Elasticsearch 6 and Elastic Stack - In Depth and Hands On!
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Elasticsearch 6 and Elastic Stack - In Depth and Hands On!

Use data from any source, in any format, and search, analyze, and visualize it in real time with reliability & security
3.7 (37 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
245 students enrolled
Created by Packt Publishing
Last updated 9/2018
English [Auto]
Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 12 hours on-demand video
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Install and configure Elasticsearch on a cluster
  • Develop an understanding of Elasticsearch dichotomy, Elasticsearch APIs, and how to interact with a cluster via API
  • Dive deeper into Elasticsearch interactions with filers, ranges, matches along with aggregations
  • Probe Elastic Stack and use Kibana, Logstash and filebeats, to develop a pipeline to get data from an external source into Elasticsearch
  • Build an Elasticsearch application
  • Learn how to use Kibana to visualize data and tell data stories in real-time
  • Learn the skills required to instantly search petabytes of data and provide amazing customer interactions
  • Leverage HTTP-based APIs for ElasticSearch insert, query, and configure operations
Course content
Expand all 100 lectures 11:55:28
+ Learning ElasticSearch 6
34 lectures 03:02:26

This video will give you an overview about the course. 

Preview 05:49

The purpose of this video is to better understand Elasticsearch as a technology.

  • Define Elasticsearch

  • Identify key features of Elasticsearch

  • Give examples of how Elasticsearch is being used in industry

What is Elasticsearch?

In this video, we will develop a general understanding of how Elasticsearch works.

  • Define Elasticsearch as a search and analytics engine

  • Gain understanding of how Elasticsearch delivers near real-time results

  • Take a quick look at DSL

Goals of Elasticsearch

In this video, we will take a detailed look at how to install Elasticsearch core technologies: Elasticsearch and Kibana.

  • Learn how to install Elasticsearch

  • Learn how to install Kibana

  • Live walkthrough for installing Elasticsearch and Kibana

Installing Elasticsearch

In this video, we will understand indices in Elasticsearch.

  • Define Elasticsearch index

  • Compare Elasticsearch to relational DBs

  • Demonstrate how to add and delete an index

What is an Index?

In this video, we will look at documents in Elasticsearch.

  • Define documents in Elasticsearch

  • Define field and type

  • Live demonstration of adding and deleting documents

Docs in Elasticsearch

In this video, we will learn about clusters in Elasticsearch.

  • Define a cluster

  • Define a node/instance

  • Demonstrate how to start an Elasticsearch instance

What is a Cluster?

In this video, we will understand shards and replicas.

  • Detailed explanation of shards and how they work

  • Detailed explanation of replicas and how they work

  • Demonstrate how to set shards and replicas in Elasticsearch

Shards and Replicas

In this video, we will develop an understanding of Bulk API in Elasticsearch.

  • Define Bulk API

  • Show example of Bulk API

  • Demonstrate how to use the Bulk API in Elasticsearch

Bulk API

In this video, we will develop a general understanding of RESTful API.

  • Define RESTful API

  • Look at HTTP verbs used in RESTful API

  • Breakdown the dichotomy of a request string

Introduction to RESTful API

In this video, we will learn how to run search queries with RESTful API.

  • Quickly look at the sample movie index and use _count to get number of documents

  • Run term query across movie index

  • Run match query across movie index

Searching with RESTful API
Updating with RESTful API

In this video, we will understand Domain Specific Language in Elasticsearch.

  • Understand leaf and compound queries

  • Look at different types of queries

  • Demonstrate queries using DSL

Introduction to DSL

In this video, we will understand how context, relevancy and score work in Elasticsearch.

  • Understand query clauses

  • Understand relevancy in Elasticsearch

  • Learn about the _score in Elasticsearch

DSL — Context, Relevancy, and Score

In this video, we will learn about exists queries in Elasticsearch.

  • Define exists query

  • View example of an exists query

  • Demonstrate how to run exists queries in Elasticsearch

Exists Query

In this video, we will develop a general understanding of RESTful API.

