Big Data and Hadoop Training
Learn job critical skills like Big Data & Hadoop frameworks, leverage
the functionality of AWS services. FiestTech's comprehensive Big Data course
is curated by 10+ years of experienced industry experts, and it covers in-depth
knowledge on Hadoop Ecosystem tools such as HDFS, YARN, MapReduce, Hive
and Pig. Throughout this online live Big Data Hadoop certification training
you will be working on real-life industry use cases in Retail, Social Media, Aviation
Tourism, and Finance domains using FiestTech's Cloud Lab. FiestTech's
Big Data Course provides you outstanding professional training with industry-based
Projects work to clear Cloudera CCA 175 Certification exam on the first attempt.
Group Enrollment with Friends or Colleagues |
Premier Authorized Training Partner
Big Data Course Overview
The Big Data Hadoop certification training is designed to give you an in-depth knowledge of the Big Data framework using Hadoop and Spark. In this hands-on Hadoop course, you will execute real-life, industry-based projects using Integrated Lab.
Hadoop Development course teaches the skill set required for the learners how to setup Hadoop Cluster, how to store Big Data using Hadoop (HDFS) and how to process/analyze the Big Data using Map-Reduce Programming or by using other Hadoop ecosystems. Attend Hadoop Training demo by Real-Time Expert.
Big Data Hadoop Training Key Features
At Fiesttech, we value the trust of our patrons immensely. But, if you feel that this Big Data Hadoop Training does not meet your expectations, we offer a 7-day money-back guarantee. Just send us a refund request via email within 7 days of purchase and we will refund 100% of your payment, no questions asked!
- 8X higher live interaction in live online classes by industry experts
- Dedicated mentoring session from our Industry expert faculties
- Aligned to Cloudera CCA175 certification exam
- 4 real-life industry projects using Hadoop, Hive and Big data stack
- Training on Yarn, MapReduce, Pig, Hive, HBase, and Apache Spark
Skills Covered
- Keras and TensorFlow Framework
- PyTorch and its elements
- Image Classification
- Artificial Neural Networks
- Autoencoders
- Deep Neural Networks
- Conventional Neural Networks
- Recurrent Neural Networks
- ADAM Adagrad and Momentum
Benefits
As humans, we are immersed in data in our every-day lives. As per IBM, the data doubles every two years on this planet. The value that data holds can only be understood when we can start to identify patterns and trends in the data. Normal computing principles do not work when data becomes huge.
There is massive growth in the big data space, and job opportunities are skyrocketing, making this the perfect time to launch your career in this space.
In this course, you will learn Hadoop to drive better business decisions and solve real-world problems.
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Training Options
Explore all of our training options and pick your suitable ones to enroll and start learning with us! We ensure that you will never regret it!
Big Data Hadoop Course Curriculum
Eligibility
Big Data Hadoop certification training online course is best suited for IT, Data Management, and Analytics professionals looking to gain expertise in Big Data Hadoop, including Software Developers and Architects, Senior IT professionals, Testing and Mainframe professionals, Business Intelligence professionals, Project Managers, Aspiring Data Scientists, Graduates looking to begin a career in Big Data Analytics.
Pre-requisites
Professionals entering into Big Data Hadoop certification training should have a basic understanding of Core Java and SQL. If you wish to brush up your Core Java skills, FiestTech offers a complimentary self-paced course Java essentials for Hadoop as part of the course syllabus.
Course Content
Live Course
Self Paced
-
2.01 - 1.1 Introduction to Big Data and Hadoop
02:23 -
2.02 - 1.2 Introduction to Big Data
03:23 -
2.03 - 1.3 Big Data Analytics
02.11 -
2.04 - 1.5 Four Vs Of Big Data
01:26 -
2.05 - 1.6 Case Study: Royal Bank of Scotland
05;44 -
2.06 - 1.7 Challenges of Traditional System
04:22 -
2.07 - 1.8 Distributed Systems
02.11 -
2.08 - 1.9 Introduction to Hadoop
01:21 -
2.09 - 1.10 Components of Hadoop Ecosystem: Part One
04:22 -
2.10 - 1.11 Components of Hadoop Ecosystem: Part Two
02:26 -
2.11 - 1.12 Components of Hadoop Ecosystem: Part Three
07:28 -
2.12 - 1.13 Commercial Hadoop Distributions
03:31 -
2.13 - 1.14 Demo: Walkthrough of Fiest Tech Cloudlab
04:34 -
2.14 - 1.15 Key Takeaways
00:32 -
2.15 - Knowledge Check
00:57
-
3.01 - 2.1 Hadoop Architecture Distributed Storage (HDFS) and YARN
04:32 -
3.02 - 2.2 What Is HDFS
03:21 -
3.03 - 2.3 Need for HDFS
01:12 -
3.04 - 2.4 Regular File System vs HDFS
01:26 -
3.05 - 2.5 Characteristics of HDFS
01:26 -
3.06 - 2.6 HDFS Architecture and Components
