Apache Kafka 1.0 Cookbook


Book Description

Simplify real-time data processing by leveraging the power of Apache Kafka 1.0 About This Book Use Kafka 1.0 features such as Confluent platforms and Kafka streams to build efficient streaming data applications to handle and process your data Integrate Kafka with other Big Data tools such as Apache Hadoop, Apache Spark, and more Hands-on recipes to help you design, operate, maintain, and secure your Apache Kafka cluster with ease Who This Book Is For This book is for developers and Kafka administrators who are looking for quick, practical solutions to problems encountered while operating, managing or monitoring Apache Kafka. If you are a developer, some knowledge of Scala or Java will help, while for administrators, some working knowledge of Kafka will be useful. What You Will Learn Install and configure Apache Kafka 1.0 to get optimal performance Create and configure Kafka Producers and Consumers Operate your Kafka clusters efficiently by implementing the mirroring technique Work with the new Confluent platform and Kafka streams, and achieve high availability with Kafka Monitor Kafka using tools such as Graphite and Ganglia Integrate Kafka with third-party tools such as Elasticsearch, Logstash, Apache Hadoop, Apache Spark, and more In Detail Apache Kafka provides a unified, high-throughput, low-latency platform to handle real-time data feeds. This book will show you how to use Kafka efficiently, and contains practical solutions to the common problems that developers and administrators usually face while working with it. This practical guide contains easy-to-follow recipes to help you set up, configure, and use Apache Kafka in the best possible manner. You will use Apache Kafka Consumers and Producers to build effective real-time streaming applications. The book covers the recently released Kafka version 1.0, the Confluent Platform and Kafka Streams. The programming aspect covered in the book will teach you how to perform important tasks such as message validation, enrichment and composition.Recipes focusing on optimizing the performance of your Kafka cluster, and integrate Kafka with a variety of third-party tools such as Apache Hadoop, Apache Spark, and Elasticsearch will help ease your day to day collaboration with Kafka greatly. Finally, we cover tasks related to monitoring and securing your Apache Kafka cluster using tools such as Ganglia and Graphite. If you're looking to become the go-to person in your organization when it comes to working with Apache Kafka, this book is the only resource you need to have. Style and approach Following a cookbook recipe-based approach, we'll teach you how to solve everyday difficulties and struggles you encounter using Kafka through hands-on examples.




Knative Cookbook


Book Description

Enterprise developers face several challenges when it comes to building serverless applications, such as integrating applications and building container images from source. With more than 60 practical recipes, this cookbook helps you solve these issues with Knative—the first serverless platform natively designed for Kubernetes. Each recipe contains detailed examples and exercises, along with a discussion of how and why it works. If you have a good understanding of serverless computing and Kubernetes core resources such as deployment, services, routes, and replicas, the recipes in this cookbook show you how to apply Knative in real enterprise application development. Authors Kamesh Sampath and Burr Sutter include chapters on autoscaling, build and eventing, observability, Knative on OpenShift, and more. With this cookbook, you’ll learn how to: Efficiently build, deploy, and manage modern serverless workloads Apply Knative in real enterprise scenarios, including advanced eventing Monitor your Knative serverless applications effectively Integrate Knative with CI/CD principles, such as using pipelines for faster, more successful production deployments Deploy a rich ecosystem of enterprise integration patterns and connectors in Apache Camel K as Kubernetes and Knative components




Spark Cookbook


Book Description

By introducing in-memory persistent storage, Apache Spark eliminates the need to store intermediate data in filesystems, thereby increasing processing speed by up to 100 times. This book will focus on how to analyze large and complex sets of data. Starting with installing and configuring Apache Spark with various cluster managers, you will cover setting up development environments. You will then cover various recipes to perform interactive queries using Spark SQL and real-time streaming with various sources such as Twitter Stream and Apache Kafka. You will then focus on machine learning, including supervised learning, unsupervised learning, and recommendation engine algorithms. After mastering graph processing using GraphX, you will cover various recipes for cluster optimization and troubleshooting.




