.io Existing Cloud Providers The Future of Lambda More Resources Conclusion

    Big data is on the tip of everyone's tongue. Everyone understands its power and importance, but many fail to grasp the actionable steps and resources required to utilise it effectively. This book fills the knowledge gap by showing how major companies are using big data every day, from an up-close, on-the-ground perspective. From technology, media and retail, to sport teams, government agencies and financial institutions, learn the actual strategies and processes being used to learn about customers, improve manufacturing, spur innovation, improve safety and so much more. Organised for easy dip-in navigation, each chapter follows the same structure to give you the information you need quickly. For each company profiled, learn what data was used, what problem it solved and the processes put it place to make it practical, as well as the technical details, challenges and lessons learned from each unique scenario. Learn how predictive analytics helps Amazon, Target, John Deere and Apple understand their customers Discover how big data is behind the success of Walmart, LinkedIn, Microsoft and more Learn how big data is changing medicine, law enforcement, hospitality, fashion, science and banking Develop your own big data strategy by accessing additional reading materials at the end of each chapter

    This text concisely describes the Python language and its programming environment for those readers already familiar with languages such as C and C++.

    However, Python’s unique strengths, charms, and expressiveness can be hard to grasp, and there are hidden pitfalls that can easily trip you up. This second edition of Effective Python will help you master a truly “Pythonic” approach to programming, harnessing Python’s full power to write exceptionally robust and well-performing code. Using the concise, scenario-driven style pioneered in Scott Meyers’ best-selling Effective C++, Brett Slatkin brings together 90 Python best practices, tips, and shortcuts, and explains them with realistic code examples so that you can embrace Python with confidence. Drawing on years of experience building Python infrastructure at Google, Slatkin uncovers little-known quirks and idioms that powerfully impact code behavior and performance. You’ll understand the best way to accomplish key tasks so you can write code that’s easier to understand, maintain, and improve. In addition to even more advice, this new edition substantially revises all items from the first edition to reflect how best practices have evolved. Key features include 30 new actionable guidelines for all major areas of Python Detailed explanations and examples of statements, expressions, and built-in types Best practices for writing functions that clarify intention, promote reuse, and avoid bugs Better techniques and idioms for using comprehensions and generator functions Coverage of how to accurately express behaviors with classes and interfaces Guidance on how to avoid pitfalls with metaclasses and dynamic attributes More efficient and clear approaches to concurrency and parallelism Solutions for optimizing and hardening to maximize performance and quality Techniques and built-in modules that aid in debugging and testing Tools and best practices for collaborative development   Effective Python will prepare growing programmers to make a big impact using Python.

    Here is a complete library of dimensional modeling techniques-- the most comprehensive collection ever written. Greatly expanded to cover both basic and advanced techniques for optimizing data warehouse design, this second edition to Ralph Kimball's classic guide is more than sixty percent updated.The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including:* Retail sales and e-commerce* Inventory management* Procurement* Order management* Customer relationship management (CRM)* Human resources management* Accounting* Financial services* Telecommunications and utilities* Education* Transportation* Health care and insuranceBy the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts.This book is also available as part of the Kimball's Data Warehouse Toolkit Classics Box Set (ISBN: 9780470479575) with the following 3 books:The Data Warehouse Toolkit, 2nd Edition (9780471200246)The Data Warehouse Lifecycle Toolkit, 2nd Edition (9780470149775)The Data Warehouse ETL Toolkit (9780764567575)

    Thus CHACHA CHAUDHARY was born in 1971.Tall and robust SABU, who is an inhabitant of planet Jupiter, gave Chaudhary an ideal company. A combination of wisdom and strength was formed to tackle any difficult task. It is said that " Chacha Chaudhary's brain works faster than a computer". Though both fight the criminals and tricksters, each episode ends with a touch of humour. The duo perform in lighter vein. The CHAUDHARY family consists of his wife Bini, a fat sharp tongue woman, Sabu, Rocket - the dog and Dag- Dag, an old truck who is half human- half machine. Chacha Chaudhary is the most popular Indian comics. More than 10 million readers enjoy this series regularly in newspapers and comic books in ten languages. A T.V. serial based on the comics has crossed 500 episodes and still continue to be telecast on premier channel "Sahara ONE".

