Get up and running with building data pipelines and creating interactive dashboards to visualize, monitor, and present a wide variety of time-series data with this comprehensive introductory guide Key Features Install, set up, and configure Grafana for real-time data analysis, visualization, and alerting Visualize and monitor data using data sources such as InfluxDB, Telegraf, Prometheus, and Elasticsearch Explore Grafana's cloud support with Microsoft Azure, Amazon CloudWatch, and Google Cloud Monitoring Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionGet ready to unlock the full potential of the open-source Grafana observability platform, ideal for analyzing and monitoring time-series data with this updated second edition. This beginners guide will help you get up to speed with Grafana’s latest features for querying, visualizing, and exploring logs and metrics, no matter where they are stored. Starting with the basics, this book demonstrates how to quickly install and set up a Grafana server using Docker. You’ll then be introduced to the main components of the Grafana interface before learning how to analyze and visualize data from sources such as InfluxDB, Telegraf, Prometheus, Logstash, and Elasticsearch. The book extensively covers key panel visualizations in Grafana, including Time Series, Stat, Table, Bar Gauge, and Text, and guides you in using Python to pipeline data, transformations to facilitate analytics, and templating to build dynamic dashboards. Exploring real-time data streaming with Telegraf, Promtail, and Loki, you’ll work with observability features like alerting rules and integration with PagerDuty and Slack. As you progress, the book addresses the administrative aspects of Grafana, from configuring users and organizations to implementing user authentication with Okta and LDAP, as well as organizing dashboards into folders, and more. By the end of this book, you’ll have gained all the knowledge you need to start building interactive dashboards.What you will learn Learn the techniques of data visualization using Grafana Get familiar with the major components of Time series visualization Explore data transformation operations, query inspector, and time interval settings Work with advanced dashboard features, such as annotations, variable-based templating, and dashboard linking and sharing Connect user authentication through Okta, Google, GitHub, and other external providers Discover Grafana’s monitoring support for cloud service infrastructures Who this book is for This book is for business intelligence developers, business analysts, data analysts, and anyone interested in performing time-series data analysis and monitoring using Grafana. You’ll also find this book useful if you’re looking to create and share interactive dashboards or get up to speed with the latest features of Grafana. Although no prior knowledge of Grafana is required, basic knowledge of data visualization and some Python programming experience will help you understand the concepts covered in the book.

A comprehensive introduction to help you get up and running with creating interactive dashboards to visualize and monitor time-series data in no time Key Features Install, set up, and configure Grafana for real-time data analysis and visualization Visualize and monitor data using data sources such as InfluxDB, Prometheus, and Elasticsearch Explore Grafana's multi-cloud support with Microsoft Azure, Amazon CloudWatch, and Google Stackdriver Book DescriptionGrafana is an open-source analytical platform used to analyze and monitoring time-series data. This beginner's guide will help you get to grips with Grafana's new features for querying, visualizing, and exploring metrics and logs no matter where they are stored. The book begins by showing you how to install and set up the Grafana server. You'll explore the working mechanism of various components of the Grafana interface along with its security features, and learn how to visualize and monitor data using, InfluxDB, Prometheus, Logstash, and Elasticsearch. This Grafana book covers the advanced features of the Graph panel and shows you how Stat, Table, Bar Gauge, and Text are used. You'll build dynamic dashboards to perform end-to-end analytics and label and organize dashboards into folders to make them easier to find. As you progress, the book delves into the administrative aspects of Grafana by creating alerts, setting permissions for teams, and implementing user authentication. Along with exploring Grafana's multi-cloud monitoring support, you'll also learn about Grafana Loki, which is a backend logger for users running Prometheus and Kubernetes. By the end of this book, you'll have gained all the knowledge you need to start building interactive dashboards.What you will learn Find out how to visualize data using Grafana Understand how to work with the major components of the Graph panel Explore mixed data sources, query inspector, and time interval settings Discover advanced dashboard features such as annotations, templating with variables, dashboard linking, and dashboard sharing techniques Connect user authentication to Google, GitHub, and a variety of external services Find out how Grafana can provide monitoring support for cloud service infrastructures Who this book is forThis book is for business intelligence developers, business analysts, data analysts, and anyone interested in performing time-series data analysis and monitoring using Grafana. Those looking to create and share interactive dashboards or looking to get up to speed with the latest features of Grafana will also find this book useful. Although no prior knowledge of Grafana is required, basic knowledge of data visualization and some experience in Python programming will help you understand the concepts covered in the book.

