100% OFF-Big Data: Hadoop| MapReduce| Hive| Pig| NoSQL| Mahout| Oozie

Welcome to our comprehensive course on Big Data and Hadoop! In this course, we dive deep into the world of big data technologies, focusing on Hadoop, one of the most powerful and widely used frameworks for processing large-scale data sets.

Throughout this course, you’ll learn the fundamentals of Hadoop, including its architecture, components, and applications. We’ll cover everything from the basics of big data and Hadoop to advanced topics such as MapReduce, HDFS, Hive, Pig, and more.

Whether you’re a beginner looking to understand the basics of big data or an experienced professional seeking to enhance your skills in Hadoop ecosystem technologies, this course has something for everyone. Get ready to explore the exciting field of big data and unleash the power of Hadoop for solving real-world data challenges. Join us on this journey as we unlock the potential of big data together! We will learn the followings section-wise:

Section 1: Big Data and Hadoop Training Introduction

In this section, students are introduced to the foundational concepts of Big Data and Hadoop training. They begin by understanding the significance of Hadoop in handling large volumes of data efficiently. Through a series of introductory sessions, learners familiarize themselves with the landscape of Big Data and Hadoop technology, setting the stage for more in-depth exploration in subsequent sections.

Section 2: Hadoop Architecture and HDFS

Moving on to the architecture of Hadoop and its distributed file system (HDFS), this section delves into the core components of Hadoop 1.0. Students gain insights into the storage layer of Hadoop and the placement policies governing data distribution across the cluster. Through hands-on exercises and cluster setup tutorials, learners develop a solid understanding of Hadoop’s architecture and its practical implementation in real-world scenarios.

Section 3: MapReduce Fundamentals

In this section, students dive into the fundamentals of MapReduce, a core component of Hadoop for processing and analyzing large datasets in parallel. Through a series of lectures, learners explore key concepts such as secondary sorting, composite keys, and the importance of partitioning. They gain hands-on experience with MapReduce programming by working on sample programs, understanding map-side joins, and implementing combiners for efficient data processing.

Section 4: MapReduce Advanced

Building upon the foundational knowledge of MapReduce, this section delves into more advanced topics and techniques for optimizing MapReduce programs. Students learn about running and debugging MapReduce programs, working with different file formats, and leveraging advanced MapReduce functionalities for tasks such as log processing and data export. By the end of this section, learners are equipped with the skills to tackle complex data processing challenges using MapReduce.

Section 5: HIVE Fundamentals

In this section, students are introduced to Apache Hive, a data warehouse infrastructure built on top of Hadoop for querying and analyzing large datasets stored in HDFS. Through a series of lectures, learners explore Hive’s architecture, data modeling concepts, and query language (HiveQL). They learn how to create and manage databases and tables, perform data loading operations, and execute various SQL-like queries to extract insights from structured data.

Section 6: Hive Advanced

Expanding upon the foundational knowledge of Hive, this section covers advanced topics and techniques for optimizing Hive queries and data processing workflows. Students learn about partitioning, bucketing, indexing, and other performance optimization strategies to enhance query performance and scalability. Additionally, they explore advanced features such as table sampling, archiving, and working with slowly changing dimensions (SCD) to address complex data analysis requirements effectively.

Section 7: PIG Fundamentals

In this section, students explore Apache Pig, a high-level data flow scripting language for processing and analyzing large datasets in Hadoop. Through a series of lectures, learners discover Pig’s features, data types, and operators for expressing data transformations and analysis tasks concisely. They gain hands-on experience with loading and storing data, grouping and joining operations, and leveraging built-in functions to perform data manipulation tasks efficiently.

Section 8: PIG Advanced

Building upon the foundational knowledge of Pig, this section delves into advanced topics and techniques for optimizing Pig scripts and data processing workflows. Students learn about debugging techniques, leveraging user-defined functions (UDFs), and working with complex data types to handle diverse data processing requirements effectively. Additionally, they explore strategies for improving Pig script performance and scalability in large-scale data processing environments.

Section 9: NoSQL Fundamentals

This section provides an introduction to NoSQL databases, covering their history, characteristics, and benefits in handling diverse and rapidly changing data types. Students learn about different types of NoSQL databases, including document-based, columnar, and graph databases, and understand their suitability for various use cases. Additionally, learners explore key concepts such as schema flexibility, consistency models, and distributed architecture, gaining insights into managing and querying data in NoSQL environments effectively.

Section 10: Apache Mahout

In this section, students explore Apache Mahout, a scalable machine learning library built on top of Hadoop for building and deploying machine learning models at scale. Through a series of lectures and hands-on exercises, learners discover Mahout’s architecture, algorithms, and use cases in real-world scenarios. They gain practical experience in implementing recommendation systems, clustering, classification, and other machine learning tasks using Mahout’s APIs and tools.

Section 11: Apache Oozie

This section introduces Apache Oozie, a workflow scheduler system for managing Hadoop jobs and data processing workflows. Students learn about Oozie’s architecture, workflow definition language, and various workflow actions for coordinating and orchestrating complex data processing pipelines. Through hands-on exercises, learners gain proficiency in creating, scheduling, and monitoring workflows using Oozie, enabling them to automate and streamline data processing tasks effectively.

Section 12: Apache Flume

In this section, students explore Apache Flume, a distributed, reliable, and available system for efficiently collecting, aggregating, and moving large volumes of log data from various sources to centralized data stores. Through lectures and practical demonstrations, learners understand Flume’s architecture, components, and data flow model for ingesting and processing log data in Hadoop environments. They gain hands-on experience in configuring Flume agents, defining data ingestion pipelines, and monitoring data flows for real-time log processing.