    Algorithms have made our lives more efficient, more entertaining, and, sometimes, better informed. At the same time, complex algorithms are increasingly violating the basic rights of individual citizens. Allegedly anonymized datasets routinely leak our most sensitive personal information; statistical models for everything from mortgages to college admissions reflect racial and gender bias. Meanwhile, users manipulate algorithms to "game" search engines, spam filters, online reviewing services, and navigation apps.Understanding and improving the science behind the algorithms that run our lives is rapidly becoming one of the most pressing issues of this century. Traditional fixes, such as laws, regulations and watchdog groups, have proven woefully inadequate. Reporting from the cutting edge of scientific research, The Ethical Algorithm offers a new approach: a set of principled solutions based on the emerging and exciting science of socially aware algorithm design. Michael Kearns and Aaron Roth explain how we can better embed human principles into machine code - without halting the advance of data-driven scientific exploration. Weaving together innovative research with stories of citizens, scientists, and activists on the front lines, The Ethical Algorithm offers a compelling vision for a future, one in which we can better protect humans from the unintended impacts of algorithms while continuing to inspire wondrous advances in technology.

    Introducing students to the many facets of neural networks, this text provides many case studies to illustrate their real-life, practical applications.

    

    Each idiom comes with a detailed description, example code showing the "wrong" way to do it, and code for the idiomatic, "Pythonic" alternative. *This version of the book is for Python 2.7.3+. There is also a Python 3.3+ version available.* "Writing Idiomatic Python" contains the most common and important Python idioms in a format that maximizes identification and understanding. Each idiom is presented as a recommendation to write some commonly used piece of code. It is followed by an explanation of why the idiom is important. It also contains two code samples: the "Harmful" way to write it and the "Idiomatic" way. * The "Harmful" way helps you identify the idiom in your own code. * The "Idiomatic" way shows you how to easily translate that code into idiomatic Python. This book is perfect for you: * If you're coming to Python from another programming language * If you're learning Python as a first programming language * If you're looking to increase the readability, maintainability, and correctness of your Python code What is "Idiomatic" Python? Every programming language has its own idioms. Programming language idioms are nothing more than the generally accepted way of writing a certain piece of code. Consistently writing idiomatic code has a number of important benefits: * Others can read and understand your code easily * Others can maintain and enhance your code with minimal effort * Your code will contain fewer bugs * Your code will teach others to write correct code without any effort on your part

    This book helps you use SQL and Excel to extract business information from relational databases and use that data to define business dimensions, store transactions about customers, produce results, and more. Each chapter explains when and why to perform a particular type of business analysis in order to obtain useful results, how to design and perform the analysis using SQL and Excel, and what the results should look like.

    Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you'll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You'll learn what it is, why it matters, and how to choose and implement solutions that work.Effectively managing big data is an issue of growing importance to businesses, not-for-profit organizations, government, and IT professionals Authors are experts in information management, big data, and a variety of solutions Explains big data in detail and discusses how to select and implement a solution, security concerns to consider, data storage and presentation issues, analytics, and much more Provides essential information in a no-nonsense, easy-to-understand style that is empowering Big Data For Dummies cuts through the confusion and helps you take charge of big data solutions for your organization.

    The title says it is the hard way to learn to writecode but it’s actually not. It’s the “hard” way only in that it’s the way people used to teach things. In this book youwill do something incredibly simple that all programmers actually do to learn a language: 1. Go through each exercise. 2. Type in each sample exactly. 3. Make it run.That’s it. This will be very difficult at first, but stick with it. If you go through this book, and do each exercise for1-2 hours a night, then you’ll have a good foundation for moving on to another book. You might not really learn“programming” from this book, but you will learn the foundation skills you need to start learning the language.This book’s job is to teach you the three most basic essential skills that a beginning programmer needs to know:Reading And Writing, Attention To Detail, Spotting Differences.

    The classic and popular text is back with refreshed pedagogy and programming problems helps the students to have an upper hand on the practical understanding of the subject. Salient Features: Expanded discussion on Recursion (Backtracking, Simulating Recursion), Spanning Trees. Covers all important topics like Strings, Arrays, Linked Lists, Trees Highly illustrative with over 300 figures and 400 solved and unsolved exercises Content 1.Introduction and Overview 2.Preliminaries 3.String Processing 4.Arrays, Records and Pointers 5.Linked Lists 6.S tacks, Queues, Recursion 7.Trees 8.Graphs and Their Applications 9.Sorting and Searching About the Author: Seymour Lipschutz Seymour Lipschutz, Professor of Mathematics, Temple University

    As the open source implementation of Google's BigTable architecture, HBase scales to billions of rows and millions of columns, while ensuring that write and read performance remain constant. Many IT executives are asking pointed questions about HBase. This book provides meaningful answers, whether you’re evaluating this non-relational database or planning to put it into practice right away. Discover how tight integration with Hadoop makes scalability with HBase easier Distribute large datasets across an inexpensive cluster of commodity servers Access HBase with native Java clients, or with gateway servers providing REST, Avro, or Thrift APIs Get details on HBase’s architecture, including the storage format, write-ahead log, background processes, and more Integrate HBase with Hadoop's MapReduce framework for massively parallelized data processing jobs Learn how to tune clusters, design schemas, copy tables, import bulk data, decommission nodes, and many other tasks