If you are a data analyst with basic knowledge of Big Data analysis but no knowledge of Splunk, then this book will help you get started with Splunk. The book assumes that you have access to a copy of Splunk, ideally not in production, and many examples also assume you have administrator rights.
Splunk is a type of analysis and reporting software for analyzing machine-generated Big Data. It captures, indexes, and correlates real-time data in a searchable repository from which it can generate graphs, reports, alerts, dashboards, and visualizations. It aims to make machine data accessible across an organization for a variety of purposes. Implementing Splunk Second Edition is a learning guide that introduces you to all the latest features and improvements of Splunk 6.2. The book starts by introducing you to various concepts such as charting, reporting, clustering, and visualization. Every chapter is dedicated to enhancing your knowledge of a specific concept, including data models and pivots, speeding up your queries, backfilling, data replication, and so on. By the end of the book, you'll have a very good understanding of Splunk and be able to perform efficient data analysis.
Learn the A to Z of building excellent Splunk applications with the latest techniques using this comprehensive guide About This Book This is the most up-to-date book on Splunk 6.3 for developers Get ahead of being just a Splunk user and start creating custom Splunk applications as per your needs Your one-stop-solution to Splunk application development Who This Book Is For This book is for those who have some familiarity with Splunk and now want to learn how to develop an efficient Splunk application. Previous experience with Splunk, writing searches, and designing basic dashboards is expected. What You Will Learn Implement a Modular Input and a custom D3 data visualization Create a directory structure and set view permissions Create a search view and a dashboard view using advanced XML modules Enhance your application using eventtypes, tags, and macros Package a Splunk application using best practices Publish a Splunk application to the Splunk community In Detail Splunk provides a platform that allows you to search data stored on a machine, analyze it, and visualize the analyzed data to make informed decisions. The adoption of Splunk in enterprises is huge, and it has a wide range of customers right from Adobe to Dominos. Using the Splunk platform as a user is one thing, but customizing this platform and creating applications specific to your needs takes more than basic knowledge of the platform. This book will dive into developing Splunk applications that cater to your needs of making sense of data and will let you visualize this data with the help of stunning dashboards. This book includes everything on developing a full-fledged Splunk application right from designing to implementing to publishing. We will design the fundamentals to build a Splunk application and then move on to creating one. During the course of the book, we will cover application data, objects, permissions, and more. After this, we will show you how to enhance the application, including branding, workflows, and enriched data. Views, dashboards, and web frameworks are also covered. This book will showcase everything new in the latest version of Splunk including the latest data models, alert actions, XML forms, various dashboard enhancements, and visualization options (with D3). Finally, we take a look at the latest Splunk cloud applications, advanced integrations, and development as per the latest release. Style and approach This book is an easy-to-follow guide with lots of tips and tricks to help you master all the concepts necessary to develop and deploy your Splunk applications.
Explore architectural approaches to building Data Lakes that ingest, index, manage, and analyze massive amounts of data using Big Data technologies About This Book Comprehend the intricacies of architecting a Data Lake and build a data strategy around your current data architecture Efficiently manage vast amounts of data and deliver it to multiple applications and systems with a high degree of performance and scalability Packed with industry best practices and use-case scenarios to get you up-and-running Who This Book Is For This book is for architects and senior managers who are responsible for building a strategy around their current data architecture, helping them identify the need for a Data Lake implementation in an enterprise context. The reader will need a good knowledge of master data management and information lifecycle management, and experience of Big Data technologies. What You Will Learn Identify the need for a Data Lake in your enterprise context and learn to architect a Data Lake Learn to build various tiers of a Data Lake, such as data intake, management, consumption, and governance, with a focus on practical implementation scenarios Find out the key considerations to be taken into account while building each tier of the Data Lake Understand Hadoop-oriented data transfer mechanism to ingest data in batch, micro-batch, and real-time modes Explore various data integration needs and learn how to perform data enrichment and data transformations using Big Data technologies Enable data discovery on the Data Lake to allow users to discover the data Discover how data is packaged and provisioned for consumption Comprehend the importance of including data governance disciplines while building a Data Lake In Detail A Data Lake is a highly scalable platform for storing huge volumes of multistructured data from disparate sources with centralized data management services. This book explores the potential of Data Lakes and explores architectural approaches to building data lakes that ingest, index, manage, and analyze massive amounts of data using batch and real-time processing frameworks. It guides you on how to go about building a Data Lake that is managed by Hadoop and accessed as required by other Big Data applications. This book will guide readers (using best practices) in developing Data Lake's capabilities. It will focus on architect data governance, security, data quality, data lineage tracking, metadata management, and semantic data tagging. By the end of this book, you will have a good understanding of building a Data Lake for Big Data. Style and approach Data Lake Development with Big Data provides architectural approaches to building a Data Lake. It follows a use case-based approach where practical implementation scenarios of each key component are explained. It also helps you understand how these use cases are implemented in a Data Lake. The chapters are organized in a way that mimics the sequential data flow evidenced in a Data Lake.
Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks
Author: James D. Miller
Publisher: Packt Publishing Ltd
Get your statistics basics right before diving into the world of data science About This Book No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs; Implement statistics in data science tasks such as data cleaning, mining, and analysis Learn all about probability, statistics, numerical computations, and more with the help of R programs Who This Book Is For This book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful. What You Will Learn Analyze the transition from a data developer to a data scientist mindset Get acquainted with the R programs and the logic used for statistical computations Understand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more Learn to implement statistics in data science tasks such as data cleaning, mining, and analysis Learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks Get comfortable with performing various statistical computations for data science programmatically In Detail Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on. This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks. By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically. Style and approach Step by step comprehensive guide with real world examples
Effective operational intelligence to transform machine-generated data into valuable business insight, 3rd Edition
Author: James Miller
Publisher: Packt Publishing Ltd
A comprehensive guide to making machine data accessible across the organization using advanced dashboards Key Features Enrich machine-generated data and transform it into useful, meaningful insights Perform search operations and configurations, build dashboards, and manage logs Extend Splunk services with scripts and advanced configurations to process optimal results Book Description Splunk is the leading platform that fosters an efficient methodology and delivers ways to search, monitor, and analyze growing amounts of big data. This book will allow you to implement new services and utilize them to quickly and efficiently process machine-generated big data. We introduce you to all the new features, improvements, and offerings of Splunk 7. We cover the new modules of Splunk: Splunk Cloud and the Machine Learning Toolkit to ease data usage. Furthermore, you will learn to use search terms effectively with Boolean and grouping operators. You will learn not only how to modify your search to make your searches fast but also how to use wildcards efficiently. Later you will learn how to use stats to aggregate values, a chart to turn data, and a time chart to show values over time; you'll also work with fields and chart enhancements and learn how to create a data model with faster data model acceleration. Once this is done, you will learn about XML Dashboards, working with apps, building advanced dashboards, configuring and extending Splunk, advanced deployments, and more. Finally, we teach you how to use the Machine Learning Toolkit and best practices and tips to help you implement Splunk services effectively and efficiently. By the end of this book, you will have learned about the Splunk software as a whole and implemented Splunk services in your tasks at projects What you will learn Focus on the new features of the latest version of Splunk Enterprise 7 Master the new offerings in Splunk: Splunk Cloud and the Machine Learning Toolkit Create efficient and effective searches within the organization Master the use of Splunk tables, charts, and graph enhancements Use Splunk data models and pivots with faster data model acceleration Master all aspects of Splunk XML dashboards with hands-on applications Create and deploy advanced Splunk dashboards to share valuable business insights with peers Who this book is for This book is intended for data analysts, business analysts, and IT administrators who want to make the best use of big data, operational intelligence, log management, and monitoring within their organization. Some knowledge of Splunk services will help you get the most out of the book
Implement continuous delivery and integration in the AWS environment, 2nd Edition
Author: Yogesh Raheja
Publisher: Packt Publishing Ltd
Scale and maintain outstanding performance in your AWS-based infrastructure using DevOps principles Key Features Implement continuous integration and continuous deployment pipelines on AWS Gain insight from an expert who has worked with Silicon Valley's most high-profile companies Implement DevOps principles to take full advantage of the AWS stack and services Book Description The DevOps movement has transformed the way modern tech companies work. Amazon Web Services (AWS), which has been at the forefront of the cloud computing revolution, has also been a key contributor to the DevOps movement, creating a huge range of managed services that help you implement DevOps principles. Effective DevOps with AWS, Second Edition will help you to understand how the most successful tech start-ups launch and scale their services on AWS, and will teach you how you can do the same. This book explains how to treat infrastructure as code, meaning you can bring resources online and offline as easily as you control your software. You will also build a continuous integration and continuous deployment pipeline to keep your app up to date. Once you have gotten to grips will all this, we'll move on to how to scale your applications to offer maximum performance to users even when traffic spikes, by using the latest technologies, such as containers. In addition to this, you'll get insights into monitoring and alerting, so you can make sure your users have the best experience when using your service. In the concluding chapters, we'll cover inbuilt AWS tools such as CodeDeploy and CloudFormation, which are used by many AWS administrators to perform DevOps. By the end of this book, you'll have learned how to ensure the security of your platform and data, using the latest and most prominent AWS tools. What you will learn Implement automatic AWS instance provisioning using CloudFormation Deploy your application on a provisioned infrastructure with Ansible Manage infrastructure using Terraform Build and deploy a CI/CD pipeline with Automated Testing on AWS Understand the container journey for a CI/CD pipeline using AWS ECS Monitor and secure your AWS environment Who this book is for Effective DevOps with AWS is for you if you are a developer, DevOps engineer, or you work in a team which wants to build and use AWS for software infrastructure. Basic computer science knowledge is required to get the most out of this book.
Deriving Operational Intelligence from Social Media, Machine Data, Existing Data Warehouses, and Other Real-Time Streaming Sources
Author: Peter Zadrozny
A hands-on book showing how to process and derive business value from big data in real time. Examples in the book draw from social media sources such as Twitter (tweets) and Foursquare (check-ins). You also learn to draw from machine data, enabling you to analyze web server log files and patterns of user access in real time, as the access is occurring.
This book is intended for users of all levels who are looking to leverage the Splunk Enterprise platform as a valuable operational intelligence tool. The recipes provided in this book will appeal to individuals from all facets of a business – IT, Security, Product, Marketing, and many more!