  • Define elastic stack

  • Detailed breakdown of the elastic stack makeup

  • Look at why using the elastic stack is beneficial

Introduction to Elastic Stack

In this video, we will understand Kibana.

  • Define Kibana

  • Look at how Kibana fits into the Elastic Stack

  • Go through detailed walk through of Kibana installation and setup


In this video, we will understand Logstash.

  • Look at a Logstash pipeline

  • Learn how to configure Logstash

  • Demonstrate installation and configuration of Logstash


In this video, we will understand X-Pack.

  • Define X-Pack

  • Look at how to install and configure X-Pack on services

  • Demonstration of installing and configuring X-Pack


In this video, we will understand light-weight data shippers in Elasticsearch.

  • Define beats

  • Look at the different beats shippers

  • Take a quick look at how to setup/configure Filebeat


In this video, we will learn all the steps to preparing for secure log analysis.

  • Get a walkthrough of installing X-Pack and setting passwords

  • Understand how to configure Logstash and kibana yml files

  • Learn how to configure .conf and filebeat.yml

Prep for Log Analysis

In this video, we will learn how to secure Elasticsearch by example.

  • Get a walkthrough installing X-Pack on Elasticsearch, Kibana, and Logstash

  • Setup password for all services using interactive or auto

  • Demonstrate configuring kibana.yml and logstash.yml with password information 

Securing Elasticsearch with X-Pack

In this video, we will learn how to configure .conf and filebeat.yml by example.

  • Get a walkthrough confirming log data

  • Learn how to configure filebeat.yml

  • Learn how to configure .conf logstash pipeline file by example

Building the Pipeline

In this video, we will learn how to run a secure pipeline for log analysis by example.

  • Start up the necessary services: Elasticsearch and Kibana

  • Learn and use command to start Filebeat

  • Test and then start pipeline by executing required Logstash command

Running the Pipeline

In this video, we will learn how term, range and boosting queries add power to Elasticsearch.

  • Define term, range and boosting queries

  • View examples of term, range and boosting queries

  • Demonstrate how to perform term, range, and boosting queries in Elasticsearch

Term, Range, and Boosting

In this video, we will learn how aggregations in Elasticsearch provide turnkey analytics.

  • Look at metrics and bucket aggregations

  • Show detailed examples of metrics aggregations

  • Demonstrate how to do average and extended stats aggregations in Elasticsearch

Aggregation Based Analytics

In this video, we will learn how geo aggregations work in Elasticsearch.

  • Define geo searching

  • Look at the different types of geo searches: distance, distance range and sorting

  • Get detailed explanations for distance, distance range and sorting in Elasticsearch

Aggregation Based Analytics (Continued)

In this video, we will learn how to run geo queries by example.

  • Setup index template to facilitate IP searches after log analysis run

  • Run example of geo distance query

  • Run example of geo sorting query

Geo Query by Example

In this video, we will learn about sorting in Elasticsearch and why it’s useful.

  • Look at the importance of sorting in Elasticsearch

  • Learn about basic sorting and the use of sort mode

  • Demonstrate sorting in Elasticsearch

Sorting in Elasticsearch

In this video, we will learn about the power of synonyms in Elasticsearch.

  • Define synonyms

  • Look at simple expansions versus simple contractions

  • Understand how the process of analysis effects synonym

Synonyms in Elasticsearch

In this video, we will get an overview of machine learning.

  • Define machine learning

  • Learn some use cases for machine learning

  • Understand how machine learning works

Intro to Machine Learning

In this video, we will learn how Elasticsearch interfaces with machine learning.

  • Learn how to get data into an Elasticsearch cluster for ML

  • Understand the different types of machine learning jobs

  • Learn to train machine learning models with Elasticsearch

Elasticsearch and Machine Learning

In this video, we will perform a step-by-step walkthrough of machine learning in Elasticsearch.

  • Simulate data to perform machine learning jobs

  • Learn to do single metric machine learning job

  • Evaluate and understand results

Machine Learning Walkthrough

In this video, we will look at how to get the data that we use throughout this course.

