Overview
Wissenhive's Big Data Hadoop Training Course is curated by Hadoop industry experts, and it covers in-depth knowledge on Big Data and Hadoop Ecosystem tools such as HDFS, YARN, MapReduce, Hive, Pig, HBase, Spark, Oozie, Flume and Sqoop. Throughout this online instructor-led Hadoop Training, you will be working on real-life industry use cases in Retail, Social Media, Aviation, Tourism and Finance domain using Edureka's Cloud Lab.
What you will learn
- Learn the fundamentals
- Efficient data extraction
- MapReduce
- Debugging techniques
- Hadoop frameworks
- Real-world analytics
Benefits
With most businesses facing a data deluge, the Hadoop platform helps in processing these large volumes of data in a rapid manner, thereby offering numerous benefits at both the organization and individual level.
Individual Benefits:
Undergoing training in Hadoop and big data is quite advantageous to the individual in this data-driven world:
- Enhance your career opportunities as more organizations work with big data
- Professionals with good knowledge and skills in Hadoop are in demand across various industries
- Improve your salary with a new skill-set. According to ZipRecruiter , a Hadoop professional earns an average of $133,296 per annum
- Secure a position with leading companies like Google, Microsoft, and Cisco with skills in Hadoop and big data
Organizational Benefits:
Training in Big Data and Hadoop has certain organizational benefits as well:
- Relative to other traditional solutions, Hadoop is quite cost-effective because of its seamless scaling capabilities across large volumes of data
- Expedited access to new data sources which allows an organization to reach its full potential
- Boosts the security of your system as Hadoop boasts of a feature called HBase security
- Hadoop enables organizations to run applications on thousands of nodes
Given the ease with which it allows you to make sense of huge volumes of data and leverage frameworks to transform the same into actionable insights, training and certification courses for Hadoop & Big Data are in great demand in the field of data science.
Course Content
| Understanding Big Data | 14:51 | Play |
| Types of Big Data | 14:51 | Play |
| Difference between Traditional Data and Big Data | 14:51 | Play |
| Introduction to Hadoop | 14:51 | Play |
| Distributed Data Storage In Hadoop, HDFS and Hbase | 14:51 | Play |
| Hadoop Data processing Analyzing Services MapReduce and spark, Hive Pig and Storm | 14:51 | Play |
| Data Integration Tools in Hadoop | 14:51 | Play |
| Resource Management and cluster management Services | 14:51 | Play |
| Need of Hadoop in Big Data | 14:52 | Play |
| Understanding Hadoop And Its Architecture | 14:52 | Play |
| The MapReduce Framework | 14:52 | Play |
| What is YARN? | 14:52 | Play |
| Understanding Big Data Components | 14:53 | Play |
| Monitoring, Management and Orchestration Components of Hadoop Ecosystem | 14:53 | Play |
| Different Distributions of Hadoop | 14:53 | Play |
| Installing Hadoop 3 | 14:53 | Play |
| Hortonworks sandbox installation & configuration | 16:49 | Play |
| Hadoop Configuration files | 16:49 | Play |
| Working with Hadoop services using Ambari | 16:49 | Play |
| Hadoop Daemons | 16:49 | Play |
| Browsing Hadoop UI consoles | 16:49 | Play |
| Basic Hadoop Shell commands | 16:49 | Play |
| Eclipse & winscp installation & configurations on VM | 16:49 | Play |
| Running a MapReduce application in MR2 | 13:24 | Play |
| MapReduce Framework on YARN | 13:24 | Play |
| Fault tolerance in YARN | 13:24 | Play |
| Map, Reduce & Shuffle phases | 13:24 | Play |
| Understanding Mapper, Reducer & Driver classes | 13:24 | Play |
| Writing MapReduce WordCount program | 13:24 | Play |
| Executing & monitoring a Map Reduce job | 13:24 | Play |
| SparkSQL and DataFrames | 13:25 | Play |
| DataFrames and the SQL API | 13:25 | Play |
| DataFrame schema | 13:25 | Play |
| Datasets and encoders | 13:25 | Play |
| Loading and saving data | 13:25 | Play |
| Aggregations | 13:25 | Play |
| Joins | 13:25 | Play |
| A short introduction to streaming | 13:27 | Play |
| Spark Streaming | 13:27 | Play |
| Discretized Streams | 13:27 | Play |
| Stateful and stateless transformations | 13:27 | Play |
| Checkpointing | 13:27 | Play |
| Operating with other streaming platforms (such as Apache Kafka) | 13:27 | Play |
| Structured Streaming | 13:27 | Play |
| Background of Pig | 13:28 | Play |
| Pig architecture | 13:28 | Play |
| Pig Latin basics | 13:29 | Play |
| Pig execution modes | 13:29 | Play |
| Pig processing – loading and transforming data | 13:29 | Play |
| Pig built-in functions | 13:29 | Play |
| Filtering, grouping, sorting data | 13:29 | Play |
| Relational join operators | 13:29 | Play |
| Pig Scripting | 13:29 | Play |
| Pig UDF's | 13:29 | Play |
| Background of Hive | 13:31 | Play |
| Hive architecture | 13:31 | Play |
| Hive Query Language | 13:31 | Play |
| Derby to MySQL database | 13:31 | Play |
| Managed & external tables | 13:31 | Play |
| Data processing – loading data into tables | 13:31 | Play |
| Hive Query Language | 13:31 | Play |
| Using Hive built-in functions | 13:31 | Play |
| Partitioning data using Hive | 13:31 | Play |
| Bucketing data | 13:31 | Play |
| Hive Scripting | 13:31 | Play |
| Using Hive UDF's | 13:31 | Play |
| HBase overview | 13:33 | Play |
| Data model | 13:33 | Play |
| HBase architecture | 13:33 | Play |
| HBase shell | 13:33 | Play |
| Zookeeper & its role in HBase environment | 13:33 | Play |
| HBase Shell environment | 13:33 | Play |
