ELK Stack Training & Certification

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Avvacado Tech Info ELK Stack course makes you an expert in ELK such that you can run and operate your own search cluster using Elasticsearch, Logstash, Kibana. You will gain proficiency to use Logstash to load data into Elasticsearch, run various search operation and do data visualization with the help of Kibana.

Why this course ?

ELK stack makes it way easier -- and way faster -- to search and analyze large data sets ​ELK has been adopted by well-known organizations such as LinkedIn, Netflix, and Stack Overflow​, Accenture, ​Fujitsu​ The average pay stands for ELK Stack developer is $1​23,​563 P.A - Indeed.com

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Instructor-led Sessions

30hrs of Online Live Instructor-led Classes. Weekend class:10 sessions of 3 hours each and Weekday class:15 sessions of 2 hours each.

Real-life Case Studies

Live project based on any of the selected use cases, involving Big Data Analytics.

Assignments

Each class will be followed by practical assignments which can be completed before the next class.

Lifetime Access

You get lifetime access to the Learning Management System (LMS). Class recordings and presentations can be viewed online from the LMS.

24 x 7 Expert Support

We have 24x7 online support team available to help you with any technical queries you may have during the course.

Certification

Towards the end of the course, you will be working on a project. Our Expert certifies you as a Big Data and Hadoop Expert based on the project.

Forum

We have a community forum for all our customers wherein you can enrich their learning through peer interaction and knowledge sharing.

   About the course

The ELK stack consists of Elasticsearch, Logstash, and Kibana. Although they've all been built to work exceptionally well together, each one is a separate tool that is driven by the open-source vendor Elastic. Elastic has created an end-to-end stack that delivers actionable insights in real time from almost any type of structured and unstructured data source. So the course can be summarized into: 
  • An overview of the key features of Elasticsearch, Logstash & Kibana
  • A deep dive on how their powers combine to deliver an end to end solution for analytics, logging, search & visualization
  • Extensive hands-on demo of the Elastic Stack in action

   Course Objectives

After completing ELK Stack course, you should be able to:

  • Learn the fundamentals of ELK stack with different use-case
  • Discuss about each component of ELK stack individually in depth
  • Install the stack components in your system
  • Use Logstash to load data into Elastic Search
  • Create visualization with the loaded data with the help of Kibana
  • Analyze real time data with ELK stack

   Why learn ELK Stack?

ELK Stack will help you find answers to the below questions that might be part of various Business Scenarios:

  • How many users have signed up this week?
  • When should we schedule the maintenance?
  • Why is the database slow?
  • How can i search in the Logs that are of different formats and have inconsistent data?
  • How to deal with different types of Time Formats?
  • How can I search logs that are spread across different locations?

   Why should go for this training?

  • Big Data Analytics Engineer – Elastic Search
  • Web Administrator
  • System Log Analyst
  • Full Stack Technical Architect
  • Web Analyst

   What are the pre-requisites for this course?

​ To master the concept of ELK Stack, you need to have basic understanding of

  • JSON Data Format​
  • SQL​
  • ​Restful API​

    • ​ ​​ ​

   What are the System requirements for this course ?

The system requirements for ELK Stack course is Multicore Processor (i3-i7 series), 8GB of RAM is recommended and 20GB Hard Disck (SDD preferable). The operating system can be Windows.

   How will I execute the Practicals?

The practicals can be executed on your machine by installing all the three component of the stack. Detailed Installation Guide will be provided as part of the LMS.

   Which Case-Studies will be part of the course?

Tech Analyst : A 9.5 years young and energetic IT services company founded by IIT'ians, providing a full 360 degree solution to the clients across the globe. One of the main task of the company involves analyzing huge amount of data. They have decided to use open source tool ELK stack for their analysis due its several robust features

Task      

The task of the employee is to fetch the required data from the source to Logstash and run queries on elastic search and finally visualize the data with the help of Kibana.

   Story Line

Introduction:
Alice is a support engineer working in TS foundation, which is a software developing company. One of its feature is enabling single sign-on for its applications.

ALICES’ DAY TO DAY CHALLENGES:
Her task is to help the customers, and troubleshoot issues when needed. Whenever there’s a ticket for an issue, the first place she checks in, is the logs in the designated servers. She keeps searching and searching for related words or keyword match. Meanwhile there is change in logs every minute, and this is making her search, more and more hectic.
How can we help her?

SOLUTION:
Well this is where ELK stack comes into the picture
ELK comes with elastic search, logstash and kibana stacked altogether to give her a full analytics system.

Elastic Search enables her to search logs easily and get to know the issue and resolve it in a faster manner; not only that she can get proactive by analyzing the logs, and see if any of those customers are facing any issues or failures.
Now she can log into Kibana and search for relevant keywords easily. She can even limit the research by using timestamp filter. Monitoring single sign-on activities can be easily done by using different visualization graphs on the dashboards

   Introduction to ELK Stack

Goal: Let’s help Alice by introducing ELK stack to her, and helping her in understanding the core concepts and the technology behind it. This will help her in learning ELK architecture and various implementation of ELK stack in companies. 

