Big Data Hadoop and Spark Developer

Choosing the right course for skills enhancement and career development is crucial. This trending and highly demanded technology – Big Data allows you to prosper in your career. So, to build your skills in big data, join this Big Data Hadoop and Spark Developer Certification Training Course Online and master the concepts of big data tools, methodologies, and the Hadoop framework. Furthermore, with the successful completion of the Big Data Hadoop online course, you will be completely prepared to work as a Big Data Developer.

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Big Data Hadoop Certification Online Training Course – Overview

This Big Data Hadoop and Spark Developer Online Course is designed and structured in order to give you an in-depth knowledge of big data concepts using Hadoop and Spark frameworks. It gives you a hands-on learning experience and enables you to work on real-time projects with the use of an integrated lab. Adding to this, Big Data Course Online at Hatigen with Hadoop and Spark Frameworks enables you to explore parallel processing and functional programming.

Big Data Hadoop Online Course – Key Features

  • Trusted content.
  • Re-learn for free anytime in a year.
  • Rigorous assignments and assessments.
  • Learn at your own pace.
  • Mandatory feedback sessions.
  • Mock-interviews.
  • Hands-on real-time experience.
  • Free mentorship.
  • Live chat for instant solutions.
  • Job ready employees post-training.
  • End-to-end training.
  • Download the certificate after the course.
Big Data Hadoop – Skills Covered: Real-time data processing. Functional programming. Spark applications. Parallel processing. Spark RDD optimization techniques. Spark SQL

Big Data Hadoop and Spark Developer Online Course – Benefits

In 2020, the global market of big data Hadoop was marked at $35.74 billion and by 2030, it is expected to reach $842.25 billion with a CAGR of 37.4%. However, with this tremendous rise in market growth, it can offer huge career opportunities for students who choose big data as their career path.

Designation
Annual Salary
Hiring Companies
Job Wise Benefits
Designation
Big Data Architect

UK
Hiring Companies
Designation
Big Data Engineer

UK
Hiring Companies
Designation
Big Data Developer

UK
Hiring Companies

Big Data Hadoop Online Course – Training Options

Self-Paced Learning

£ 1200

  • 1-year access to the Big Data Hadoop course content.
  • 1 capstone project.
  • Multiple assessments.
  • Continuous feedback sessions.
  • Access to the class recordings.
  • Assistance and support.
  • Download certification.
  • Free mentorship.

Online Boot Camp

£ 1000

  • Everything in Self-paced learning +
  • On-spot doubt clarification.
  • Interactive training sessions.
  • Sessions on the capstone project.
  • Live, online classroom training.
  • Mock-interviews.

Corporate Training

Customized to your team's needs

  • 1-year access to the Big Data Hadoop course content.
  • 1 capstone project.
  • Multiple assessments.
  • Continuous feedback sessions.
  • Class recordings.
  • Assistance and support.
  • Certification after the course.

Big Data Hadoop Certification Online Training Course– Curriculum

Eligibility

Graduates who are aiming to build their career in Big Data can join this Big Data Hadoop and Spark Developer Training course online. In addition, this big data online course is best suitable for software developers, IT professionals, data management and analytics professionals, project managers, data scientists, etc. who want to gain expertise in big data.

Pre-requisites

If you are planning to choose Big Data Hadoop as your career domain, then you should have a basic knowledge of core java and SQL before you join this Big Data Hadoop and Spark Developer Online Training Course.