Akka Cookbook


Book Description

Learn how to use the Akka framework to build effective applications in Scala About This Book Covers a discussion on Lagom—the newest launched Akka framework that is built to create complex microservices easily The recipe approach of the book allows the reader to know important and independent concepts of Scala and Akka in a seamless manner Provides a comprehensive understanding of the Akka actor model and implementing it to create reactive web applications Who This Book Is For If you are a Scala developer who wants to build scalable and concurrent applications, then this book is for you. Basic knowledge of Akka will help you take advantage of this book. What You Will Learn Control an actor using the ContolAware mailbox Test a fault-tolerant application using the Akka test kit Create a parallel application using futures and agents Package and deploy Akka application inside Docker Deploy remote actors programmatically on different nodes Integrate Streams with Akka actors Install Lagom and create a Lagom project In Detail Akka is an open source toolkit that simplifies the construction of distributed and concurrent applications on the JVM. This book will teach you how to develop reactive applications in Scala using the Akka framework. This book will show you how to build concurrent, scalable, and reactive applications in Akka. You will see how to create high performance applications, extend applications, build microservices with Lagom, and more. We will explore Akka's actor model and show you how to incorporate concurrency into your applications. The book puts a special emphasis on performance improvement and how to make an application available for users. We also make a special mention of message routing and construction. By the end of this book, you will be able to create a high-performing Scala application using the Akka framework. Style and approach This highly practical recipe-based approach will allow you to build scalable, robust, and reactive applications using the Akka framework.




Artificial Intelligence for IoT Cookbook


Book Description

Implement machine learning and deep learning techniques to perform predictive analytics on real-time IoT data Key FeaturesDiscover quick solutions to common problems that you'll face while building smart IoT applicationsImplement advanced techniques such as computer vision, NLP, and embedded machine learningBuild, maintain, and deploy machine learning systems to extract key insights from IoT dataBook Description Artificial intelligence (AI) is rapidly finding practical applications across a wide variety of industry verticals, and the Internet of Things (IoT) is one of them. Developers are looking for ways to make IoT devices smarter and to make users' lives easier. With this AI cookbook, you'll be able to implement smart analytics using IoT data to gain insights, predict outcomes, and make informed decisions, along with covering advanced AI techniques that facilitate analytics and learning in various IoT applications. Using a recipe-based approach, the book will take you through essential processes such as data collection, data analysis, modeling, statistics and monitoring, and deployment. You'll use real-life datasets from smart homes, industrial IoT, and smart devices to train and evaluate simple to complex models and make predictions using trained models. Later chapters will take you through the key challenges faced while implementing machine learning, deep learning, and other AI techniques, such as natural language processing (NLP), computer vision, and embedded machine learning for building smart IoT systems. In addition to this, you'll learn how to deploy models and improve their performance with ease. By the end of this book, you'll be able to package and deploy end-to-end AI apps and apply best practice solutions to common IoT problems. What you will learnExplore various AI techniques to build smart IoT solutions from scratchUse machine learning and deep learning techniques to build smart voice recognition and facial detection systemsGain insights into IoT data using algorithms and implement them in projectsPerform anomaly detection for time series data and other types of IoT dataImplement embedded systems learning techniques for machine learning on small devicesApply pre-trained machine learning models to an edge deviceDeploy machine learning models to web apps and mobile using TensorFlow.js and JavaWho this book is for If you're an IoT practitioner looking to incorporate AI techniques to build smart IoT solutions without having to trawl through a lot of AI theory, this AI IoT book is for you. Data scientists and AI developers who want to build IoT-focused AI solutions will also find this book useful. Knowledge of the Python programming language and basic IoT concepts is required to grasp the concepts covered in this artificial intelligence book more effectively.




Apache Spark for Data Science Cookbook


Book Description

Over insightful 90 recipes to get lightning-fast analytics with Apache Spark About This Book Use Apache Spark for data processing with these hands-on recipes Implement end-to-end, large-scale data analysis better than ever before Work with powerful libraries such as MLLib, SciPy, NumPy, and Pandas to gain insights from your data Who This Book Is For This book is for novice and intermediate level data science professionals and data analysts who want to solve data science problems with a distributed computing framework. Basic experience with data science implementation tasks is expected. Data science professionals looking to skill up and gain an edge in the field will find this book helpful. What You Will Learn Explore the topics of data mining, text mining, Natural Language Processing, information retrieval, and machine learning. Solve real-world analytical problems with large data sets. Address data science challenges with analytical tools on a distributed system like Spark (apt for iterative algorithms), which offers in-memory processing and more flexibility for data analysis at scale. Get hands-on experience with algorithms like Classification, regression, and recommendation on real datasets using Spark MLLib package. Learn about numerical and scientific computing using NumPy and SciPy on Spark. Use Predictive Model Markup Language (PMML) in Spark for statistical data mining models. In Detail Spark has emerged as the most promising big data analytics engine for data science professionals. The true power and value of Apache Spark lies in its ability to execute data science tasks with speed and accuracy. Spark's selling point is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. It lets you tackle the complexities that come with raw unstructured data sets with ease. This guide will get you comfortable and confident performing data science tasks with Spark. You will learn about implementations including distributed deep learning, numerical computing, and scalable machine learning. You will be shown effective solutions to problematic concepts in data science using Spark's data science libraries such as MLLib, Pandas, NumPy, SciPy, and more. These simple and efficient recipes will show you how to implement algorithms and optimize your work. Style and approach This book contains a comprehensive range of recipes designed to help you learn the fundamentals and tackle the difficulties of data science. This book outlines practical steps to produce powerful insights into Big Data through a recipe-based approach.