| Creating table | 13:33 | Play |
| Creating column families | 13:33 | Play |
| CLI commands – get, put, delete & scan | 13:33 | Play |
| Scan Filter operations | 13:33 | Play |
| Importing data from RDBMS to HDFS | 13:34 | Play |
| Exporting data from HDFS to RDBMS | 13:34 | Play |
| Importing & exporting data between RDBMS & Hive tables | 13:35 | Play |
| Overview of Oozie | 13:37 | Play |
| Oozie Workflow Architecture | 13:37 | Play |
| Creating workflows with Oozie | 13:37 | Play |
| Introduction to Flume | 13:37 | Play |
| Flume Architecture | 13:37 | Play |
| Flume Demo | 13:37 | Play |
| Introduction | 13:38 | Play |
| Tableau | 13:38 | Play |
| Chart types | 13:38 | Play |
| Data visualization tools | 13:38 | Play |
| Cloud computing basics | 13:39 | Play |
| Concepts and terminology | 13:39 | Play |
| Goals and benefits | 13:39 | Play |
| Risks and challenges | 13:39 | Play |
| Roles and boundaries | 13:39 | Play |
| Cloud characteristics | 13:39 | Play |
| Cloud delivery models | 13:39 | Play |
| Cloud deployment models | 13:39 | Play |
Course Details
Hadoop is an Apache project (i.e. an open source software) to store & process Big Data. Hadoop stores Big Data in a distributed & fault tolerant manner over commodity hardware. Afterwards, Hadoop tools are used to perform parallel data processing over HDFS (Hadoop Distributed File System).
As organisations have realized the benefits of Big Data Analytics, so there is a huge demand for Big Data & Hadoop professionals. Companies are looking for Big data & Hadoop experts with the knowledge of Hadoop Ecosystem and best practices about HDFS, MapReduce, Spark, HBase, Hive, Pig, Oozie, Sqoop & Flume.
Wissenhive Hadoop Training is designed to make you a certified Big Data practitioner by providing you rich hands-on training on Hadoop Ecosystem. This Hadoop developer certification training is stepping stone to your Big Data journey and you will get the opportunity to work on various Big data projects.
Who Should attend?
- Data Architects
- Data Scientists
- Developers
- BI Developers
- BI Analysts
- SAS Developers
- Data Analysts
Course Info.
Training Options
Selfpaced Training
299
- Lifetime access to high-quality self-paced eLearning content curated by industry experts
- 3 simulation test papers for self-assessment
- Lab access to practice live during sessions
- 24x7 learner assistance and support
Live Virtual Classes
499
- Online Classroom Flexi-Pass
- Lifetime access
- Practice lab and projects with integrated Azure labs
- Access to Microsoft official content aligned to examination
One on One Training
999
- Customized learning delivery model (self-paced and/or instructor-led)
- Flexible pricing options
- Enterprise grade learning management system (LMS)
- Enterprise dashboards for individuals and teams
- 24x7 learner assistance and support
Exam & Certification
No Exam Required.
you will be required to complete a project which will be assesd by our certified instructors. on succesful completion of the project you will be awarded a training certificate.
FAQs
The Big Data Analytics course sets you on your path to become an expert in Big Data Analytics by understanding its core concepts and learning the involved technologies. Most of the courses will also involve you working on real-time and industry-based projects. Through an intensive training program, you will learn the practical applications of the field.
Today, the job market is saturated and there is immense competition. Without any specialization, chances are that you will not be considered for the job you are aspiring for.
Big Data Hadoop is used across enterprises in various industries and the demand for Hadoop professionals is bound to increase in the future. Certification is a way of letting recruiters know that you have the right Big Data Hadoop skills they are looking for. With top corporations bombarded with tens of thousands of resumes for a handful of job postings, a Hadoop certification helps you stand out from the crowd. A Certified Hadoop Administrator also commands a higher pay in the market with an average annual income of $123,000. Hadoop certifications can thus propel your career to the next level.
Here are the main differences between Hadoop and Big Data:
- Accessibility - It is difficult to access Big data, whereas you can use the Hadoop framework for processing and accessing data at a faster rate.
- Storage - Storing Big Data is extremely difficult as it is usually in structured and unstructured form. Apache Hadoop HDFS can be used to store big data.
- Significance - Big Data doesn’t have any value on its own until it can be used for creating profit after data processing. Hadoop is the framework that can make Big data meaningful.
- Definition - Big Data is just a large volume of data present in structured and unstructured form. Hadoop is a framework responsible for handling Big Data and processing it.
- Developers - Big data developers develop applications in Pg, MapReduce, Spark, Hive, etc. Hadoop developers are responsible for coding that will be used for processing the data.
- Type - Big data is a problem that doesn’t have any value or meaning unless it is processed. Hadoop is a solution that solves Big Data complex processing.
Reach Us
Useful Links
Company
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B-115, Sector-2, Noida, Uttar Pradesh - 201301
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+91-1204229536
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info@wissenhive.com
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