Radicals.in

HADOOP DEV + SPARK & SCALA TRAINING IN KOCHI

Embark on a comprehensive journey into big data with our combined Hadoop Dev, Spark, and Scala training in Kochi. Dive deep into Hadoop development, Spark programming, and Scala language fundamentals. Our expert instructors will guide you through hands-on exercises, covering essential concepts and advanced techniques for building robust data processing applications. Gain proficiency in leveraging Hadoop ecosystem tools, Spark’s lightning-fast processing engine, and Scala’s expressive syntax. Whether you’re a beginner or experienced developer, our program caters to all skill levels, providing a solid foundation and practical insights.

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Why Radical Technologies

Live Online Training

Highly practical oriented training
Installation of Software On your System
24/7 Email and Phone Support
100% Placement Assistance until you get placed
Global Certification Preparation
Trainer Student Interactive Portal
Assignments and Projects Guided by Mentors
And Many More Features
Course completion certificate and Global Certifications are part of our all Master Program

Live Classroom Training

Weekend / Weekdays / Morning / Evening Batches
80:20 Practical and Theory Ratio
Real-life Case Studies
Easy Coverup if you missed any sessions
PSI | Kryterion | Redhat Test Centers
Life Time Video Classroom Access ( coming soon )
Resume Preparations and Mock Interviews
And Many More Features
Course completion certificate and Global Certifications are part of our all Master Program

Self Paced Training

Self Paced Learning
Learn 300+ Courses at Your Own Time
50000+ Satisfied Learners
Course Completion Certificate
Practical Labs Available
Mentor Support Available
Doubt Clearing Session Available
Attend Our Virtual Job Fair
10% Discounted Global Certification
Course completion certificate and Global Certifications are part of our all Master Program

Live Online Training

Highly practical oriented training
Installation of Software On your System
24/7 Email and Phone Support
100% Placement Assistance until you get placed
Global Certification Preparation
Trainer Student Interactive Portal
Assignments and Projects Guided by Mentors
And Many More Features
Course completion certificate and Global Certifications are part of our all Master Program

Live Classroom Training

Weekend / Weekdays / Morning / Evening Batches
80:20 Practical and Theory Ratio
Real-life Case Studies
Easy Coverup if you missed any sessions
PSI | Kryterion | Redhat Test Centers
Life Time Video Classroom Access ( coming soon )
Resume Preparations and Mock Interviews
And Many More Features
Course completion certificate and Global Certifications are part of our all Master Program

Self Test Training

300+ Technologies - Learn at your Convenience
4500+ High Quality Videos
Self Paced Training By Experts
Self Paced Hands-On Practical LABS
Cloud Sand Boxes
Do Projects and Assignments with Live LABS
100+ Exam Simulators & Discounted Vouchers
Live Mentor Support - By 10+ Years Experts
Course completion certificate and Global Certifications are part of our all Master Program

Why enroll for LINUX RHEL 8 course ?

Course Benefits

Solution for BigData Problem
Open Source Technology
Based on open source platforms
Contains several tool for entire ETL data processing Framework
It can process Distributed data and no need to store entire data in centralized storage as it is required for SQL based tools.

Designations

Want to become Engineer?

Want to become Engineer?

Want to become Engineer?

WhyTerraform ?

Multi-Cloud Support

Terraform supports multiple cloud providers, including AWS, Azure, Google Cloud Platform (GCP), and others. This allows you to use a single tool and consistent workflow across different cloud environments, enabling hybrid and multi-cloud deployments.

Declarative Configuration

Terraform uses a declarative language to define infrastructure configurations. This means you specify the desired state of your infrastructure, and Terraform handles the provisioning and management to achieve that state. This approach is intuitive and allows for easier understanding and maintenance of infrastructure code.

Resource Graph

Terraform builds a dependency graph of your infrastructure resources based on their relationships and dependencies. This enables Terraform to determine the correct order of resource provisioning and manage complex infrastructures with ease.

Modularity and Reusability

Terraform encourages modularization and code reuse through the use of modules. Modules are self-contained units of Terraform configurations that can be reused across projects, teams, and environments. This promotes consistency, reduces duplication, and speeds up development.

