Big Data & Hadoop

Course Overview

With Codec Networks Big Data & Hadoop trainings, gain skills in data-driven business strategy and learn tools / techniques to Big Data Hadoop technology falls into four major roles: analysts, scientists, developer and administrator, its anticipated to grow by five-fold in next few years and will sense an increased temptation of great job prospects with big data sector.

Big Data or Hadoop is often characterized by 3Vs: the extreme volume of data, the wide variety of data types and the velocity at which the data must be processed. Big Data has grown in significance over the last few years because of the evasiveness of its application, across areas ranging from weather forecasting to analyzing business trends, fighting crime and preventing epidemics etc. Big data sets are so large that traditional data management tools are incapable of analyzing all the data effectively and processing valuable information out of it. Hadoop is an open source java framework that enables distributed parallel processing of large volume of data across servers which has emerged as the solution to extract potential value from all this data.

The need for big data velocity imposes unique demands on the underlying compute infrastructure. The computing power required to quickly process huge volumes and varieties of data can overwhelm a single server or server cluster. Organizations must apply adequate compute power to big data tasks to achieve the desired velocity. This can potentially demand hundreds or thousands of servers that can distribute the work and operate collaboratively.

Big-Data and Hadoop Administrator

Secure coding training

Administrator Training course for Apache Hadoop provides participants with a comprehensive understanding of all the steps necessary to operate and maintain a Hadoop cluster. The course topics include Introduction to Hadoop and its Architecture, MapReduce and HDFS and MapReduce Abstraction. From installation and configuration through load balancing and tuning, this training course is the best preparation for the real-world challenges faced by Hadoop administrators. It further covers best practices to configure, deploy, administer, maintain, monitor and troubleshoot a Hadoop Cluster.

After completing this course, student will be able to:

  • Understand Hadoop main components and Architecture
  • Deep dive into Hadoop Distributed File System (HDFS)
  • Understand concepts of Yarn
  • Understand MapReduce abstraction and its working
  • Plan and Deploy a Hadoop cluster
  • Optimize Hadoop cluster for high performance, based on specific job requirements
  • Monitor Hadoop cluster and Execute routine Administration procedures
  • Handle Hadoop component failures and recoveries
  • Determining the correct hardware and infrastructure for your cluster
  • How to load data into the cluster from dynamically-generated files using Flume and from RDBMS using Sqoop
  • Configuring the Fair Scheduler to provide service-level agreements for multiple users of a cluster
  • Best practices for preparing and maintaining Apache Hadoop in production
  • Troubleshooting, diagnosing, tuning, and solving Hadoop issues

Who Should Attend

This course is best suited to systems administrators and IT managers who have basic Linux experience. Fundamental knowledge of any programming language and Linux environment. Participants should know how to navigate and modify files within a Linux environment. Prior knowledge of Apache Hadoop is not required.

Modules Covered

  • Introduction
  • Hadoop Cluster Installation
  • The Hadoop Distributed File System (HDFS)
  • MapReduce and Spark on YARN
  • Hadoop Configuration and Daemon Logs
  • Getting Data Into HDFS
  • Planning Your Hadoop Cluster
  • Installing and Configuring Hive, Impala, and Pig
  • Hadoop Clients Including Hue
  • Advanced Cluster Configuration
  • Hadoop Security
  • Managing Resources
  • Cluster Maintenance
  • Cluster Monitoring and Troubleshooting

Course Duration

  • Regular Track : 5 Weeks (2 Hours/Day)
  • Weekend Track : 5 Weekends (4 Hours/Day)

Kits Include

  • Pre-course technical evaluation
  • SI-Android Tools Kit
  • Training Material (E-Books)
  • Certificate of Excellence from Codec Networks

Post Training Program (Codec Networks Specialty)

  • Live Project Work
  • Hand-over Labs & Practical's Checklist for review
  • Placement Assistance **
  • Discount Vouchers up to 15 - 25% for further training

Big-Data and Hadoop Science

Secure coding training

Data scientists build information platforms to provide deep insight and answer previously unimaginable questions. Spark and Hadoop are transforming how data scientists work by allowing interactive and iterative data analysis at scale. Learn how Spark and Hadoop enable data scientists to help companies reduce costs, increase profits, improve products, retain customers, and identify new opportunities.