  • Learn how to restore snapshot

  • Simulate log data

  • Include access.log file

Supplemental — Getting Data
Learning Elasticsearch 6
4 questions
+ Learning Elastic Stack 6.0
29 lectures 02:47:42

This video gives glimpse of the entire course. 

Preview 03:33

Elasticsearch is at the core of Elastic Stack, playing the central role of a search and analytics engine. Elasticsearch is built on a radically different technology, Apache Lucene.

  • Look at key benefits of using Elasticsearch

What is Elasticsearch, and Why Use It?

Some Elastic stack components are general purpose and they can be used outside of Elastic Stack without using any of the other components. In this video we will look at the purpose of each component and how they fit in the stack.

  • Understand the function and application of each component

Exploring the Components of Elastic Stack

In this video we will downloading and installing the key components. Precisely, we will download and install Elasticsearch and Kibana 

Downloading and Installing

Before we start writing our first queries to interact with Elasticsearch, we should familiarize ourselves with a very important tool – Kibana Console.

  • Send the query GET

  • Continue working on it

Using the Kibana Console UI

Elasticsearch supports a wide variety of data types for supporting different scenarios. We will also look at mappings.

  • Create an index with name catalog and define mappings for type of product

  • Look at core, complex, and other data types

Mappings and Data Types

In this video, we will look at how to perform basic CRUD operations, which are the most fundamental operations required by any data store.

  • Look at Index, Get, Update and Delete API

CRUD Operations

You would want to control how indices are created and also how mapping is created. We will see how you can take control of this process in this video.

  • Create an index

  • Create type mapping in an existing index

  • Update mapping

Creating Indexes and Taking Control of Mapping

The APIs that deal with Elasticsearch are categorized into some types. We will look at them and work with indexing.

  • Format the JSON response

  • Deal with multiple indices

REST API Overview

Logstash allows us to easily build a pipeline that can help in collecting data from a wide variety of input sources, and parse, enrich, unify, and store it in a wide variety of destinations. In this video, we will look at salient features of logstash and Download and install Logstash.

  • Look at salient features

  • Installation and configuration


In this video, we will explore about Logstash pipeline in detail and with code example.

After that we will learn several types of plugins.

  • Understand the Logstash Architecture using the pipeline diagram

  • Installing or updating Logstash plugins

Architecture and Overview of Logstash Plugins

An input plugin is used to configure a set of events to be fed to Logstash. This video will help you with some of the most commonly used input plugins in detail.

Exploring Input Plugins

Output plugins allow one to configure single or multiple output sources. This video will walk through some of the most commonly used output plugins in detail.

Output Plugins

In this video, we will look at the type of aggregations and learn how they work.

  • Look at bucket, metric, matrix aggregations

The Basics of Aggregations

Metric aggregations work with numeric data, computing one or more aggregate metrics within the given context. Let’s see more about them

  • Work with sum, average, min, and max aggregations

Metric Aggregations

Sometimes, we may need to bucket the data or segment the data based on a field that has a string datatype, typically keyword typed fields in Elasticsearch.

Another common scenario is when we want to segment or slice the data into various buckets based on a numeric field. We will learn both in this video.

  • Perform terms aggregation for string

  • Perform histogram and range aggregation on numeric data

Bucketing on String and Numeric Data

Elasticsearch has a very powerful Date Histogram aggregation. We will bucket on date/time data using that

  • Create buckets across time. Use a different time zone

  • Compute other metrics within sliced time intervals

  • Focus on a specific day and changing intervals

Bucketing on Date/Time Data

Another powerful feature is the ability to do geo-spatial analysis on the data. Let’s see how to do that in this video

  • Look at Geo distance and GeoHash grid ggregation

Bucketing on Geo-Spatial Data

One of the important processes of Logstash is converting unstructured log data into structured data, which helps in searching for relevant information easily and also assists in analysis. In this video we will explore some common filter plugins used for transformation.