Course Content

  • 1.1 Course Introduction
  • 1.2 Accessing Practice Lab
  • 1.1 Introduction to Big Data and Hadoop
  • 1.2 Introduction to Big Data
  • 1.3 Big Data Analytics
  • 1.4 What is Big Data
  • 1.5 Four Vs Of Big Data
  • 1.6 Case Study: Royal Bank of Scotland
  • 1.7 Challenges of Traditional System
  • 1.8 Distributed Systems
  • 1.9 Introduction to Hadoop
  • 1.10 Components of Hadoop Ecosystem: Part One
  • 1.11 Components of Hadoop Ecosystem: Part Two
  • 1.12 Components of Hadoop Ecosystem: Part Three
  • 1.13 Commercial Hadoop Distributions
  • 1.14 Demo: Walkthrough of Simplilearn Cloudlab
  • 1.15 Key Takeaways
  • Knowledge Check
  • 2.1 Hadoop Architecture Distributed Storage (HDFS) and YARN
  • 2.2 What Is HDFS
  • 2.3 Need for HDFS
  • 2.4 Regular File System vs HDFS
  • 2.5 Characteristics of HDFS
  • 2.6 HDFS Architecture and Components
  • 2.7 High Availability Cluster Implementations
  • 2.8 HDFS Component File System Namespace
  • 2.9 Data Block Split
  • 2.10 Data Replication Topology
  • 2.11 HDFS Command Line
  • 2.12 Demo: Common HDFS Commands
  • HDFS Command Line
  • 2.13 YARN Introduction
  • 2.14 YARN Use Case
  • 2.15 YARN and Its Architecture
  • 2.16 Resource Manager
  • 2.17 How Resource Manager Operates
  • 2.18 Application Master
  • 2.19 How YARN Runs an Application
  • 2.20 Tools for YARN Developers
  • 2.21 Demo: Walkthrough of Cluster Part One
  • 2.22 Demo: Walkthrough of Cluster Part Two
  • 2.23 Key Takeaways
  • Knowledge Check
  • Hadoop Architecture,Distributed Storage (HDFS) and YARN
  • 3.1 Data Ingestion into Big Data Systems and ETL
  • 3.2 Data Ingestion Overview Part One
  • 3.3 Data Ingestion
  • 3.4 Apache Sqoop
  • 3.5 Sqoop and Its Uses
  • 3.6 Sqoop Processing
  • 3.7 Sqoop Import Process
  • Assisted Practice: Import into Sqoop
  • 3.8 Sqoop Connectors
  • 3.9 Demo: Importing and Exporting Data from MySQL to HDFS
  • Apache Sqoop
  • 3.9 Apache Flume
  • 3.10 Flume Model
  • 3.11 Scalability in Flume
  • 3.12 Components in Flume’s Architecture
  • 3.13 Configuring Flume Components
  • 3.15 Demo: Ingest Twitter Data
  • 3.14 Apache Kafka
  • 3.15 Aggregating User Activity Using Kafka
  • 3.16 Kafka Data Model
  • 3.17 Partitions
  • 3.18 Apache Kafka Architecture
  • 3.19 Producer Side API Example
  • 3.20 Consumer Side API
  • 3.21 Demo: Setup Kafka Cluster
  • 3.21 Consumer Side API Example
  • 3.22 Kafka Connect
  • 3.23 Key Takeaways
  • 3.26 Demo: Creating Sample Kafka Data Pipeline using Producer and Consumer
  • Knowledge Check
  • Data Ingestion into Big Data Systems and ETL
  • 4.1 Distributed Processing MapReduce Framework and Pig
  • 4.2 Distributed Processing in MapReduce
  • 4.3 Word Count Example
  • 4.4 Map Execution Phases
  • 4.5 Map Execution Distributed Two Node Environment
  • 4.6 MapReduce Jobs
  • 4.7 Hadoop MapReduce Job Work Interaction
  • 4.8 Setting Up the Environment for MapReduce Development
  • 4.9 Set of Classes
  • 4.10 Creating a New Project
  • 4.11 Advanced MapReduce
  • 4.12 Data Types in Hadoop
  • 4.13 OutputFormats in MapReduce
  • 4.14 Using Distributed Cache
  • 4.15 Joins in MapReduce
  • 4.16 Replicated Join
  • 4.17 Introduction to Pig
  • 4.18 Components of Pig
  • 4.19 Pig Data Model
  • 4.20 Pig Interactive Modes
  • 4.21 Pig Operations
  • 4.22 Various Relations Performed by Developers
  • 4.23 Demo: Analyzing Web Log Data Using MapReduce
  • 4.24 Demo: Analyzing Sales Data and Solving KPIs using PIG
  • Apache Pig
  • 4.25 Demo: Wordcount
  • 4.26 Key takeaways
  • Knowledge Check
  • Distributed Processing - MapReduce Framework and Pig
  • 5.1 Apache Hive
  • 5.2 Hive SQL over Hadoop MapReduce
  • 5.3 Hive Architecture
  • 5.4 Interfaces to Run Hive Queries
  • 5.5 Running Beeline from Command Line
  • 5.6 Hive Metastore