04:34 -
3.07 - 2.7 High Availability Cluster Implementations
04:34 -
3.08 - 2.8 HDFS Component File System Namespace
03:43 -
3.09 - 2.9 Data Block Split
02:26 -
3.10 - 2.10 Data Replication Topology
03:23 -
3.11 - 2.11 HDFS Command Line
04:22 -
3.12 - 2.12 Demo: Common HDFS Commands
08:08 -
3.13 - 2.13 YARN Introduction
02:26 -
3.14 - 2.14 YARN Use Case
02:11 -
3.15 - 2.15 YARN and Its Architecture
07:28 -
3.16 - 2.16 Resource Manager
04:32 -
3.17 - 2.17 How Resource Manager Operates
02:21 -
3.18 - 2.18 Application Master
05:25 -
3.19 - 2.19 How YARN Runs an Application
08:43 -
3.20 - 2.20 Tools for YARN Developers
09:08 -
3.21 - 2.21 Demo: Walkthrough of Cluster Part One
03:11 -
3.22 - 2.22 Demo: Walkthrough of Cluster Part Two
09:34 -
3.23 - 2.23 Key Takeaways
00:57 -
3.24 - Knowledge Check
00:25 -
3.25 - Hadoop Architecture,Distributed Storage (HDFS) and YARN
08:08
-
4.01 - 3.1 Data Ingestion into Big Data Systems and ETL
02:26 -
4.02 - 3.2 Data Ingestion Overview Part One
02:23 -
4.03 - 3.3 Data Ingestion
02:26 -
4.04 - 3.4 Apache Sqoop
04:22 -
4.05 - 3.5 Sqoop and Its Uses
02:32 -
4.06 - 3.6 Sqoop Processing
02:11 -
4.07 - 3.7 Sqoop Import Process
06:07 -
4.08 - 3.8 Sqoop Connectors
04:32 -
4.09 - 3.9 Demo: Importing and Exporting Data from MySQL to HDFS
07:28 -
4.10 - 3.9 Apache Flume
04:34 -
4.11 - Apache Sqoop
03:32 -
4.12 - 3.10 Flume Model
03:21 -
4.13 - 3.11 Scalability in Flume
05:56 -
4.14 - 3.12 Components in Flume’s Architecture
04:32 -
4.15 - 3.13 Configuring Flume Components
06:00 -
4.16 - 3.14 Apache Kafka
01:26 -
4.17 - 3.15 Demo: Ingest Twitter Data
06:07 -
4.18 - 3.15 Aggregating User Activity Using Kafka
02:11 -
4.19 - 3.16 Kafka Data Model
02:23 -
4.20 - 3.17 Partitions
02:27 -
4.21 - 3.18 Apache Kafka Architecture
03:21 -
4.22 - 3.19 Producer Side API Example
04:34 -
4.23 - 3.20 Consumer Side API
05:56 -
4.24 - 3.21 Demo: Setup Kafka Cluster
01:21 -
4.25 - 3.21 Consumer Side API Example
02:26 -
4.26 - 3.22 Kafka Connect
02:32 -
4.27 - 3.23 Key Takeaways
00:21 -
4.28 - 3.26 Demo: Creating Sample Kafka Data Pipeline using Producer an
08:43 -
4.29 - Knowledge Check
00:12 -
4.30 - Data Ingestion into Big Data Systems and ETL
08:43
-
5.01 - 4.1 Distributed Processing MapReduce Framework and Pig
02.11 -
5.02 - 4.2 Distributed Processing in MapReduce
01:26 -
5.03 - 4.3 Word Count Example
02:26 -
5.04 - 4.4 Map Execution Phases
04:32 -
5.05 - 4.5 Map Execution Distributed Two Node Environment
04:32 -
5.06 - 4.6 MapReduce Jobs
04:32 -
5.07 - 4.7 Hadoop MapReduce Job Work Interaction
02:11 -
5.08 - 4.8 Setting Up the Environment for MapReduce Development
04:32 -
5.09 - 4.9 Set of Classes
03:11 -
5.10 - 4.10 Creating a New Project
05:25 -
5.11 - 4.11 Advanced MapReduce
07:28 -
5.12 - 4.12 Data Types in Hadoop
02:32 -
5.13 - 4.13 OutputFormats in MapReduce
03:23 -
5.14 - 4.14 Using Distributed Cache
03:43 -
5.15 - 4.15 Joins in MapReduce
01:21 -
5.16 - 4.16 Replicated Join
02:21 -
5.17 - 4.17 Introduction to Pig
03:11 -
5.18 - 4.18 Components of Pig
02.11 -
5.19 - 4.19 Pig Data Model
02:32 -
5.20 - 4.20 Pig Interactive Modes
04:34 -
5.21 - 4.21 Pig Operations
01:26 -
5.22 - 4.22 Various Relations Performed by Developers
03:23 -
5.23 - 4.23 Demo: Analyzing Web Log Data Using MapReduce
09:08 -
5.24 - 4.24 Demo: Analyzing Sales Data and Solving KPIs using PIG
08:43 -
5.25 - Apache Pig
02.11 -
5.26 - 4.25 Demo: Wordcount
03:21 -
5.27 - 4.26 Key takeaways
00:57 -
5.28 - Knowledge Check
02:11 -
5.29 - Distributed Processing - MapReduce Framework and Pig
03:23
Big Data Hadoop Training Exam & Certification
Upon successful completion of the Big Data Hadoop certification training, you will be awarded the course completion certificate from FiestTech. To get a CCA175 - Spark and Hadoop certificate from Fiesttech, you need to clear the exam.
- Online Classroom: attend one complete batch of Big Data Hadoop certification training and complete one project and one simulation test with a minimum score of 80%
- Online Self-learning: complete 85% of the course and complete one project and one simulation test with a minimum score of 80%
FAQS
Big Data Hadoop Training Course FAQs
Big data refers to a collection of extensive data sets, including structured, unstructured, and semi-structured data coming from various data sources and having different formats.These data sets are so complex and broad that they can't be processed using traditional techniques. When you combine big data with analytics, you can use it to solve business problems and make better decisions.
Hadoop is an open-source framework that allows organizations to store and process big data in a parallel and distributed environment. It is used to store and combine data, and it scales up from one server to thousands of machines, each offering low-cost storage and local computation.
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