  • Understand the need to parse and enrich logs using logstash

  • Look at the types of filter plugins

Parsing and Enriching Logs Using Logstash

Beats are lightweight data shippers that are installed as agents on edge servers to ship operational data to Elasticsearch. In this video, we will look at some of the commonly used beats by Elastic.co in detail


As Kibana is all about gaining insight from data, let's load some sample data that we will use as we follow the tutorial. Before that, we will also configure Kibana.

  • Configure Kibana

  • Create apache.conf. Start the Logstash

  • Verify total number of documents indexed

Getting Started with Kibana

Before you can start working with data and creating visualizations to analyze the data, Kibana requires you to configure the index pattern. That’s what we will see in this video.

  • Look at time series and regular indexes

  • Type logstash–* in index name

  • Create @timestamp time filter field name

Kibana UI

The Visualize page helps to create visualizations in the form of graphs, tables, and charts, thus assisting in visualizing all the data that has been stored in Elasticsearch easily.

  • Work with Kibana aggregations

  • Create a visualization


In this video, we will see how different visualizations are used to perform functions.

  • Create visualization to find response codes and top 10 URLs

  • Find bandwidth usage of top five countries over time and web traffic originating from different countries

  • Find the most used user agent

Visualizations in Action

Dashboards help one bring different visualizations into a single page.

  • Create a dashboard

  • Save the dashboard

  • Clone and share the dashboard


Timelion is a visualization tool for analyzing time-series data in Kibana. Plugins are a way to enhance the functionality of Kibana. Let’s get to know them better here

  • Understand timeline UI and timeline expressions

  • Install and remove plugins

Timelion and Plugins

We have understood what the application is about and what the data represents. As we start developing the application, we will start the solution from the inside out. So, we will start defining our solution from the very heart of it by first building the data model in Elasticsearch

  • Define an index template

  • Understand mapping

  • Setup metadata database

Modeling data in Elasticsearch

The sensor_metadata database is ready to look up the necessary sensor metadata. In this video, let us build the Logstash data pipeline by performing following steps.

  • Accept JSON requests over the web

  • Store resulting documents in Elasticsearch

  • Senddata to Logstash over HTTP

Building the Logstash Data Pipeline

We have successfully setup the Logstash data pipeline and also loaded some data using the pipeline into Elasticsearch. It is time to explore the data and build a dashboard that will help us gain some insights into the data.

  • Set up an index pattern in Kibana

  • Build visualizations for different scenarios

Visualizing the Data in Kibana
Learning Elastic Stack 6.0
5 questions
+ Mastering ElasticSearch 6.x and the Elastic Stack
37 lectures 06:05:20

This video gives an overview of the entire course. 

Preview 06:01

In this video, we’ll look at the target that we want to build within Elasticsearch and Kibana.

  • We need to get data and visualize it

  • Start installing components and configuring connectivity

  • Review the data

Overview of a Final Working Solution – This Is What We're Working Towards

From the beginning, we don’t have an Elasticsearch node running, let’s set one up.

  • Cover all the information needed to download and install ES

  • Configure ES to be usable for our demos

  • Verify a running ES node

Install and Configure Elasticsearch

Now that we have the system to store our data, we need to be able to visualize it.

  • Cover the information on how to download and install Kibana

  • Configure Kibana to be usable and connect to ES

  • Verify configuration by seeing a configuration screen in Kibana

Install and Configure Kibana

Before we start using ES and Kibana, we need to be able to validate the health of our system from the beginning.

  • Configure ES and Kibana by installing X-Pack

  • Configure ES and Kibana to use monitoring, but turn off security for now

  • Dig into the monitoring section

Enable Monitoring via X-Pack for Elasticsearch and Kibana

Our ES node has no other data besides monitoring, learn how to fix that.

  • Determine what data will look like

  • Use an HTTP API to insert documents

  • Find those documents in Kibana

Loading Example Data in Elasticsearch

We’ve seen how we can insert data into ES, but we need to understand more about that process to be effective.