  • 5.7 Hive DDL and DML
  • 5.8 Creating New Table
  • 5.9 Data Types
  • 5.10 Validation of Data
  • 5.11 File Format Types
  • 5.12 Data Serialization
  • 5.13 Hive Table and Avro Schema
  • 5.14 Hive Optimization Partitioning Bucketing and Sampling
  • 5.15 Non Partitioned Table
  • 5.16 Data Insertion
  • 5.17 Dynamic Partitioning in Hive
  • 5.18 Bucketing
  • 5.19 What Do Buckets Do
  • 5.20 Hive Analytics UDF and UDAF
  • Assisted Practice: Synchronization
  • 5.21 Other Functions of Hive
  • 5.22 Demo: Real-Time Analysis and Data Filteration
  • 5.23 Demo: Real-World Problem
  • 5.24 Demo: Data Representation and Import using Hive
  • 5.25 Key Takeaways
  • Knowledge Check
  • Apache Hive
  • 6.1 NoSQL Databases HBase
  • 6.2 NoSQL Introduction
  • Demo: Yarn Tuning
  • 6.3 HBase Overview
  • 6.4 HBase Architecture
  • 6.5 Data Model
  • 6.6 Connecting to HBase
  • HBase Shell
  • 6.7 Key Takeaways
  • Knowledge Check
  • NoSQL Databases - HBase
  • 7.1 Basics of Functional Programming and Scala
  • 7.2 Introduction to Scala
  • 7.3 Demo: Scala Installation
  • 7.3 Functional Programming
  • 7.4 Programming with Scala
  • Demo: Basic Literals and Arithmetic Operators
  • Demo: Logical Operators
  • 7.5 Type Inference Classes Objects and Functions in Scala
  • Demo: Type Inference Functions Anonymous Function and Class
  • 7.6 Collections
  • 7.7 Types of Collections
  • Demo: Five Types of Collections
  • Demo: Operations on List
  • 7.8 Scala REPL
  • Assisted Practice: Scala REPL
  • Demo: Features of Scala REPL
  • 7.9 Key Takeaways
  • Knowledge Check
  • Basics of Functional Programming and Scala
  • 8.1 Apache Spark Next Generation Big Data Framework
  • 8.2 History of Spark
  • 8.3 Limitations of MapReduce in Hadoop
  • 8.4 Introduction to Apache Spark
  • 8.5 Components of Spark
  • 8.6 Application of In-Memory Processing
  • 8.7 Hadoop Ecosystem vs Spark
  • 8.8 Advantages of Spark
  • 8.9 Spark Architecture
  • 8.10 Spark Cluster in Real World
  • 8.11 Demo: Running a Scala Programs in Spark Shell
  • 8.12 Demo: Setting Up Execution Environment in IDE
  • 8.13 Demo: Spark Web UI
  • 8.14 Key Takeaways
  • Knowledge Check
  • Apache Spark Next Generation Big Data Framework
  • 9.1 Processing RDD
  • 9.1 Introduction to Spark RDD
  • 9.2 RDD in Spark
  • 9.3 Creating Spark RDD
  • 9.4 Pair RDD
  • 9.5 RDD Operations
  • 9.6 Demo: Spark Transformation Detailed Exploration Using Scala Examples
  • 9.7 Demo: Spark Action Detailed Exploration Using Scala
  • 9.8 Caching and Persistence
  • 9.9 Storage Levels
  • 9.10 Lineage and DAG
  • 9.11 Need for DAG
  • 9.12 Debugging in Spark
  • 9.13 Partitioning in Spark
  • 9.14 Scheduling in Spark
  • 9.15 Shuffling in Spark
  • 9.16 Sort Shuffle
  • 9.17 Aggregating Data with Pair RDD
  • 9.18 Demo: Spark Application with Data Written Back to HDFS and Spark UI
  • 9.19 Demo: Changing Spark Application Parameters
  • 9.20 Demo: Handling Different File Formats
  • 9.21 Demo: Spark RDD with Real-World Application
  • 9.22 Demo: Optimizing Spark Jobs
  • Assisted Practice: Changing Spark Application Params
  • 9.23 Key Takeaways
  • Knowledge Check
  • Spark Core Processing RDD
  • 10.1 Spark SQL Processing DataFrames
  • 10.2 Spark SQL Introduction
  • 10.3 Spark SQL Architecture
  • 10.4 DataFrames
  • 10.5 Demo: Handling Various Data Formats
  • 10.6 Demo: Implement Various DataFrame Operations
  • 10.7 Demo: UDF and UDAF
  • 10.8 Interoperating with RDDs
  • 10.9 Demo: Process DataFrame Using SQL Query
  • 10.10 RDD vs DataFrame vs Dataset
  • Processing DataFrames
  • 10.11 Key Takeaways
  • Knowledge Check
  • Spark SQL - Processing DataFrames
  • 11.1 Spark MLlib Modeling Big Data with Spark
  • 11.2 Role of Data Scientist and Data Analyst in Big Data
  • 11.3 Analytics in Spark