Objectives: Upon completing this lesson, you should be able to:

• Introduce ELK stack and state its various benefits
• Get a brief idea about Architecture of ELK stack and various terminology associated with it.
• Learn why should we implement ELK in the company
• Get an overview of Elastic Search, Logstash and Kibana

Topics:
• Introduction to ELK stack
• Why ELK?
• Architecture of ELK
• High level overview of 
o Elastic Search
o Logstash
o Kibana

   Getting Started with Logstash

Goal: Alice has learnt to the basic concepts of ELK stack. Now what if she has to work with new sets of inputs, let’s help her with the another component of ELK stack, logstash. This module will give her a basic introduction to Logstash and guide through the process of installing Logstash and verifying that everything is running properly. After learning how to stash your first event, you can go on to create a more advanced pipeline that takes Apache web logs as input, parses the logs, and writes the parsed data to an Elasticsearch cluster. Then you learn how to stitch together multiple input and output plugins to unify data from a variety of disparate sources.
 
Objectives: At the end of this lesson, you should be able to:

• Install and verify running of Logstash on your machine
• Learn to stash first event 
• Create a more advanced pipeline that takes Apache web logs as input, parses the logs, and writes the parsed data to an Elasticsearch cluster.
• Learn how to stitch together multiple input and output plugins to unify data from a variety of disparate sources

 Topics:
• Introduction to Logstash
• Installing Logstash
• Configuring a log file
• Stashing your First Event
• Parsing Logs with Logstash
• Stitching together Multiple Input and Output
• Plugins 
• Execution Model

Hands On: 
• Step by step guide to install Logstash on your machine
• Configure the log file
• Stash your first event in Logstash
• Parsing Logs with Logstash
• Installing FileBeats and configuring it to work with Logstash
• Configuring Grok Plugin

   Introduction to Elastic Search

Goal: Alice got the overview of the ELK stack, now she wants a deep understanding of each component of the stack. Let’s help her in getting started, with a brief introduction to Elastic Search with a use-case. 
 
Objectives: Let’s Build an Employee Directory

We happen to work for Megabuilder, and as part of HR’s new initiative, we have been tasked with creating an employee directory. The directory is supposed to foster employer empathy and real-time, synergistic, dynamic collaboration, so it has a few business requirements:

• Enable data to contain multi value tags, numbers, and full text.
• Retrieve the full details of any employee.
• Allow structured search, such as finding employees over the age of 30.
• Allow simple full-text search and more-complex phrase searches.
• Return highlighted search snippets from the text in the matching documents.
• Enable management to build analytic dashboards over the data

Topics:
• Elastic Search Overview
• Installing and running Elastic Search
• Indexing Documents
• Retrieving a Document
• Searching a Document

Hands On:
• Installing and running Elastic Search
• Indexing Documents
• Retrieving Full Document
• Retrieving a part of Document
• Checking Document Existence
• Updating a Document
• Deleting a Document
• Searching a Document (Overview)

   Searching in Depth

Goal: Alice seemed excited and she is curious about learning searching in depth. She wants to explore more about Elastic Search. She understood its not just enough to use the match query. She needs to understand the data and run search query through it. This module explains her, how to index and query your data to allow her to take advantage of word proximity, partial matching, fuzzy matching, and language awareness.

Topics:
• Structured Search
• Full text Search
• Complicated Search 
• Phrase Search
• Highlighting our Search
• Multi-field Search
• Proximity Matching
• Partial Matching

Hands On:
Above all topics are hands-on intensive

   Dealing with Human Language

Goal: Alice learned and performed various searching queries and was satisfied with it, when she suddenly realized a problem. Her query was not able to remove distinction between singular and plural words, or between tenses. She even faced problem with typos and various other problem. Let’s help Alice in solving her issues by training her on how to deal with human language for improving performance.
 
Objectives: At the end of this lesson, you should be able to:

• Remove diacritics like ´, ^, and ¨ so that a search for rôle will also match role, and vice versa using Normalizing Tokens.
• Remove the distinction between singular and plural—fox versus foxes—or between tenses—jumping versus jumped versus jumps—by stemming each word to its root form in Reducing Words to Their Root Form.
• Remove commonly used words or stopwords like the, and, and or to improve search performance in Stopwords: Performance Versus Precision.
• Including synonyms so that a query for quick could also match fast, or UK could match United Kingdom with the help of Synonyms.
• Check for misspellings or alternate spellings, or match on homophones—words that sound the same, like their versus there, meat versus meet versus mete using Typos and Misspellings.

Topics:
• Getting Started with languages
• Identifying Words
• Normalizing Tokens
• Reducing Words to their Root Form
• Stopwords: Performance versus Precision
• Synonyms
• Typos and Misspellings

Hands On:
Above all topics are hands-on intensive

   Aggregation

Goal: Alice leaned all about how to search through her data, now once data is searched she needs to get a higher-level overview of the dataset and perform queries on it to get her answers in near-real time. This has made her task very tedious and tiring. Let’s ease her problem by training her with aggregation.
 
