Continuous Integration Vs Continuous Delivery Vs Continuous Deployment 2nd Edition

Continuous Integration Vs. Continuous Delivery Vs. Continuous Deployment, 2nd Edition

Continuous integration, continuous delivery, and continuous deployment are key software delivery processes in a DevOps environment. But what does each one do for your product development and release cycles? In this updated report, Brent Laster explains what these terms really boil down to and how they work separately and together to help your team release software. This powerful set of disciplines, best practices, and technologies automates the integration and delivery of source code changes from inception through production. Although their implementation may vary, continuous integration, continuous delivery, and continuous deployment are necessary to ensure that software is released frequently, reliably, and with high quality. You'll learn how: Continuous integration makes certain that individual code changes are suitable for inclusion in the code base and merged in successfully Continuous delivery assembles your product, automatically tests quality and functionality, and produces deliverables that are proven to be deployable Continuous deployment simplifies releasing the product to customers-whether it's in the cloud, via download, or in some other format-while also allowing for limited deployments or rolling deployments back This valuable resource for business professionals, software engineering managers, senior developers, and architects will also explore how containers and Kubernetes interact in this environment.

Continuous Integration Vs. Continuous Delivery Vs. Continuous Deployment

Spark in Action, Second Edition

Summary The Spark distributed data processing platform provides an easy-to-implement tool for ingesting, streaming, and processing data from any source. In Spark in Action, Second Edition, you’ll learn to take advantage of Spark’s core features and incredible processing speed, with applications including real-time computation, delayed evaluation, and machine learning. Spark skills are a hot commodity in enterprises worldwide, and with Spark’s powerful and flexible Java APIs, you can reap all the benefits without first learning Scala or Hadoop. Foreword by Rob Thomas. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Analyzing enterprise data starts by reading, filtering, and merging files and streams from many sources. The Spark data processing engine handles this varied volume like a champ, delivering speeds 100 times faster than Hadoop systems. Thanks to SQL support, an intuitive interface, and a straightforward multilanguage API, you can use Spark without learning a complex new ecosystem. About the book Spark in Action, Second Edition, teaches you to create end-to-end analytics applications. In this entirely new book, you’ll learn from interesting Java-based examples, including a complete data pipeline for processing NASA satellite data. And you’ll discover Java, Python, and Scala code samples hosted on GitHub that you can explore and adapt, plus appendixes that give you a cheat sheet for installing tools and understanding Spark-specific terms. What's inside Writing Spark applications in Java Spark application architecture Ingestion through files, databases, streaming, and Elasticsearch Querying distributed datasets with Spark SQL About the reader This book does not assume previous experience with Spark, Scala, or Hadoop. About the author Jean-Georges Perrin is an experienced data and software architect. He is France’s first IBM Champion and has been honored for 12 consecutive years. Table of Contents PART 1 - THE THEORY CRIPPLED BY AWESOME EXAMPLES 1 So, what is Spark, anyway? 2 Architecture and flow 3 The majestic role of the dataframe 4 Fundamentally lazy 5 Building a simple app for deployment 6 Deploying your simple app PART 2 - INGESTION 7 Ingestion from files 8 Ingestion from databases 9 Advanced ingestion: finding data sources and building your own 10 Ingestion through structured streaming PART 3 - TRANSFORMING YOUR DATA 11 Working with SQL 12 Transforming your data 13 Transforming entire documents 14 Extending transformations with user-defined functions 15 Aggregating your data PART 4 - GOING FURTHER 16 Cache and checkpoint: Enhancing Spark’s performances 17 Exporting data and building full data pipelines 18 Exploring deployment