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Complete ElasticSearch with LogStash, Hive, Pig, MR & Kibana

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Complete ElasticSearch with LogStash, Hive, Pig, MR & Kibana, available at $19.99, has an average rating of 2.75, with 148 lectures, 11 quizzes, based on 74 reviews, and has 565 subscribers.

You will learn about Advance your career in Big data by learning how to integrate ElasticSearch on Hadoop Ecosystem and create real world data pipelines for your big data applications. Develop sound understanding of data ingestion, integration across systems, full text search & data analytics. Develop in demand skills for building Data Pipelines using Apache Hive, Pig, MapReduce (Java), & LogStash to index data into ElasticSearch clusters for quick data analytics and text searching. Advance your career by applying for an awesome job with great pay. Develop awesome data visualizations using Kibana. This course is ideal for individuals who are This will be an excellent course for anyone who wants to learn about Big Data technologies and how to use them together in order to create amazing Big Data applications. or Big Data Developers, Architects, Data Scientists, Data Analysts and Students (with zero experience) It is particularly useful for This will be an excellent course for anyone who wants to learn about Big Data technologies and how to use them together in order to create amazing Big Data applications. or Big Data Developers, Architects, Data Scientists, Data Analysts and Students (with zero experience).

Enroll now: Complete ElasticSearch with LogStash, Hive, Pig, MR & Kibana

Summary

Title: Complete ElasticSearch with LogStash, Hive, Pig, MR & Kibana

Price: $19.99

Average Rating: 2.75

Number of Lectures: 148

Number of Quizzes: 11

Number of Published Lectures: 148

Number of Published Quizzes: 11

Number of Curriculum Items: 159

Number of Published Curriculum Objects: 159

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Advance your career in Big data by learning how to integrate ElasticSearch on Hadoop Ecosystem and create real world data pipelines for your big data applications.
  • Develop sound understanding of data ingestion, integration across systems, full text search & data analytics.
  • Develop in demand skills for building Data Pipelines using Apache Hive, Pig, MapReduce (Java), & LogStash to index data into ElasticSearch clusters for quick data analytics and text searching.
  • Advance your career by applying for an awesome job with great pay.
  • Develop awesome data visualizations using Kibana.

Who Should Attend

  • This will be an excellent course for anyone who wants to learn about Big Data technologies and how to use them together in order to create amazing Big Data applications.
  • Big Data Developers, Architects, Data Scientists, Data Analysts and Students (with zero experience)

Target Audiences

  • This will be an excellent course for anyone who wants to learn about Big Data technologies and how to use them together in order to create amazing Big Data applications.
  • Big Data Developers, Architects, Data Scientists, Data Analysts and Students (with zero experience)

Complete ElasticSearch tutorial for beginners to advanced level professionals.

Learn how to use ElasticSearch with Apache Hadoop and build various real world big data applications.

This comprehensive course focuses on building real world like data applications to move data from one system to another. A common practice for any data engineer today. No other course can cover so much ground as you will do in this one.

In this course you will learn:

Section 1 – Ingestion Flows (Hadoop to ElasticSearch)

In this section of the course, you will learn to move data from various Hadoop applications (such as Hive, Pig, MR) & LogStash into an ElasticSearch index. This is an ideal business use case to prepare data for business analytics. Here are four major topics that will be covered in this section of the course:

  • Learn how to install Apache Hive on your computer. Then read data from a hive table and load it into ElasticSearch

  • Learn how to install Apache PIG on your computer and index data into ElasticSearch using Apache PIG

  • Create a MapReduce program (Java code) and load data into an ElasticSearch index

  • Learn how to move data using LogStash into an ElasticSearch index

Section 2 – Egression Flows (ElasticSearch to Hadoop)

In this section of the course, you will learn to use indexed data from an ElasticSearch cluster and load it back into Hadoop cluster. After data is loaded back into Hadoop, you will learn how to directly import it into Hive, Pig, M/R or LogStash. Here are four major topics that we will cover under this section:

  • Learn how to import an ElasticSearch index directly into Apache Hive table

  • Learn how to import an ElasticSearch indexed data into Hadoop using Apache PIG scripts

  • Learn how to import an ElasticSearch indexed data into Hadoop using Java MapReduce program

  • Learn how to import an ElasticSearch indexed data using LogStash application

Section 3 – Data Visualization (Business Intelligence)

In part of the course, you will learn how to use indexed data from an ElasticSearch cluster and create dynamic dashboards using Kibana.

This will be a very important lesson for Data Analysts and Data Scientists.

Section 4 – Production Cluster Monitor tool (Administration)

No knowledge is complete without learning how to maintain an application in production. In this section of the course, you will learn how to monitor your ElasticSearch cluster using Marvel plugins. Here are few things that you will learn:

  • Cluster Health monitoring at Index, Shard, Node levels

  • Parsing ElasticSearch Cluster statistics using Linux utilities

  • Setting up wait-for-trigger mechanism and much more

Section 5 – Searching an ElasticSearch Index

  • Learn about awesome search capabilities offered by ElasticSearch

  • How to search something from an ElasticSearch index in real time.

We will cover lots of basics to build foundation required to understand ElasticSearch. You will also learn about behind the scenes on how a search engine and specifically ElasticSearch works in a single or multiple node cluster.

You will also get step by step instructions for installing all required tools and components on your machine in order to run  all examples provided in this course. Each video will explain entire process in detail and easy to understand manner.

You will get access to working code for you to play with it and expand on it. All code examples are working and will be demonstrated in video lessons.

Windows users will need to install virtual machine on their PC to setup single node hadoop cluster. Detailed Instructions are available inside the course.