  • Documents are the foundation of data within ES

  • Insert, update and delete documents into ES a few ways

  • Finish up by retrieving the documents back

How Do We Store and Group Data – Documents

Understand what options do we have for storing data within a document.

  • Review various data types within ES

  • Configure ES to be able to index and store our docs

  • Validate documents and mappings

Specifying Document Attributes – Data Types

Pick up on more details to how we classified data.

  • Review a mapping file

  • Insert mapping file into ES

  • Verify mappings of our documents

Classifying Similar Documents Types

We’ve started inserting documents, but we need to learn how we can arrange groups of documents.

  • Find example data to insert for different use cases

  • Setup indexes for ‘reference’ data as well as time based data

  • Alias, reindex and delete indexes via APIs

Organizing and Grouping Documents Indexes

We’re on a roll, we have all kinds of data and options to put data in, but, we need to be familiarised with how we get data back.

  • Insert new data and perform basic queries

  • Explore filtering and searching via different API

  • Look at aggregations and buckets

Give Me My Data Back – Searches

Kibana can be overwhelming at first, there are so many components that you need to understand before you can decide how to use it.

  • First we’ll browse through the user interface

  • We’ll cover various components that we are going to use for searching

  • Get ready to search

Kibana Is Huge – Let’s Take It Apart

Kibana needs to know how your data is stored within ES. It can auto discover a lot of things, but you need to start by telling Kibana what indexes to use.

  • Create an index pattern

  • Determine what ‘regex’ to use depending on your use case

  • See how index patterns appear in the search tab

Configuring Groups of Data for Querying – Index Patterns

In this video, we will understand that If you’re looking at time based data, you’ll have specific searching needs.

  • Walk through an overview of how time ranges are selected

  • Define different time range selection options

  • Use the built in interface to change time ranges

Time Range Queries – Slicing and Dicing

Learn how Kibana provides different ways to search for specific data.

  • First we’ll look into using the search bar

  • Then we’ll use the top fields aggregations to discover data without typing in queries

  • Modify and manipulate searches on the fly

Searches – Refining Queries to Find the Needle in the Haystack

Kibana provides different ways to save and share queries

  • Learn how to Save Queries

  • Understand all about Loading queries

  • Share and report queries

Saving and Sharing – Let Your Friends Know What You Found

Logstash is the primary tool for getting data into ES, we need to learn all about it.

  • Dive into the configuration for a pipeline in ES

  • Execute a few pipelines

  • Determine how to use this for real logs

Logstash – Introduction

After we configure Logstash to send data to ES, learn how to make sure our configuration is doing what we think it’s doing.

  • Find the logstash pipeline viewer in Kibana

  • Explain what each component of the pipeline viewer means

  • Figure out how we can use this in the real world

Visualizing Pipelines in Kibana

Understand how Logstash can do a lot more than just read data and write it to ES.

  • Look at the configuration file and determine how to make changes

  • Apply different changes to the data

  • Verify all of our data changes in ES

Process Documents with Filters

It’s really easy to have Logstash read files from a local system. Learn how can we get distributed data

  • Look at different options to receive data

  • Configure logstash to receive data over the network

  • Verify data was making it to ES/Kibana

Get Data into Logstash as a Server

Understand that Logstash is quite a heavy application, and in the shifting paradigm of micro services and Docker containers, Logstash may be too heavy.

  • We’ll look at what Beats is and how we can use it

  • Walk through the various beats packages

  • Make sure they all work in our setup

Working with Beats

We’re heavily focused on ES and Kibana (obviously, since this is an Elastic Stack class!), but now you will learn what else can Logstash do.

  • Look through a list of potential outputs

  • Configure and monitor logstash outputs

  • Figure out what you need

Outputs – Where Else Can Data Go

Learn that Kibana can do a lot more than just an interactive search console.

  • Break down different visualizations

  • Pick the right chart for the data you have

  • Create and save visualizations

Impress Your Boss with Charts and Graphs

In this video, we will learn how do we put all of our visualizations together.