State Management

Terraform maintains a state file that tracks the current state of your infrastructure. This state file is used to plan and apply changes, track resource attributes, and manage updates. Terraform's state management ensures consistency and facilitates collaboration among team members.

Plan and Apply Workflow

Terraform follows a plan and apply workflow, where changes to infrastructure are first planned and previewed before being applied. This allows you to review the proposed changes, identify potential issues, and apply changes safely, minimizing the risk of unintended consequences.

Extensibility

Terraform is highly extensible and integrates with a wide range of third-party tools and services. This includes integrations with configuration management tools, CI/CD pipelines, monitoring solutions, and more, allowing you to build comprehensive infrastructure automation workflows. While other IaC tools offer similar capabilities, Terraform's multi-cloud support, declarative configuration, resource graph, modularity, state management, workflow, and extensibility make it a popular choice for infrastructure automation in diverse environments. Additionally, Terraform's active community, robust documentation, and frequent updates contribute to its widespread adoption and continued development.

About your Terraform Certification Course

Terraform Skills Covered

  • State Management

  • Terraform Modules

  • Dependency Management

  • Terraform CLI

  • Terraform Configuration Language (HCL)

  • Infrastructure as Code (IaC) Principles

  • Resource Provisioning

  • Terraform Providers

  • Terraform Workspaces

  • Terraform Best Practices

Curriculum Designed by Experts

Hadoop Dev + Spark & Scala Course Syllabus

Training Syllabus / Big data

Training Syllabus

HADOOP DEV + SPARK & SCALA + NoSQL + Splunk + HDFS (Storage) + YARN (Hadoop Processing Framework) + MapReduce using Java (Processing Data) +  Apache Hive + Apache Pig + HBASE (Real NoSQL ) + Sqoop + Flume + Oozie  + Kafka With ZooKeeper + Cassandra + MongoDB + Apache Splunk

Big data

  • Distributed computing
  • Data management – Industry Challenges
  • Overview of Big Data
  • Characteristics of Big Data
  • Types of data
  • Sources of Big Data
  • Big Data examples
  • What is streaming data?
  • Batch vs Streaming data processing
  • Overview of Analytics
  • Big data Hadoop opportunities
Hadoop / HDFS (Storage)

Hadoop

  • Why we need Hadoop
  • Data centers and Hadoop Cluster overview
  • Overview of Hadoop Daemons
  • Hadoop Cluster and Racks
  • Learning Linux required for Hadoop
  • Hadoop ecosystem tools overview
  • Understanding the Hadoop configurations and Installation.

HDFS (Storage)

  • HDFS
  • HDFS Daemons – Namenode, Datanode, Secondary Namenode
  • Hadoop FS and Processing Environment’s UIs
  • Fault Tolerant 
  • High Availability
  • Block Replication
  • How to read and write files
  • Hadoop FS shell commands
YARN (Hadoop Processing Framework) / MapReduce using Java (Processing Data)

YARN (Hadoop Processing Framework)

  • YARN
  • YARN Daemons – Resource Manager, NodeManager etc.
  • Job assignment & Execution flow

MapReduce using Java (Processing Data)

  • The introduction of MapReduce.
  • MapReduce Architecture
  • Data flow in MapReduce
  • Understand Difference Between Block and InputSplit
  • Role of RecordReader
  • Basic Configuration of MapReduce
  • MapReduce life cycle
  • How MapReduce Works
  • Writing and Executing the Basic MapReduce Program using Java
  • Submission & Initialization of MapReduce Job.
  • File Input/Output Formats in MapReduce Jobs
  • Text Input Format
  • Key Value Input Format
  • Sequence File Input Format
  • NLine Input Format
  • Joins
  • Map-side Joins
  • Reducer-side Joins
  • Word Count Example(or) Election Vote Count
  • Will cover five to Ten Map Reduce Examples with real time data.
Apache Hive / Apache Pig

Apache Hive

  • Data warehouse basics
  • OLTP vs OLAP Concepts
  • Hive
  • Hive Architecture
  • Metastore DB and Metastore Service
  • Hive Query Language (HQL)
  • Managed and External Tables
  • Partitioning & Bucketing
  • Query Optimization
  • Hiveserver2 (Thrift server)
  • JDBC , ODBC connection to Hive
  • Hive Transactions
  • Hive UDFs
  • W