This Big-Data and Hadoop Science using Spark course helps participants understand what data scientists do, the problems they solve, and the tools and techniques they use. Through in-class simulations, participants apply data science methods to real-world challenges in different industries and, ultimately, prepare for data scientist roles in the field.

Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, and develop concrete skills such as:

  • How to identify potential business use cases where data science can provide impactful results
  • How to obtain, clean and combine disparate data sources to create a coherent picture for analysis
  • What statistical methods to leverage for data exploration that will provide critical insight into your data
  • Debugging techniques and implementation of workflows and common algorithms
  • Where and when to leverage Hadoop streaming and Apache Spark for data science pipelines
  • What machine learning technique to use for a particular data science project
  • How to implement and manage recommenders using Spark‚Äôs MLlib, and how to set up and evaluate data experiments
  • What are the pitfalls of deploying new analytics projects to production

Who Should Attend

This course is suitable for developers, data analysts, and statisticians with basic knowledge of Apache Hadoop: HDFS, MapReduce, Hadoop Streaming, and Apache Hive as well as experience working in Linux environments.

Students should have proficiency in a scripting language; Python is strongly preferred, but familiarity with Perl or Ruby is sufficient.

Modules Covered

  • Data Science Overview
  • Use Cases
  • Project Lifecycle
  • Data Acquisition
  • Evaluating Input Data
  • Data Transformation
  • Data Analysis and Statistical Methods
  • Fundamentals of Machine Learning
  • Recommender Overview
  • Introduction to Apache Spark and MLlib
  • Implementing Recommenders with MLlib
  • Latent Factor Recommenders
  • Experimentation and Evaluation
  • Production Deployment and Beyond

Course Duration

  • Rgular Track : 5 Weeks (2 Hours/Day)
  • Weekend Track : 5 Weekends (4 Hours/Day)

Kits Include

  • Pre-course technical evaluation
  • SI-Android Tools Kit
  • Training Material (E-Books)
  • Certificate of Excellence from Codec Networks

Post Training Program (Codec Networks Specialty)

  • Live Project Work
  • Hand-over Labs & Practical's Checklist for review
  • Placement Assistance **
  • Discount Vouchers up to 15 - 25% for further training

Big-Data and Hadoop Analyst

Secure coding training

Apache Hive makes multi-structured data accessible to analysts, database administrators, and others without Java programming expertise. Apache Pig applies the fundamentals of familiar scripting languages to the Hadoop cluster. Impala enables real-time, interactive analysis of the data stored in Hadoop via a native SQL environment.

This data analyst training course focusing on Apache Pig, Hive and Impala will teach you to apply traditional data analytics and business intelligence skills to big data. This course presents the tools data professionals need to access, manipulate, transform, and analyze complex data sets using SQL and familiar scripting languages.

Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, learning topics such as:

  • The features that Pig, Hive, and Impala offer for data acquisition, storage, and analysis
  • The fundamentals of Apache Hadoop and data ETL (extract, transform, load), ingestion, and processing with Hadoop tools
  • How Pig, Hive, and Impala improve productivity for typical analysis tasks
  • Joining diverse datasets to gain valuable business insight
  • Performing real-time, complex queries on datasets

Who Should Attend

This course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators. Knowledge of SQL is assumed, as is basic Linux command-line familiarity. Knowledge of at least one scripting language (e.g., Bash scripting, Perl, Python, Ruby) would be helpful but is not essential.