Visualize, analyze, and optimize your data with Grafana KEY FEATURES ● Explore AIOps monitoring with Grafana for optimized operations and proactive decision making. ● Discover how to conduct performance testing using Grafana. ● Master the art of designing Grafana dashboards and visualizations. DESCRIPTION Grafana, a popular open-source observability platform, provides robust tools for analyzing and visualizing data from diverse sources. If you are looking to unlock its full potential as a data visualization and monitoring platform, then this book is for you. This book offers a comprehensive insight into the capabilities of Grafana and empowers you to leverage this powerful tool to its fullest extent. It provides you with the knowledge and skills necessary to create impressive visualizations, establish dashboards, and optimize monitoring processes. The book will help you delve into various aspects of Grafana, including its interface, utilizing the Graph Panel for visualizing data, connecting it to data sources, organizing dashboards, harnessing advanced features, and exploring additional functionalities like Grafana Loki for log exploration and managing authorization and authentication. Furthermore, the book explores specific use cases such as blackbox exporter, synthetic monitoring, Kubernetes monitoring, AIOps monitoring, and maximizing Grafana plugins. It concludes by presenting best practices for working with Grafana and offering insights into setting up performance testing and engineering dashboards. By the end of the book, you will be equipped with the necessary knowledge and skills to unlock its full potential as a data visualization and monitoring platform. WHAT YOU WILL LEARN ● Learn how to create visually appealing dashboards and panels using Grafana. ● Gain the ability to track and optimize application performance, ensuring an enhanced user experience. ● Utilize Grafana to record and analyze system applications. ● Track and analyze unique metrics for customized performance monitoring insights. ● Set up Grafana alerts and email notifications to receive timely notifications about critical events and anomalies. WHO THIS BOOK IS FOR This book is suitable for professionals in DevSecOps, Performance Testing, Site Reliability, AIOps, MLOps, Platform, Development, and Test Engineering teams. TABLE OF CONTENTS 1. Introduction to Data Visualization with Grafana 2. A Tour of the Grafana Interface 3. An Introduction to the Graph Panel 4. Connecting Grafana to a Data Source 5. Visualizing Data in the Graph Panel 6. Creating Your First Dashboard 7. Visualization Panels in Grafana 8. Organizing Dashboards 9. Grafana Alerting 10. Working with Advanced Dashboard Features 11. Exploring Logs with Grafana Loki 12. Managing Authorization and Authentication 13. Blackbox Exporter 14. Synthetic Monitoring 15. Maximizing the Grafana Plug-in 16. Kubernetes Monitoring 17. Grafana Cloud 18. AIOps Monitoring 19. Dashboard Setup for Performance Testing and Engineering 20. Best Practices of Working with Grafana

The IoT developer's complete guide to building powerful dashboards, analyzing data, and integrating with other platforms Key Features • Connect devices, store and manage data, and build powerful data visualizations • Integrate Grafana with other systems, such as Prometheus, OpenSearch, and LibreNMS • Learn about message brokers and data forwarders to send data from sensors and systems to different platforms Book Description Grafana is a powerful open source software that helps you to visualize and analyze data gathered from various sources. It allows you to share valuable information through unclouded dashboards, run analytics, and send notifications. Building IoT Visualizations Using Grafana offers how-to procedures, useful resources, and advice that will help you to implement IoT solutions with confidence. You'll begin by installing and configuring Grafana according to your needs. Next, you'll acquire the skills needed to implement your own IoT system using communication brokers, databases, and metric management systems, as well as integrate everything with Grafana. You'll learn to collect data from IoT devices and store it in databases, as well as discover how to connect databases to Grafana, make queries, and build insightful dashboards. Finally, the book will help you implement analytics for visualizing data, performing automation, and delivering notifications. By the end of this Grafana book, you'll be able to build insightful dashboards, perform analytics, and deliver notifications that apply to IoT and IT systems. What you will learn • Install and configure Grafana in different types of environments • Enable communication between your IoT devices using different protocols • Build data sources by ingesting data from IoT devices • Gather data from Grafana using different types of data sources • Build actionable insights using plugins and analytics • Deliver notifications across several communication channels • Integrate Grafana with other platforms Who this book is for This book is for IoT developers who want to build powerful visualizations and analytics for their projects and products. Technicians from the embedded world looking to learn how to build systems and platforms using open source software will also benefit from this book. If you have an interest in technology, IoT, open source, and related subjects then this book is for you. Basic knowledge of administration tasks on Linux-based systems, IP networks and network services, protocols, ports, and related topics will help you make the most out of this book.

