Elasticsearch 7 and Elastic Stack - In Depth and Hands On!

Video description

Learn all the latest techniques to search, analyze, and visualize big data with Elasticsearch, Kibana, Logstash, and FileBeat

About This Video

  • Find about the different techniques used to import data into Elasticsearch
  • Learn how to analyze and visualize data in Elasticsearch using Kibana
  • Integrate Elasticsearch with other systems, such as Spark Kafka, relational databases, S3, and more

In Detail

Elasticsearch is gradually becoming an alternative to Hadoop, Spark, and Flink for many common data analysis and visualization requirements. It is fast, easy to understand, and above all easy to use. This course will help you to understand the Elasticsearch tool in detail.

The course starts with an introduction to Elasticsearch, guiding you through the installation process, and giving an overview of Hypertext Transfer Protocol (HTTP) and RESTful Application Programming Interfaces (APIs). Next, you will map and index data, and learn how to search structured data using Elasticsearch. Moving along, you will import data into your index with a script, client libraries, Logstash, and Apache Spark. You will also learn the concepts of aggregation, metrics, buckets, and Kibana. Next, you will analyze log data with the Elastic stack, and understand Elasticsearch operations, such as shards, heap sizing, monitoring, and snapshots. Towards the end, you will use the Elasticsearch service on the cloud, and set up Elasticsearch and Kibana on Kubernetes.

By the end of this course, you will develop the Elasticsearch skills needed for searching, analyzing, and visualizing big data.