  • Create a new dashboard

  • Add visualizations to the dashboard

  • Rearrange them to make it tell a great story

Building a Heads Up Dashboard

You’ve made the best dashboard anyone has ever seen. Now learn how you can show it off to the world.

  • Create, save and load a dashboard

  • Share via a link

  • Generate and download a report to email around

Sharing Dashboards Recap

We setup monitoring in the very first section. Remember? Even if you do, now that you have a lot more context, let’s dig deep.

  • Find your cluster information

  • Look at index status

  • Monitor all the things

Use Kibana to Monitor the Health of Your Elastic Stack

Our ES and Kibana are configured how we want them. Learn to now restrict access

  • Configure and enable security

  • Add users and roles

  • Authenticate

Security – Provide Authentication and Authorization to Kibana

If we’re tracking events, like network events, there are tons of relationships. Learn to figure them out in this video

  • Find the graph module

  • Get some data visualized

  • Pivot to other data

Graph – Use the Built in Graph Interface to Pivot Around Data

Computers are good at alerting when certain conditions are programmed. Learn how can the Elastic Stack automatically determine limits

  • Configure a machine learning job

  • Load data and back analyze it

  • Dig into anomalies

Machine Learning Is Hot, See How Elastic Facilitate0073

We’ve monitored it all. Files, networks and system metrics. Learn to get this visibility into our applications

  • Install and configure an APM node

  • Wire up an application with the client

  • Look at all the fantastic data in your Python or Node application

Application Perfromance Monitoring – APM

Continue your journey with APM

  • Get a hands on feel of APM

Application Performance Monitoring – APM (Continued)

My favorite use case for the Elastic Stack is pulling in data over time. How can we analyze it effectively?

  • Find timelion in the menu

  • Start using your data over time

  • Chain, custom colors, and external data

Timelion – Time Series

Our Elasticsearch node has been yellow, and that’s OK, Learn how to we fix that and make our data safer.

  • Learn about shards

  • Learn about replica’s

  • Learn how to plan shard distribution across Elasticsearch cluster

Indexes, Shards, and Replicas

In this video, we add nodes to our Elasticsearch cluster

  • Add nodes

  • Add another node, and watch your data get spread across your cluster

  • Realize the safety and performance increases you just unlocked

Adding Elasticsearch Nodes to increase query and indexing performance

If your node is really important, let’s look at master nodes to keep them safe.

  • Learn what a master node is

  • Avoid split brain syndrome

  • Size your master nodes appropriately

Use Master Nodes to control the cluster

Learn how to configure different node types in the configuration

  • Differentiate a master only node from an all purpose node

  • Set up data only nodes for heavy work loads

  • Plan a cluster with both master and data nodes

Master and Data Nodes – Sizing Your Cluster

In this video, we see how Kibana provides different ways to search for specific data.

  • First we’ll look into using the search bar

  • Then we’ll use the top fields aggregations to discover data without typing in queries

  • Modify and manipulate searches on the fly

SaaS offerings of Elasticsearch at AWS and Elastic Cloud
Mastering ElasticSearch 6.x and the Elastic Stack
5 questions
  • No prior knowledge of Elasticsearch or Elastic Stack required, however fundamental knowledge of JSON would be helpful.

Elasticsearch is a powerful tool not only for powering search on big websites, but also for analyzing big data sets in a matter of milliseconds! It's an increasingly popular technology, and a valuable skill to have in today's job market. If you’re a technologist who wants to add Elasticsearch to their tool chest for searching and analyzing big data sets, then go for this Learning Path.

This  comprehensive 3-in-1 course is a hands-on guide to using Elasticsearch in conjunction with Elastic Stack, to search, analyze, and visualize data with Elasticsearch, Logstash, Beats, Kibana, and more. You will gain a firm understanding of all the fundamentals of Elasticsearch 6 to build efficient search and analytics applications using Elasticsearch 6. You will also learn what Elastic stack is all about, and how to use it efficiently to build powerful real-time data processing applications. This course covers each and every concept of ElasticSearch and Elastic Stack with the help of practical examples making it easy for you to understand and implement in your own applications.