Implement the LGTM stack for cost-effective, faster, and secure delivery and management of applications to provide effective infrastructure solutions Key Features Use personas to better understand the needs and challenges of observability tools users Get hands-on practice with Grafana and the LGTM stack through real-world examples Implement and integrate LGTM with AWS, Azure, GCP, Kubernetes and tools such as OpenTelemetry, Ansible, Terraform, and Helm Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionTo overcome application monitoring and observability challenges, Grafana Labs offers a modern, highly scalable, cost-effective Loki, Grafana, Tempo, and Mimir (LGTM) stack along with Prometheus for the collection, visualization, and storage of telemetry data. Beginning with an overview of observability concepts, this book teaches you how to instrument code and monitor systems in practice using standard protocols and Grafana libraries. As you progress, you’ll create a free Grafana cloud instance and deploy a demo application to a Kubernetes cluster to delve into the implementation of the LGTM stack. You’ll learn how to connect Grafana Cloud to AWS, GCP, and Azure to collect infrastructure data, build interactive dashboards, make use of service level indicators and objectives to produce great alerts, and leverage the AI & ML capabilities to keep your systems healthy. You’ll also explore real user monitoring with Faro and performance monitoring with Pyroscope and k6. Advanced concepts like architecting a Grafana installation, using automation and infrastructure as code tools for DevOps processes, troubleshooting strategies, and best practices to avoid common pitfalls will also be covered. After reading this book, you’ll be able to use the Grafana stack to deliver amazing operational results for the systems your organization uses.What you will learn Understand fundamentals of observability, logs, metrics, and distributed traces Find out how to instrument an application using Grafana and OpenTelemetry Collect data and monitor cloud, Linux, and Kubernetes platforms Build queries and visualizations using LogQL, PromQL, and TraceQL Manage incidents and alerts using AI-powered incident management Deploy and monitor CI/CD pipelines to automatically validate the desired results Take control of observability costs with powerful in-built features Architect and manage an observability platform using Grafana Who this book is for If you’re an application developer, a DevOps engineer, a SRE, platform engineer, or a cloud engineer concerned with Day 2+ systems operations, then this book is for you. Product owners and technical leaders wanting to gain visibility of their products in a standardized, easy to implement way will also benefit from this book. A basic understanding of computer systems, cloud computing, cloud platforms, DevOps processes, Docker or Podman, Kubernetes, cloud native, and similar concepts will be useful.

Work seamlessly with production-ready machine learning systems and pipelines on AWS by addressing key pain points encountered in the ML life cycle Key FeaturesGain practical knowledge of managing ML workloads on AWS using Amazon SageMaker, Amazon EKS, and moreUse container and serverless services to solve a variety of ML engineering requirementsDesign, build, and secure automated MLOps pipelines and workflows on AWSBook Description There is a growing need for professionals with experience in working on machine learning (ML) engineering requirements as well as those with knowledge of automating complex MLOps pipelines in the cloud. This book explores a variety of AWS services, such as Amazon Elastic Kubernetes Service, AWS Glue, AWS Lambda, Amazon Redshift, and AWS Lake Formation, which ML practitioners can leverage to meet various data engineering and ML engineering requirements in production. This machine learning book covers the essential concepts as well as step-by-step instructions that are designed to help you get a solid understanding of how to manage and secure ML workloads in the cloud. As you progress through the chapters, you'll discover how to use several container and serverless solutions when training and deploying TensorFlow and PyTorch deep learning models on AWS. You'll also delve into proven cost optimization techniques as well as data privacy and model privacy preservation strategies in detail as you explore best practices when using each AWS. By the end of this AWS book, you'll be able to build, scale, and secure your own ML systems and pipelines, which will give you the experience and confidence needed to architect custom solutions using a variety of AWS services for ML engineering requirements. What you will learnFind out how to train and deploy TensorFlow and PyTorch models on AWSUse containers and serverless services for ML engineering requirementsDiscover how to set up a serverless data warehouse and data lake on AWSBuild automated end-to-end MLOps pipelines using a variety of servicesUse AWS Glue DataBrew and SageMaker Data Wrangler for data engineeringExplore different solutions for deploying deep learning models on AWSApply cost optimization techniques to ML environments and systemsPreserve data privacy and model privacy using a variety of techniquesWho this book is for This book is for machine learning engineers, data scientists, and AWS cloud engineers interested in working on production data engineering, machine learning engineering, and MLOps requirements using a variety of AWS services such as Amazon EC2, Amazon Elastic Kubernetes Service (EKS), Amazon SageMaker, AWS Glue, Amazon Redshift, AWS Lake Formation, and AWS Lambda -- all you need is an AWS account to get started. Prior knowledge of AWS, machine learning, and the Python programming language will help you to grasp the concepts covered in this book more effectively.

Get up to speed with Prometheus, the metrics-based monitoring system used by tens of thousands of organizations in production. This practical guide provides application developers, sysadmins, and DevOps practitioners with a hands-on introduction to the most important aspects of Prometheus, including dashboarding and alerting, direct code instrumentation, and metric collection from third-party systems with exporters. This open source system has gained popularity over the past few years for good reason. With its simple yet powerful data model and query language, Prometheus does one thing, and it does it well. Author and Prometheus developer Brian Brazil guides you through Prometheus setup, the Node exporter, and the Alertmanager, then demonstrates how to use them for application and infrastructure monitoring. Know where and how much to apply instrumentation to your application code Identify metrics with labels using unique key-value pairs Get an introduction to Grafana, a popular tool for building dashboards Learn how to use the Node Exporter to monitor your infrastructure Use service discovery to provide different views of your machines and services Use Prometheus with Kubernetes and examine exporters you can use with containers Convert data from other monitoring systems into the Prometheus format