Aggregations will allow her to ask sophisticated questions of her data in near real time. With search, we have a query and we want to find a subset of documents that match the query. We are looking for the needle(s) in the haystack.
 
With aggregations, we zoom out to get an overview of our data. Instead of looking for individual documents, we want to analyze and summarize our complete set of data:

• How many needles are in the haystack?
• What is the average length of the needles?
• What is the median length of the needles, broken down by manufacturer?
• How many needles were added to the haystack each month?

Aggregations can answer more subtle questions too:
• What are your most popular needle manufacturers?
• Are there any unusual or anomalous clumps of needles?

Topics:
• High Level Concepts 
• Getting started with Aggregation
• Time Analysis
• Filtering Queries and Aggregations
• Sorting Multivalue Buckets
• Approximate Aggregation
• Doc Values and Field Data

Hands On:
Above all topics are hands-on intensive

   Data Modeling

Goal: Alice was well-versed in working with SQL she thought that for handling relationships, the golden rule of relational database- normalize your data will be applicable in Elastic Search too. But as a matter of fact, this golden rule does not apply to Elastic Search. Joining entities at query time is expensive—the more joins that are required, the more expensive the query. Performing joins between entities that live on different hardware is so expensive that it is just not practical. In this module let’s discover how data is modelled in Elastic Search. 
 
Objectives:
At the end of this lesson, you should be able to:

• Handle relationship between the entities
• Discuss the pros and cons of different approaches
• Scale out quickly and flexibly.

 Topics:
• Elastic Search vs RDBMS
• Handling Relationships 
• Nested Objects
• Parent-Child Relationship
• Designing for Scale

Hands On:
Above all topics are hands-on intensive

   Geolocation

Goal: The beauty of Elasticsearch is that it allows you to combine geolocation with full-text search, structured search, and analytics. 

For instance: show me restaurants that mention PIZZA, BURGER, and are within a 5-minute walk, and are open at 11 p.m., and then rank them by a combination of user rating, distance, and price.

Objectives:
At the end of this lesson, you should be able to:

• Use Geo-Points to calculate distance from a point, to determine whether a point falls within a bounding box, or in aggregations.
• Encode lat/log points as strings using Geohases, to have a URL-friendly way of specifying geolocations and indexing geo-points and geo-shapes in databases.
• Cluster geo-points into more manageable buckets with geo aggregation
 
Topics:
• Geo Points
• Geohashes
• Geo Aggregations
• Geo Shapes

Hands On:
Above all topics are hands-on intensive

   Getting Started with Kibana

Goal: Learn to search, view, and interact with data stored in Elasticsearch indices. You can easily perform advanced data analysis and visualize your data in a variety of charts, tables, and maps.

Objectives:
At the end of this lesson, you should be able to:

• Install and verify running of Logstash on your machine
• Learn to stash first event 
• Create a more advanced pipeline that takes Apache web logs as input, parses the logs, and writes the parsed data to an Elasticsearch cluster.
• Learn how to stitch together multiple input and output plugins to unify data from a variety of disparate sources

Topics:
• Introduction to Kibana
• Installing Kibana
• Loading Sample Data
• Discovering your Data
• Visualizing your Data
• Working with Dashboard

Hands On:
Using Kibana to create a dashboard

   Discovering Data in Depth and Dashboard

Goal: Learn to interact and explore your data from the Discover page. You have access to every document in every index that matches the selected index pattern. You can submit search queries, filter the search results, and view document data. You can also see the number of documents that match the search query and get field value statistics. If a time field is configured for the selected index pattern, the distribution of documents over time is displayed in a histogram at the top of the page.

Topics:
• Setting the Time Filter
• Searching your Data
• Filtering by Field
• Viewing Document Data
• Viewing Document Context
• Viewing Field Statistics
• Data Visualization
• Dashboard
• Analyzing live data with ELK stack

Hands On:
• Time Filter
• Document Context
• Creating a Dashboard

   What if I miss a class?

You will never miss a lecture at Avvacado Tech Info! You can choose either of the two options:

  • View the recorded session of the class available in your LMS.
  • You can attend the missed session, in any other live batch.

   Will I get placement assistance?

Avvacado Tech Info is committed to provide you an awesome learning experience through world-class content and best-in-class instructors. We will create an ecosystem through this training, that will enable you to convert opportunities into job offers by presenting your skills at the time of an interview. We can assist you in resume building and also share important interview questions once you are done with the training. However, please understand that we are not into job placements.

   Can I attend a demo session before enrolment?

We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in a class.

   Who are the Instructors?

All the instructors at Avvacado Tech Info are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by Avvacado Tech Info for providing an awesome learning experience to the participants.

   Avvacado Tech Info's certification process

    • Once you are successfully through the project (Reviewed by a Avvacado Tech Info expert), you will be awarded with Avvacado Tech Info’s ELK Stack Expert Certificate.
    • Avvacado Tech Info certification has industry recognition and we are the preferred training partner for many MNCs e.g.Cisco, Ford, Mphasis, Nokia, Wipro, Accenture, IBM, Philips, Citi, Ford, Mindtree, BNYMellon etc. Please be assured.