Modules Covered

  • Hadoop Fundamentals
  • Introduction to Pig
  • Basic Data Analysis with Pig
  • Processing Complex Data with Pig
  • Multi-Dataset Operations with Pig
  • Pig Troubleshooting and Optimization
  • Introduction to Hive and Impala
  • Querying with Hive and Impala
  • Data Management
  • Data Storage and Performance
  • Relational Data Analysis with Hive and Impala
  • Working with Impala
  • Analyzing Text and Complex Data with Hive
  • Hive Optimization
  • Extending Hive
  • Choosing the Best Tool

Course Duration

  • Rgular Track : 5 Weeks (2 Hours/Day)
  • Weekend Track : 5 Weekends (4 Hours/Day)

Kits Include

  • Pre-course technical evaluation
  • SI-Android Tools Kit
  • Training Material (E-Books)
  • Certificate of Excellence from Codec Networks

Post Training Program (Codec Networks Specialty)

  • Live Project Work
  • Hand-over Labs & Practical's Checklist for review
  • Placement Assistance **
  • Discount Vouchers up to 15 - 25% for further training

Big-Data and Hadoop Developer

Secure coding training

Apache Hadoop is an open-source software framework written in Java for distributed storage and distributed processing of very large data sets on computer clusters built from commodity hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common and should be automatically handled by the framework. Apache Hadoop's MapReduce and HDFS components were inspired by Google papers on their MapReduce and Google File System.

The Hadoop framework itself is mostly written in the Java programming language, with some native code in C and command line utilities written as shell scripts. Though MapReduce Java code is common, any programming language can be used with "Hadoop Streaming" to implement the "map" and "reduce" parts of the user's program.[11] Other projects in the Hadoop ecosystem expose richer user interfaces.

This Developer training course for Hadoop Trainings delivers the key concepts and expertise necessary to create robust data processing applications using Apache Hadoop.

Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, learning topics such as:

  • MapReduce and the Hadoop Distributed File System (HDFS) and how to write MapReduce code
  • Best practices and considerations for Hadoop development
  • Understand concepts of Yarn
  • Debugging techniques and implementation of workflows and common algorithms
  • Plan and Deploy a Hadoop cluster
  • How to leverage Hive, Pig, Sqoop, Flume, Oozie and other projects from the Apache Hadoop ecosystem
  • Optimal hardware configurations and network considerations for building out maintaining and monitoring your Hadoop cluster
  • Advanced Hadoop API topics required for real-world data analysis

Who Should Attend

This course is intended and appropriate for developers who will be writing, maintaining, or optimizing Hadoop jobs Participants should have programming experience, preferably with Java. Understanding of common computer science concepts is a plus.

Modules Covered

  • The Motivation for Hadoop
  • Hadoop: Basic Concepts
  • Writing a MapReduce Program
  • Integrating Hadoop into the Workflow
  • Graph Manipulation in Hadoop
  • Using Hive and Pig
  • Delving Deeper Into the Hadoop API
  • Practical Development Tips and Techniques
  • Common MapReduce Algorithms
  • Advanced MapReduce Programming
  • Joining Data Sets in MapReduce Jobs
  • Creating Workflows with Oozie

Course Duration

  • Rgular Track : 5 Weeks (2 Hours/Day)
  • Weekend Track : 5 Weekends (4 Hours/Day)

Kits Include

  • Pre-course technical evaluation
  • SI-Android Tools Kit
  • Training Material (E-Books)
  • Certificate of Excellence from Codec Networks

Post Training Program (Codec Networks Specialty)

  • Live Project Work
  • Hand-over Labs & Practical's Checklist for review
  • Placement Assistance **
  • Discount Vouchers up to 15 - 25% for further training

Course related details

  • Pre-Course Technical Evaluation
  • Hand-Over Labs & Practical's Checklist for Review
  • Classroom Evaluation Tests
  • Candidate Wise Reporting & Post Assessment
  • Certificate of Excellence from Codec Networks
  • Live Project Work

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