Programming & Software Development Price list in India

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Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation

Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation

Winner of the 2011 Jolt Excellence Award! Getting software released to users is often a painful, risky, and time-consuming process. This groundbreaking new book sets out the principles and technical practices that enable rapid, incremental delivery of high quality, valuable new functionality to users. Through automation of the build, deployment, and testing process, and improved collaboration between developers, testers, and operations, delivery teams can get changes released in a matter of hours- sometimes even minutes-no matter what the size of a project or the complexity of its code base. Jez Humble and David Farley begin by presenting the foundations of a rapid, reliable, low-risk delivery process. Next, they introduce the "deployment pipeline," an automated process for managing all changes, from check-in to release. Finally, they discuss the "ecosystem" needed to support continuous delivery, from infrastructure, data and configuration management to governance. The authors introduce state-of-the-art techniques, including automated infrastructure management and data migration, and the use of virtualization. For each, they review key issues, identify best practices, and demonstrate how to mitigate risks. Coverage includes Automating all facets of building, integrating, testing, and deploying software Implementing deployment pipelines at team and organizational levels Improving collaboration between developers, testers, and operations Developing features incrementally on large and distributed teams Implementing an effective configuration management strategy Automating acceptance testing, from analysis to implementation Testing capacity and other non-functional requirements Implementing continuous deployment and zero-downtime releases Managing infrastructure, data, components and dependencies Navigating risk management, compliance, and auditing

Architecting for Scale: High Availability for Your Growing Applications

Architecting for Scale: High Availability for Your Growing Applications

All Indian Reprints of O'Reilly are printed in Grayscale.

Every day, companies struggle to scale critical applications. As traffic volume and data demands increase, these applications become more complicated and brittle, exposing risks and compromising availability. This practical guide shows IT, devopsand system reliability managers how to prevent an application from becoming slow, inconsistentor downright unavailable as it grows.

Scaling isn’t just about handling more users; it’s also about managing risk and ensuring availability. Author Lee Atchison provides basic techniques for building applications that can handle huge quantities of traffic, dataand demand without affecting the quality your customers expect.

In five parts, this book explores:

  • Availability: learn techniques for building highly available applicationsand for tracking and improving availability going forward
  • Risk management: identify, mitigateand manage risks in your application, test your recovery/disaster plansand build out systems that contain fewer risks
  • Services and microservices: understand the value of services for building complicated applications that need to operate at higher scale
  • Scaling applications: assign services to specific teams, label the criticalness of each serviceand devise failure scenarios and recovery plans
  • Cloud services: understand the structure of cloud-based services, resource allocationand service distribution

Advanced Analytics with Spark: Patterns for Learning from Data at Scale

Advanced Analytics with Spark: Patterns for Learning from Data at Scale

"In the second edition of this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods and real-world data sets together to teach you how to approach analytics problems by example. Updated for Spark 2.1, this edition acts as an introduction to these techniques and other best practices in Spark programming.
Youíll start with an introduction to Spark and its ecosystem and then dive into patterns that apply common techniquesóincluding classification, clustering, collaborative filtering and anomaly detectionóto fields such as genomics, security and finance.
If you have an entry-level understanding of machine learning and statistics and you program in Java, Python, or Scala, youíll find the bookís patterns useful for working on your own data applications.
With this book, you will:

Familiarize yourself with the Spark programming model
Become comfortable within the Spark ecosystem
Learn general approaches in data science
Examine complete implementations that analyze large public data sets
Discover which machine learning tools make sense for particular problems
Acquire code that can be adapted to many uses
"

Cloud Native Java: Designing Resilient Systems with Spring Boot, Spring Cloud, and Cloud Foundry

Cloud Native Java: Designing Resilient Systems with Spring Boot, Spring Cloud, and Cloud Foundry

Learn the essentials of the Spring Boot microframework for developing modern, cloud-ready JVM applications and microservices across a variety of environments. with this practical book, youíll learn everything you need to know to get started working with Spring Boot.
A modern cloud-native architecture looks very different from the architectures inspired by the economics of scale ten years ago. Now that the cloud is the default for everyoneóand not just trailblazers like Google, Amazon, Twitter and NetflixóSpring Boot and Spring Cloud offer the best tools to commoditize the architecture of the cloud. This book shows you how to leverage Spring Boot to build modular, highly-scalable applications.

Kafka: The Definitive Guide- Real-Time Data and Stream Processing at Scale

Kafka: The Definitive Guide- Real-Time Data and Stream Processing at Scale

Every enterprise application creates data, whether itís log messages, metrics, user activity, outgoing messages, or something else. And how to move all of this data becomes nearly as important as the data itself. If youíre an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds.
Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices and build scalable stream-processing applications with this platform. Through detailed examples, youíll learn Kafkaís design principles, reliability guarantees, key APIs and architecture details, including the replication protocol, the controller and the storage layer.

Understand publish-subscribe messaging and how it fits in the big data ecosystem.
Explore Kafka producers and consumers for writing and reading messages
Understand Kafka patterns and use-case requirements to ensure reliable data delivery
Get best practices for building data pipelines and applications with Kafka
Manage Kafka in production and learn to perform monitoring, tuning and maintenance tasks
Learn the most critical metrics among Kafkaís operational measurements
Explore how Kafkaís stream delivery capabilities make it a perfect source for stream processing systems

Java 9 Modularity: Patterns and Practices for Developing Maintainable Applications

Java 9 Modularity: Patterns and Practices for Developing Maintainable Applications

The upcoming Java 9 module system will affect existing applications and offer new ways of creating modular and maintainable applications. with this hands-on book, Java developers will learn not only about the joys of modularity, but also about the patterns needed to create truly modular and reliable applications. Authors Sander Mak and Paul Bakker teach you the concepts behind the Java 9 module system, along with the new tools it offers. Youíll also gain learn how to modularize existing code and how to build new Java applications in a modular way.

Understand Java 9 module system concepts
Master the patterns and practices for building truly modular applications
Migrate existing applications and libraries to Java 9 modules
Use JDK 9 tools for modular development and migration

Data science for Business

Data science for Business

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.

Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You'll not only learn how to improve communication between business stakeholders and data scientists but also how participate intelligently in your company's data science projects. You'll also discover how to think data-analytically and fully appreciate how data science methods can support business decision-making.

  • Understand how data science fits in your organization - and how you can use it for competitive advantage.
  • Treat data as a business asset that requires careful investment if you're to gain real value.
  • Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way.
  • Learn general concepts for actually extracting knowledge from data.
  • Apply data science principles when interviewing data science job candidates.

Python Data Science Handbook: Essential Tools for Working with Data

Python Data Science Handbook: Essential Tools for Working with Data

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all IPython, NumPy, Pandas, Matplotlib, Scikit-Learn and other related tools.

Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.

With this handbook, you'll learn how to use:
IPython and Jupyter: provide computational environments for data scientists using Python
NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python
Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python
Matplotlib: includes capabilities for a flexible range of data visualizations in Python
Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms.

Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

this practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems such as loading data, Handling text or numerical data, model selection, and dimensionality reduction and many other topics.
Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications.
you'll find recipes for:

  • handling numerical and categorical data, text, images, and dates and times

  • Model evaluation and selection
  • saving and loading trained models

  • .

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