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Learning Elasticsearch 6, begins with explaining you what is Elasticsearch, what is it used for, and why is it important. You will then be introduced to the new features of Elasticsearch 6 and its fundamental components such as indices, documents, nodes and clusters, all which form the dichotomy of Elasticsearch. You will also learn how to add more power to your searches using filters, ranges, and more. Next, you will explore how Elasticsearch can be used with the other components of the Elastic Stack such as LogStash, Kibana, and Beats, to get data into an Elasticsearch cluster. Finally, you will develop a Elasticsearch application.

In the second course, Learning Elastic Stack 6.0, after a quick overview of the newly introduced features in Elastic Stack 6, you'll learn how to set up the stack by installing the tools, and explore their basic configurations. You will then demonstrate the creation of custom plugins using Kibana. You will also get some useful tips on how to use the Elastic Cloud and deploy the Elastic Stack in production environments.

The third course, Mastering ElasticSearch 6.x and the Elastic Stack, focuses on two major use cases with Elasticsearch. The first use case is on leveraging the powerful full-text search engine ElasticSearch is built on, allowing developers to add blazingly fast search features to applications. The second use case is on leveraging different components of the Elastic Stack to continuously monitor applications, infrastructure, or even customer transactions.

By the end of this Learning Path, you will be well-versed with the concepts of Elasticsearch and Elastic Stack to build complete, open source solutions for storing, managing, analyzing, and visualizing structured and unstructured data.

Meet Your Expert(s):

We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:

  • Ethan Anthony is a San Francisco based Data Scientist who specializes in distributed data-centric technologies. He is also the Founder of XResults, where the vision is to harness the power of big data to deliver intuitive customer-facing solutions, largely to non-technical professionals. Ethan is Harvard-educated in the areas of data science and software engineering. He began using Elasticsearch in 2012 and delivered solutions based on the Elastic Stack to a broad range of clientele. Ethan has also consulted globally with firms in a cross-section of industry verticals, from the U.S. to the Far East.

  • Pranav Shukla is the founder and CEO of Valens DataLabs, a technologist, husband, and father of two. He is a big data architect and software craftsman who uses JVM-based languages. Pranav has diverse experience of over 14 years in architecting enterprise applications for Fortune 500 companies and startups. His core expertise lies in building JVM-based, scalable, reactive, and data-driven applications using Java/Scala, the Hadoop ecosystem, Apache Spark, and NoSQL databases. He is a big data engineering, analytics, and machine learning enthusiast. Pranav founded Valens DataLabs with a vision to help companies leverage data to their competitive advantage. Valens DataLabs specializes in developing next-generation, cloud-based, reactive, and data-intensive applications using big data and web technologies. The company believes in agile practices, lean principles, test-driven and behavior-driven development, continuous integration, and continuous delivery for sustainable software systems.

  • Sharath Kumar M N has done his masters in Computer Science at The University of Texas, Dallas, USA. He has been in the IT industry for more than ten years now and is the Elasticsearch Solutions Architect at Oracle. He is an Elastic Stack advocate, and being an avid speaker he has also given several tech talks in conferences such as the Oracle Code Event. Sharath is a certified trainer—Elastic Certified Instructor—one of the few technology experts in the world who has been certified by Elastic Inc to deliver their official from the creators of Elastic training. He is also a data science and machine learning enthusiast. In his free time, he enjoys trekking, listening to music, playing with his lovely pets Guddu and Milo and the geek in him loves exploring his Python skills for stock market analysis.

  • Chris Fauerbach is an active technical expert in the area of cybersecurity and the Elastic Stack. As a seasoned software engineer, he's built multiple commercial products on the Elastic Stack and has a passion for teaching. Chris continues to research new technologies and explore new ways to solve problems. As he has become an expert in his field, he has been focusing primarily on writing and teaching.

Who this course is for:
  • This learning path is for developers, architects, data professionals, and system administrators who want to add Elasticsearch to their toolchest for searching, analyzing, and visualizing data sets.