Elasticsearch 8.x Cookbook


Book Description

Search, analyze, store and manage data effectively with Elasticsearch 8.x Key Features • Explore the capabilities of Elasticsearch 8.x with easy-to-follow recipes • Extend the Elasticsearch functionalities and learn how to deploy on Elastic Cloud • Deploy and manage simple Elasticsearch nodes as well as complex cluster topologies Book Description Elasticsearch is a Lucene-based distributed search engine at the heart of the Elastic Stack that allows you to index and search unstructured content with petabytes of data. With this updated fifth edition, you'll cover comprehensive recipes relating to what's new in Elasticsearch 8.x and see how to create and run complex queries and analytics. The recipes will guide you through performing index mapping, aggregation, working with queries, and scripting using Elasticsearch. You'll focus on numerous solutions and quick techniques for performing both common and uncommon tasks such as deploying Elasticsearch nodes, using the ingest module, working with X-Pack, and creating different visualizations. As you advance, you'll learn how to manage various clusters, restore data, and install Kibana to monitor a cluster and extend it using a variety of plugins. Furthermore, you'll understand how to integrate your Java, Scala, Python, and big data applications such as Apache Spark and Pig with Elasticsearch and create efficient data applications powered by enhanced functionalities and custom plugins. By the end of this Elasticsearch cookbook, you'll have gained in-depth knowledge of implementing the Elasticsearch architecture and be able to manage, search, and store data efficiently and effectively using Elasticsearch. What you will learn • Become well-versed with the capabilities of X-Pack • Optimize search results by executing analytics aggregations • Get to grips with using text and numeric queries as well as relationship and geo queries • Install Kibana to monitor clusters and extend it for plugins • Build complex queries by managing indices and documents • Monitor the performance of your cluster and nodes • Design advanced mapping to take full control of index steps • Integrate Elasticsearch in Java, Scala, Python, and big data applications Who this book is for If you're a software engineer, big data infrastructure engineer, or Elasticsearch developer, you'll find this Elasticsearch book useful. The book will also help data professionals working in e-commerce and FMCG industries who use Elastic for metrics evaluation and search analytics to gain deeper insights and make better business decisions. Prior experience with Elasticsearch will help you get the most out of this book.




Azure Databricks Cookbook


Book Description

Get to grips with building and productionizing end-to-end big data solutions in Azure and learn best practices for working with large datasets Key FeaturesIntegrate with Azure Synapse Analytics, Cosmos DB, and Azure HDInsight Kafka Cluster to scale and analyze your projects and build pipelinesUse Databricks SQL to run ad hoc queries on your data lake and create dashboardsProductionize a solution using CI/CD for deploying notebooks and Azure Databricks Service to various environmentsBook Description Azure Databricks is a unified collaborative platform for performing scalable analytics in an interactive environment. The Azure Databricks Cookbook provides recipes to get hands-on with the analytics process, including ingesting data from various batch and streaming sources and building a modern data warehouse. The book starts by teaching you how to create an Azure Databricks instance within the Azure portal, Azure CLI, and ARM templates. You'll work through clusters in Databricks and explore recipes for ingesting data from sources, including files, databases, and streaming sources such as Apache Kafka and EventHub. The book will help you explore all the features supported by Azure Databricks for building powerful end-to-end data pipelines. You'll also find out how to build a modern data warehouse by using Delta tables and Azure Synapse Analytics. Later, you'll learn how to write ad hoc queries and extract meaningful insights from the data lake by creating visualizations and dashboards with Databricks SQL. Finally, you'll deploy and productionize a data pipeline as well as deploy notebooks and Azure Databricks service using continuous integration and continuous delivery (CI/CD). By the end of this Azure book, you'll be able to use Azure Databricks to streamline different processes involved in building data-driven apps. What you will learnRead and write data from and to various Azure resources and file formatsBuild a modern data warehouse with Delta Tables and Azure Synapse AnalyticsExplore jobs, stages, and tasks and see how Spark lazy evaluation worksHandle concurrent transactions and learn performance optimization in Delta tablesLearn Databricks SQL and create real-time dashboards in Databricks SQLIntegrate Azure DevOps for version control, deploying, and productionizing solutions with CI/CD pipelinesDiscover how to use RBAC and ACLs to restrict data accessBuild end-to-end data processing pipeline for near real-time data analyticsWho this book is for This recipe-based book is for data scientists, data engineers, big data professionals, and machine learning engineers who want to perform data analytics on their applications. Prior experience of working with Apache Spark and Azure is necessary to get the most out of this book.