  • 11.4 Machine Learning
  • 11.5 Supervised Learning
  • 11.6 Demo: Classification of Linear SVM
  • 11.7 Demo: Linear Regression with Real World Case Studies
  • 11.8 Unsupervised Learning
  • 11.9 Demo: Unsupervised Clustering K-Means
  • Assisted Practice: Unsupervised Clustering K-means
  • 11.10 Reinforcement Learning
  • 11.11 Semi-Supervised Learning
  • 11.12 Overview of MLlib
  • 11.13 MLlib Pipelines
  • 11.14 Key Takeaways
  • Knowledge Check
  • Spark MLLib - Modeling BigData with Spark
  • 12.1 Stream Processing Frameworks and Spark Streaming
  • 12.1 Streaming Overview
  • 12.2 Real-Time Processing of Big Data
  • 12.3 Data Processing Architectures
  • 12.4 Demo: Real-Time Data Processing
  • 12.5 Spark Streaming
  • 12.6 Demo: Writing Spark Streaming Application
  • 12.7 Introduction to DStreams
  • 12.8 Transformations on DStreams
  • 12.9 Design Patterns for Using ForeachRDD
  • 12.10 State Operations
  • 12.11 Windowing Operations
  • 12.12 Join Operations stream-dataset Join
  • 12.13 Demo: Windowing of Real-Time Data Processing
  • 12.14 Streaming Sources
  • 12.15 Demo: Processing Twitter Streaming Data
  • 12.16 Structured Spark Streaming
  • 12.17 Use Case Banking Transactions
  • 12.18 Structured Streaming Architecture Model and Its Components
  • 12.19 Output Sinks
  • 12.20 Structured Streaming APIs
  • 12.21 Constructing Columns in Structured Streaming
  • 12.22 Windowed Operations on Event-Time
  • 12.23 Use Cases
  • 12.24 Demo: Streaming Pipeline
  • Spark Streaming
  • 12.25 Key Takeaways
  • Knowledge Check
  • Stream Processing Frameworks and Spark Streaming
  • 13.1 Spark GraphX
  • 13.2 Introduction to Graph
  • 13.3 Graphx in Spark
  • 13.4 Graph Operators
  • 13.5 Join Operators
  • 13.6 Graph Parallel System
  • 13.7 Algorithms in Spark
  • 13.8 Pregel API
  • 13.9 Use Case of GraphX
  • 13.10 Demo: GraphX Vertex Predicate
  • 13.11 Demo: Page Rank Algorithm
  • 13.12 Key Takeaways
  • Knowledge Check
  • Spark GraphX
  • 13.14 Project Assistance
  • Car Insurance Analysis
  • Transactional Data Analysis
  • K-Means clustering for telecommunication domain

Big Data Hadoop Online Training Course – FAQs

Businesses collect extensive data in different formats from various data sources. This collected data can be structured, semi-structured, and unstructured which is difficult to process with the help of traditional techniques. However, with the methods and techniques of big data and analytics, this collected information can be better processed to derive insights and overcome business challenges by making better business decisions.

Hadoop is an open-source framework where businesses can store and process data in a parallel format and in a distributed environment. Furthermore, the Hadoop framework offers local computation and low-cost storage, and it scales up data from one server to a large number of machines.

With an aim to be a more advanced product that Hadoop, most organizations consider Spark which is an open-source framework. It offers several interconnected systems, platforms, and standards for big data projects.

Most organizations use the Hadoop framework for the operations of big data. However, being a beginner, taking your first step towards your dream career – Big Data can be quite challenging. Therefore, we always advise our students to learn the basics of Big Data and Hadoop before they begin with the main course curriculum. Also, it is essential to meet the pre-requisites of the course. Adding to this, you can go through the YouTube videos and tutorials to get a basic understanding of Big Data and Hadoop. However, Hatigen has designed a course curriculum in order to train you right from the fundamental concepts.

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