Course Curriculum

Chapter 1: Building Foundation

Lecture 1: Introduction

Lecture 2: Who we are – DataShark Academy

Lecture 3: Lets talk about Search

Lecture 4: Course Resources

Lecture 5: What is a Search Engine

Lecture 6: Inside a Search Engine

Lecture 7: What is MetaData

Lecture 8: ElasticSearch in a Nutshell

Lecture 9: How ElasticSearch offers Scalability

Lecture 10: ElasticsSearch provides High Availability

Lecture 11: Multi-Tenancy Out of the Box

Lecture 12: Full Text Search inside ElasticSearch

Lecture 13: Real Time Analytics with ElasticSearch

Lecture 14: Chapter Summary

Chapter 2: Setup a Working Environment

Lecture 1: Introduction

Lecture 2: Tools we will need

Lecture 3: Install Hortonworks Data Platform Sandbox – ( FOR WINDOWS PC USERS ONLY )

Lecture 4: [Hands-on] – Installing wget

Lecture 5: [Hands-on] – Lets start with installing Homebrew

Lecture 6: [Hands-on] – Check Java Version installed

Lecture 7: [Hands-on] – Check and Enable SSH

Lecture 8: [Hands-on] – Getting Latest Apache Hadoop

Lecture 9: [Hands-on] – Configuring Apache Hadoop Part – 1

Lecture 10: [Hands-on] – Configuring Apache Hadoop Part – 2

Lecture 11: [Hands-on] – Lets Bring Hadoop Daemons to Life

Lecture 12: [Hands-on] – Configuring Apache Hadoop Part – 3

Lecture 13: [Hands-on] – Getting ElasticSearch

Lecture 14: [Hands-on] – Configuring ElasticSearch

Lecture 15: [Hands-on] – Installing ElasticSearch Plugins: <Head Plugin>

Lecture 16: [Hands-on] – Installing ElasticSearch Plugins: <Marvel Plugin>

Lecture 17: [Hands-on] – Starting ElasticSearch Daemon and Checking out Plugins

Lecture 18: Chapter Summary

Chapter 3: Building Blocks of ElasticSearch

Lecture 1: Introduction

Lecture 2: RDBMS vs ElasticSearch

Lecture 3: How a document (data record) looks likes inside ElasticSearch

Lecture 4: Marvels of Inverted Index

Lecture 5: Shards – low level storage units in ElasticSearch

Lecture 6: An ElasticSearch Node

Lecture 7: ElasticSearch Cluster

Lecture 8: Monitoring ElasticSearch Cluster's Health

Lecture 9: How to Scale an ElasticSearch Cluster

Lecture 10: RestAPIs in ElasticSearch

Lecture 11: Chapter Summary

Chapter 4: Operations in ElasticSearch

Lecture 1: Introduction

Lecture 2: A look at ShardID

Lecture 3: Types of Operation in ElasticSearch

Lecture 4: Inside Operations: <Write> & <Delete>

Lecture 5: [Hands-on] – Lets try a Write Operation

Lecture 6: Inside Operations: <Read>

Lecture 7: [Hands-on] – Lets try a Read Operation Part-1

Lecture 8: [Hands-on] – Lets try a Read Operation Part-2

Lecture 9: Inside Operation: <Update>

Lecture 10: [Hands-on] – Lets try an Update Operation

Lecture 11: [Hands-on] – Lets try a Delete Operation

Lecture 12: Concept of Mapping in ElasticSearch

Lecture 13: [Hands-on] – How to add Mappings on Indexed data

Lecture 14: Data consistency using Templates

Lecture 15: [Hands-on] – Using Templates

Lecture 16: Chapter Summary

Chapter 5: Queries in ElasticSearch

Lecture 1: Introduction

Lecture 2: Types of Search Queries

Lecture 3: [Hands-on] – Creating Dataset for Search

Lecture 4: [Hands-on] – Using QueryString Part-1: Select All

Lecture 5: [Hands-on] – Using QueryString Part-2: Filter Specific Fields

Lecture 6: Word of Caution with QueryStrings

Lecture 7: [Hands-on] – Using DSL Queries Part-1

Lecture 8: [Hands-on] – Using DSL Queries Part-2

Lecture 9: Chapter Summary

Chapter 6: Data Pipelines

Lecture 1: Basics about a Data Pipeline

Chapter 7: Data Pipeline#1 – Apache Hive to ElasticSearch

Lecture 1: Setting Objectives

Lecture 2: [Hands-on] – Installing Apache Hive

Lecture 3: [Hands-on] – Configuring Apache Hive Part-1

Lecture 4: [Hands-on] – Configuring Apache Hive Part-2

Lecture 5: Getting ElasticSearch Connector JAR

Lecture 6: Where to get free datasets for exercises

Lecture 7: Understanding Our Dataset

Lecture 8: [Hands-on] – Creating a Data Flow from Hive to ElasticSearch Index Part-1

Lecture 9: [Hands-on] – Creating a Data Flow from Hive to ElasticSearch Part-2

Lecture 10: [Hands-on] – Looking at ingested data inside ElasticSearch Cluster

Chapter 8: Data Pipeline#2 – ElasticSearch to Apache Hive

Lecture 1: Introduction

Lecture 2: Setting Objectives

Lecture 3: [Hands-on] – Indexing Data inside ElasticSearch using Bulk API

Lecture 4: [Hands-on] – Creating Data Flow from ElasticSearch Index to Hive table

Lecture 5: Chapter Summary

Chapter 9: Data Pipeline#3 – Apache PIG to ElasticSearch

Lecture 1: Introduction

Instructors

Rating Distribution

  • 1 stars: 7 votes
  • 2 stars: 7 votes
  • 3 stars: 15 votes
  • 4 stars: 20 votes
  • 5 stars: 25 votes

Frequently Asked Questions


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