ELK Stack Certification Training

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
• Learn about Architecture of ELK stack
• Understand various ELK terminology
• Learn the basics of Elastic Search, Logstash and Kibana
• Understand ELK stack use case

Topics:

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

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 module, 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

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

• Enable Multi Value tags, Numbers, and Full text
• Retrieve the full details of any employee
• Perform Structured search
• Learn about full-text search
• Return highlighted search snippets

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)

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.

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

• Perform Structured Search using Elastic search
• Deploy and understand full text search query
• Know your data with multifield search
• Find associated words
• Understand partial matching query

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

Goal: Alice learned and performed various searching queries and was satisfied with it, when she suddenly realised 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 module you will be able to:

• Remove diacritics like ´, ^, and ¨ so that a search for rôle will also match role, and vice versa using Normalising 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
• Normalising Tokens
• Reducing Words to their Root Form
• Stopwords: Performance versus Precision
• Synonyms
• Typos and Misspellings

Hands On:
Above all topics are hands-on intensive

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 analyse and summarise our complete set of data:

Objectives: At the end of this module you will be able to:

• Understand the concepts of buckets and metrics
• Build bar chart with buckets
• Look at the time using Date Histogram
• Filter queries and aggregation
• Sort multivalue bucket

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

Goal: Alice was well-versed in working with SQL she thought that for handling relationships, the golden rule of relational database- normalise 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:

• Compare Elasticsearch with RDBMS
• Get the best search result by learning Denormalizing Data
• Perform action with Nested Objects
• Understand Parent-Child Relationship
• Finally conclude the module with concept of shards and replicas

Topics:

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

Hands On:
Above all topics are hands-on intensive

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 module you will be able to:

• Understand the concepts of Geo Points
• Aggregate Geo Distance
• Understand Geohash and aggregate geohash grid
• Learn about different Geo Shapes

Topics:

• Geo Points
• Geohashes
• Geo Aggregations
• Geo Shapes

Hands On:
Above all topics are hands-on intensive

Goal: Learn to search, view, and interact with data stored in Elasticsearch indices. You can easily perform advanced data analysis and visualise 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 Kibana
• Ingest .json files into Elasticsearch
• Create different visualization
• Pie Chart
• Bar Chart
• Coordinate Map
• Summarize the Dashboard

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

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
• Learn about Architecture of ELK stack
• Understand various ELK terminology
• Learn the basics of Elastic Search, Logstash and Kibana
• Understand ELK stack use case

Topics:

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

Big Data Hadoop Certification Training is designed by industry experts to make you a Certified Big Data Practitioner. The Big Data Hadoop course offers:

• In-depth knowledge of Big Data and Hadoop including HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator) & MapReduce
• Comprehensive knowledge of various tools that fall in Hadoop Ecosystem like Pig, Hive, Sqoop, Flume, Oozie, and HBase
• The capability to ingest data in HDFS using Sqoop & Flume, and analyze those large datasets stored in the HDFS
• The exposure to many real world industry-based projects which will be executed in Brighter Connect’s CloudLab
• Projects which are diverse in nature covering various data sets from multiple domains such as banking, telecommunication, social media, insurance, and e-commerce
• Rigorous involvement of a Hadoop expert throughout the Big Data Hadoop Training to learn industry standards and best practices

Big Data is one of the accelerating and most promising fields, considering all the technologies available in the IT market today. In order to take benefit of these opportunities, you need a structured training with the latest curriculum as per current industry requirements and best practices. Besides strong theoretical understanding, you need to work on various real world big data projects using different Big Data and Hadoop tools as a part of solution strategy. Additionally, you need the guidance of a Hadoop expert who is currently working in the industry on real world Big Data projects and troubleshooting day to day challenges while implementing them.

Big Data Hadoop Certification Training will help you to become a Big Data expert. It will hone your skills by offering you comprehensive knowledge on Hadoop framework, and the required hands-on experience for solving real-time industry-based Big Data projects. During Big Data & Hadoop course you will be trained by our expert instructors to:

• Master the concepts of HDFS (Hadoop Distributed File System), YARN (Yet Another Resource Negotiator), & understand how to work with Hadoop storage & resource management.
• Understand MapReduce Framework
• Implement complex business solution using MapReduce
• Learn data ingestion techniques using Sqoop and Flume
• Perform ETL operations & data analytics using Pig and Hive
• Implementing Partitioning, Bucketing and Indexing in Hive
• Understand HBase, i.e a NoSQL Database in Hadoop, HBase Architecture & Mechanisms
• Integrate HBase with Hive
• Schedule jobs using Oozie
• Implement best practices for Hadoop development
• Understand Apache Spark and its Ecosystem
• Learn how to work with RDD in Apache Spark
• Work on real world Big Data Analytics Project
• Work on a real-time Hadoop cluster

The market for Big Data analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals. Hiring managers are looking for certified Big Data Hadoop professionals. Our Big Data & Hadoop Certification Training helps you to grab this opportunity and accelerate your career. Our Big Data Hadoop Course can be pursued by professional as well as freshers. It is best suited for:

• Software Developers, Project Managers
• Software Architects
• ETL and Data Warehousing Professionals
• Data Engineers
• Data Analysts & Business Intelligence Professionals
• DBAs and DB professionals
• Senior IT Professionals
• Testing professionals
• Mainframe professionals
• Graduates looking to build a career in Big Data Field

The below predictions will help you in understanding the growth of Big Data:

• Hadoop Market is expected to reach $99.31B by 2022 at a CAGR of 42.1% -Forbes
• McKinsey predicts that by 2018 there will be a shortage of 1.5M data experts
• Average Salary of Big Data Hadoop Developers is $97k

There are no such prerequisites for Big Data & Hadoop Course. However, prior knowledge of Core Java and SQL will be helpful but is not mandatory. Further, to brush up your skills, Edureka offers a complimentary self-paced course on "Java essentials for Hadoop" when you enroll for the Big Data and Hadoop Course.

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.

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 analysing 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 visualise the data with the help of Kibana.