Publisher resources

Download Example Code

Table of contents

  1. Chapter 1 : Installing and Understanding Elasticsearch
    1. Introduction
    2. Installing Elasticsearch [Step-by-step]
    3. Overview of Elasticsearch
    4. Introducing HTTP and RESTful APIs
    5. Elasticsearch Basics: Logical Concepts
    6. Term Frequency/Inverse Document Frequency (TF/IDF)
    7. Using Elasticsearch
    8. What's New in Elasticsearch 7?
    9. How Elasticsearch Scales?
    10. Quiz: Elasticsearch Concepts and Architecture
    11. Section 1 Wrap-up
  2. Chapter 2 : Mapping and Indexing Data
    1. Section 2 Introduction
    2. Connecting to Your Cluster
    3. Introducing the MovieLens Dataset
    4. Analyzers
    5. Importing a Single Movie through JavaScript Object Notation/Representational State Transfer (JSON/REST) API
    6. Inserting Many Movies at Once With Bulk API
    7. Updating Data in Elasticsearch
    8. Deleting Data in Elasticsearch
    9. [Exercise] Inserting, Updating, and Deleting a Movie
    10. Dealing with Concurrency
    11. Using Analyzers and Tokenizers
    12. Data Modeling and Parent/Child Relationships - Part 1
    13. Data Modeling and Parent/Child Relationships - Part 2
    14. Flattened Datatype
    15. Dealing with Mapping Extensions
    16. Section 2 Wrap-up
  3. Chapter 3 : Searching with Elasticsearch
    1. Section 3 Introduction
    2. "Query Lite" Interface
    3. JavaScript Object Notation (JSON) Search In-Depth
    4. Phrase Matching
    5. [Exercise] Querying in Different Ways
    6. Pagination
    7. Sorting
    8. More with Filters
    9. [Exercise] Using Filters
    10. Fuzzy Queries
    11. Partial Matching
    12. Query-Time Search-as-you-Type
    13. N-Grams - Part 1
    14. N-Grams - Part 2
    15. "Search-as- you- Type" Field Type
    16. Section 3 Wrap-up
  4. Chapter 4 : Importing Data into Your Index (Big or Small)
    1. Section 4 Introduction
    2. Importing Data with a Script
    3. Importing Data with Client Libraries
    4. [Exercise] Importing Data with a Script
    5. Introducing Logstash
    6. Installing Logstash
    7. Running Logstash
    8. Logstash and MySQL - Part 1
    9. Logstash and MySQL - Part 2
    10. Importing Comma Separated Values (CSV) Data with Logstash
    11. Importing JavaScript Object Notation (JSON) Data with Logstash
    12. Logstash and Simple Storage Service (S3)
    13. Parsing and Filtering Logstash with Grok
    14. Logstash Grok Examples for Common Log Formats
    15. Logstash Input Plug-ins -Part 1: Heartbeat
    16. Logstash Input Plug-ins -Part 2: Generator Input and Dead Letter Queue
    17. Logstash Input Plug-ins -Part 3: HTTP Poller
    18. Logstash Input Plug-ins -Part 4: Twitter
    19. Syslog with Logstash Deep Dive
    20. Elasticsearch and Apache Hadoop
    21. Elasticsearch and Kafka - Part 1
    22. Elasticsearch and Kafka - Part 2
    23. Elasticsearch and Apache Spark - Part 1
    24. Elasticsearch and Apache Spark - Part 2
    25. [Exercise] Importing Data with Spark
    26. Section 4 Wrap-up
  5. Chapter 5 : Using Aggregation
    1. Section 5 Introduction
    2. Aggregations, Buckets, and Metrics
    3. Histograms
    4. Time Series
    5. [Exercise] Generating Histogram Data
    6. Nested Aggregations - Part 1
    7. Nested Aggregations - Part 2
    8. Section 5 Wrap-up
  6. Chapter 6 : Using Kibana
    1. Section 6 Introduction
    2. Installing Kibana
    3. Playing with the Kibana Interface
    4. [Exercise] Exploring Data with Kibana
    5. Kibana Lens
    6. Kibana Management
    7. Elasticsearch SQL
    8. Using the Kibana Canvas
    9. Section 6 Wrap-up
  7. Chapter 7 : Analyzing Log Data with the Elastic Stack
    1. Section 7 Introduction
    2. Data Frame Transforms
    3. Filebeat and the Elastic Stack Architecture
    4. X-Pack Security
    5. Installing Filebeat
    6. Analyzing Logs with Kibana Dashboards
    7. [Exercise] Log Analysis with Kibana
    8. Section 7 Wrap-up
  8. Chapter 8 : Elasticsearch Operations
    1. Section 8 Introduction
    2. Choosing the Right Number of Shards
    3. Adding Indices as a Scaling Strategy
    4. Index Alias Rotation
    5. Index Lifecycle Management
    6. Choosing Your Cluster's Hardware
    7. Heap Sizing
    8. Monitoring
    9. Troubleshooting Common Issues
    10. Registering the 1st user
    11. Failover in Action - Part 1
    12. Index Design Changes
    13. Snapshots
    14. Snapshots Lifecycle Management
    15. Rolling Restarts
    16. Search Profiling
    17. Uptime Monitoring with Heartbeat
    18. Section 8 Wrap-up
  9. Chapter 9 : Elasticsearch in the Cloud
    1. Section 9 Introduction
    2. (Free Preview): Amazon Elasticsearch Service - Part 1
    3. Amazon Elasticsearch Service, Part 2
    4. The Elastic Cloud
    5. Section 9 Wrap-up
  10. Chapter 10 : Elasticsearch/Logstash/Kibana (ELK) on Kubernetes with Elastic Cloud on Kubernetes (ECK)
    1. Introducing Elastic Cloud on Kubernetes (ECK) and Setting Up a Cluster
    2. Setting up Elasticsearch and Kibana on Kubernetes and Installing Plug-ins
    3. Using Elastic Cloud on Kubernetes (ECK) Persistent Volumes and Setting Up a Multi-Node Elasticsearch Cluster
  11. Chapter 11 : You Made It!
    1. Wrapping Up

Product information

  • Title: Elasticsearch 7 and Elastic Stack - In Depth and Hands On!
  • Author(s): Frank Kane
  • Release date: November 2017
  • Publisher(s): Packt Publishing
  • ISBN: 9781788995122