Duration
4 days
Overview
Observability is an attribute of a system you build, much like usability, high availability, and stability. The goal of designing and building an observable system is to make sure that when it is running, operators responsible for it can detect undesirable behaviours and have actionable information to pin down the root cause in an effective manner. This instructor-led course provides a strong foundation on using Elastic to implement unified observability with a single platform. You will learn how to collect logs, metrics, uptime and APM data, and then ship them to a single datastore — Elasticsearch. You will also learn how unified observability data can be made even more actionable through machine learning and alerting, as well as easier to correlate data across different sources. Using Kibana, you will also explore how to visualize your observability data through an intuitive user interface. After completing this course, you will be well on your way to becoming an Elastic Certified Observability Engineer.
Audience
- Software Developers
- Software Engineers
- Data Architects
- System Administrators
- DevOps
Prerequisites
- No prior knowledge of the Elastic Stack required
- Working knowledge of Linux terminal commands and editors recommended
Requirements
- Stable internet connection
- Mac, Linux, or Windows
- Latest version of Chrome or Firefox (other browsers not supported)
- Disable any ad blockers and restart your browser before class
Outlines
Getting started
Learn why it is important to collect logs, metrics, traces, and uptime data as well as how Elastic Observability makes them actionable through machine learning and alerting, making it easier to find and fix issues before they impact your
customers. Also learn how you can use Discover to start exploring your observability data.
Structuring and processing data
Learn how to structure and process your unstructured data using Elasticsearch ingest nodes. You will learn how to use the dissect processor to structure your data before indexing it into Elasticsearch. You will also learn how to use ingest pipelines for converting, enriching and processing your data in any way you want.
Collecting logs and metrics
The Elastic Agent is a single, unified way to add monitoring of logs, metrics, and other data. Learn how to use the Elastic Agent along with Fleet and Integrations to collect logs and metrics from popular services and platforms.
Collecting APM data
Learn how Elastic APM can help monitor your applications and services in real time as well as which Elastic components
you need to collect traces, errors, performance metrics and user experience data. Discover how Elastic APM supports
distributed tracing and how it enables you to analyze performance throughout your microservice architecture all in one view.
Working with Observability data
Learn how to use the Logs app to monitor all of the log events flowing in from your servers and the Metrics app to visualize infrastructure metrics that help diagnose issues in your architecture. Also learn how to use the APM app to
monitor your software services and applications in real-time as well as visualize detailed performance information on your services, identify and analyze errors. And learn how you can use the User Experience app to quantify and analyze the perceived performance of your web applications.
Actionable observability data
Learn how extracting new insights from your observability data is as simple as clicking a button, making machine learning truly operational. Also learn how to use time series modeling to detect anomalies in your current data and forecast trends based on historical data as well as how to configure alerts on top of machine learning to get notified when anomalies are found in your observability data.
Visualizing observability data
Learn how to leverage Kibana as an observability visualization platform. You will learn to visualize your data in different ways through preconfigured and editable dashboards. You will also learn how to create your own dashboards and visualizations to combine different sources of data that provide you a more unified view of your observability data.
Managing observability data
Learn why and how data streams are well-suited for logs, metrics, traces, and other data. Also learn how index lifecycle management (ILM) is implemented through the hot-warm-cold-frozen architecture to automate how your data streams are managed over time. Finally, learn how to use snapshots to backup your data streams and how searchable snapshots let you use snapshots to search infrequently accessed and read-only data in a very cost-effective fashion.
Exam Details
Following the completion of the 3 hour exam the Grading team thoroughly reviews each result to ensure that all of the answers are properly submitted and credits (partial or full) are accounted for. Since the exams are all performance-based and each exam holds different criteria, they do not share a minimum passing grade nor number of questions. Each question can receive full, partial, or no credit, and not all questions are weighted the same. In order to maintain exam integrity standards and to make sure all of our students get a fair opportunity to prepare and pass the Elastic Certified Professional exams, they do not disclose solutions to mistakes made during the exam. They highly recommend using the resources of the course and lab materials, if you are a paid training subscriber enrolling in the practice exam and creating your own lab environment to further prepare.
To be fully prepared for the Elastic Certified Observability Engineer exam, candidates should be able to complete all of the following exam objectives with only the assistance of Elastic documentation:
Uptime
- Configure and run Heartbeat to determine the uptime of a process or service
- Use Heartbeat to determine if a service is reachable via ICMP, TCP or HTTP
- Use the Uptime app in Kibana to monitor the uptime and availability of a service
Metrics
- Install the Elastic Agent to collect metrics
- Enable and configure integrations to collect the metrics of a specific service
- Use the Metrics app in Kibana to analyze and answer questions about metrics collected in Elasticsearch
- Use the Metrics app to enable and analyze the predefined machine learning jobs
Logging
- Install the Elastic Agent to collect logs
- Enable and configure integrations to collect the logs from a specific service
- Enable and configure integrations to tail a given custom log file
- Use the Logs app in Kibana to analyze and answer questions about log events collected in Elasticsearch
- Use the Logs app to enable and analyze the predefined machine learning jobs
APM
- Use the APM app in Kibana to analyze and answer questions about APM data collected in Elasticsearch
- Use the Real Experience app in Kibana to analyze and answer questions about RUM data collected in Elasticsearch
- Edit and configure the APM integration
Structuring and Processing Data
- Use Kibana to edit or define an ingest node pipeline
- Configure the custom logs integration to use an ingest pipeline
- Define ingest node pipelines that use the various processors, including (but not limited to) append, convert, date, dissect, dot expander, geoip, grok, fail, json, remove, rename, set, and split
Working with Observability Data
- Find anomalies in Observability data using the predefined machine learning jobs in Kibana
- Define a machine learning job in Kibana on Observability data
- Define or edit an Index Lifecycle Management policy for indices
- Define an alert using Kibana Alerts
- Create dashboards that use visualizations based on Observability data
Programme scope, delivery arrangements and suitability are confirmed during enquiry.