Microservices Deployment Cookbook


Book Description

Master over 60 recipes to help you deliver complete, scalable, microservice-based solutions and see the improved business results immediately About This Book Adopt microservices-based architecture and deploy it at scale Build your complete microservice architecture using different recipes for different solutions Identify specific tools for specific scenarios and deliver immediate business results, correlate use cases, and adopt them in your team and organization Who This Book Is For This book is for developers, ops, and DevOps professionals who would like to put microservices to work and improve products, services, and operations. Those looking to build and deploy microservices will find this book useful, as well as managers and people at CXO level looking to adopt microservices in their organization. Prior knowledge of Java is expected. No prior knowledge of microservices is assumed. What You Will Learn Build microservices using Spring Boot, Wildfly Swarm, Dropwizard, and SparkJava Containerize your microservice using Docker Deploy microservices using Mesos/Marathon and Kubernetes Implement service discovery and load balancing using Zookeeper, Consul, and Nginx Monitor microservices using Graphite and Grafana Write stream programs with Kafka Streams and Spark Aggregate and manage logs using Kafka Get introduced to DC/OS, Docker Swarm, and YARN In Detail This book will help any team or organization understand, deploy, and manage microservices at scale. It is driven by a sample application, helping you gradually build a complete microservice-based ecosystem. Rather than just focusing on writing a microservice, this book addresses various other microservice-related solutions: deployments, clustering, load balancing, logging, streaming, and monitoring. The initial chapters offer insights into how web and enterprise apps can be migrated to scalable microservices. Moving on, you'll see how to Dockerize your application so that it is ready to be shipped and deployed. We will look at how to deploy microservices on Mesos and Marathon and will also deploy microservices on Kubernetes. Next, you will implement service discovery and load balancing for your microservices. We'll also show you how to build asynchronous streaming systems using Kafka Streams and Apache Spark. Finally, we wind up by aggregating your logs in Kafka, creating your own metrics, and monitoring the metrics for the microservice. Style and approach This book follows a recipe-driven approach and shows you how to plug and play with all the various pieces, putting them together to build a complete scalable microservice ecosystem. You do not need to study the chapters in order, as you can directly refer to the content you need for your situation.




Cloud Native Microservices Cookbook


Book Description

Unlock the secrets of cloud-native success with step-by-step recipes for conquering every stage of microservice deployment KEY FEATURES ● Develop, test, build, and deploy with cloud-native microservices. ● Orchestrate microservices with containerization in the cloud. ● Ensure cloud observability and security in implementation. DESCRIPTION The convergence of microservices and cloud technology represents a significant paradigm shift in software development. To fully leverage the potential of both, integration from the outset of application development is crucial. Cloud-native microservices cookbook serve as a conduit, harmonizing disparate elements of microservice construction by establishing a cohesive framework from inception to deployment. This book meticulously outlines the various stages involved in launching an application utilizing cloud-native microservices. It commences with the foundational aspects of application development, emphasizing microservice architecture principles such as configuration and service discovery, considering cloud infrastructure. Progressing through containerization, continuous integration (CI), and continuous deployment (CD) pipelines, the book explores the intricacies of orchestration, high availability (HA), auto scalability, and cloud security. Subsequently, it elucidates the significance of observability in monitoring microservices post-deployment, concluding with a comprehensive exploration of Infrastructure as Code (IaC) for cloud infrastructure provisioning. Explore cloud-native microservices basics using real-world examples from the finance sector. Follow curated recipes from concept to cloud deployment for a clear understanding and smooth application development. WHAT YOU WILL LEARN ● Learn the fundamental principles of data architecture. ● Practical methodology encompassing the development, testing, building, containerization, and orchestration of microservices. ● Software development, spanning from initial design to cloud hosting. ● Achieve microservice auto scalability and high availability. ● Utilizing cloud services and experimenting with newfound services confidently. ● Meticulously track cloud expenditures, alleviating any apprehension surrounding cost management. WHO THIS BOOK IS FOR The book is ideal for software developers, solution designers, and DevOps engineers with a foundational understanding of programming concepts and professionals seeking to deepen their expertise in system architecture and full-stack development within cloud environments. TABLE OF CONTENTS 1. Microservices and Cloud 2. Developing Microservices and Test Cases 3. Externalize Application Configurations 4. Implementing Dynamic Services 5. Containerization Using Docker 6. Pipeline Automation for CI/CD 7. Microservices Orchestration 8. Auto Scalability, High Availability, and Disaster Recovery 9. Cloud Security 10. Observability 11. Infrastructure Automation with IaC