Section 13: Apache Storm

This section introduces Apache Storm, a distributed real-time stream processing system for processing high-velocity data streams with low latency and fault tolerance. Students learn about Storm’s architecture, components, and stream processing model, including spouts, bolts, and topologies. Through hands-on exercises, learners gain practical experience in setting up Storm clusters, developing and deploying stream processing topologies, and handling real-time data streams for various use cases such as real-time analytics, event processing, and more.

Section 14: Apache Avro

In this section, students delve into Apache Avro, a data serialization system that provides rich data structures, a compact binary format, and a JSON-like data model for efficient data exchange between applications. Learners explore Avro’s schema definition language, supported data types, and integration with other big data tools like Apache Sqoop. Through practical examples and exercises, students gain proficiency in using Avro for data serialization, schema evolution, and interoperability in Hadoop ecosystems.

Section 15: Apache Spark Fundamentals

This section provides an introduction to Apache Spark, a fast and general-purpose cluster computing framework for processing large-scale data sets with high speed and ease of use. Students learn about Spark’s core components, including Spark Context, Resilient Distributed Datasets (RDDs), and transformations/actions for distributed data processing. Through hands-on labs and demonstrations, learners gain practical experience in working with RDDs, applying transformations/actions, and performing basic data analysis tasks using Spark’s APIs.

Section 16: Apache Spark Advanced

Building upon the fundamentals, this section delves deeper into advanced concepts and features of Apache Spark, empowering students to tackle complex data processing and analytics challenges efficiently. Learners explore topics such as connecting Spark to external data sources, working with Spark SQL for structured data processing, and leveraging Spark’s machine learning and graph processing libraries for advanced analytics tasks. Through a combination of lectures and hands-on exercises, students develop advanced skills in building end-to-end data processing pipelines and deploying machine learning models using Spark.

Section 17: Hadoop Project 01 – Sales Data Analysis

In this project-based section, students apply their knowledge of Hadoop and related technologies to analyze sales data and derive actionable insights. Learners work through various problem statements, such as calculating average sales, analyzing sales trends, and segmenting customers based on purchasing behavior. By completing this project, students gain practical experience in data analysis, Hadoop ecosystem tools, and real-world data processing scenarios.

Section 18: Hadoop Project 02 – Tourism Survey Analysis

Continuing with project-based learning, this section focuses on analyzing tourism survey data using Hadoop technologies. Students work on tasks such as calculating average spending by tourists, analyzing demographics, and identifying trends in tourism preferences. Through hands-on exercises and guided projects, learners apply their skills in data manipulation, querying, and visualization to derive valuable insights for the tourism industry.

Section 19: Hadoop Project 03 – Faculty Data Management

In this project, students tackle the task of managing faculty data within an educational institution using Hadoop-based solutions. Learners work on tasks such as data ingestion, schema design, data transformation, and querying to create a comprehensive faculty data management system. By completing this project, students gain practical experience in designing and implementing data management solutions using Hadoop technologies.

Section 20: Hadoop Project 04 – E-Commerce Sales Analysis

In this project, students dive into analyzing e-commerce sales data using Hadoop tools and techniques. They work on tasks such as customer segmentation, product performance analysis, and sales forecasting to extract valuable insights for e-commerce businesses. By applying their knowledge of Hadoop ecosystem components, data processing techniques, and analytics methodologies, students gain hands-on experience in solving real-world challenges in the e-commerce domain.

Section 21: Hadoop Project 05 – Salary Analysis

This project revolves around analyzing salary data using Hadoop-based approaches. Students engage in tasks such as identifying patterns in salary distributions, calculating department-wise salary averages, and analyzing trends in employee compensation. Through practical exercises and data analysis tasks, learners enhance their skills in data manipulation, statistical analysis, and deriving actionable insights from large-scale salary datasets.

Section 22: Hadoop Project 06 – Health Survey Analysis using HDFS

In this project, students undertake the analysis of health survey data using Hadoop Distributed File System (HDFS) and related technologies. They work on tasks such as data preprocessing, trend analysis, and geographical mapping of health indicators to gain insights into public health trends and issues. Through hands-on projects and data visualization tasks, learners develop proficiency in leveraging Hadoop for health data analysis and decision-making in healthcare settings.

Section 23: Hadoop Project 07 – Traffic Violation Analysis

In this project, students explore the analysis of traffic violation data using Hadoop tools and frameworks. They work on tasks such as data ingestion from various sources, geospatial analysis of traffic violations, and identifying patterns in traffic offense data. By applying Hadoop-based solutions to traffic data analysis, learners gain practical experience in understanding traffic patterns, improving road safety, and implementing data-driven interventions to manage traffic violations effectively.

Section 24: Hadoop Project 08 – PIG/MapReduce – Analyze Loan Dataset

This project focuses on analyzing a loan dataset using a combination of Apache Pig and MapReduce techniques. Students engage in tasks such as data preprocessing, calculating risk metrics, and generating reports on loan performance. Through hands-on exercises and coding assignments, learners develop proficiency in using Pig Latin scripts, implementing MapReduce algorithms, and performing analytics on large-scale loan datasets to support financial decision-making processes.

Section 25: Hadoop Project:09 – HIVE – Case Study on Telecom Industry

In this project, students delve into a case study focused on analyzing telecom industry data using Apache Hive. They work on tasks such as data modeling, query optimization, and performance tuning to extract meaningful insights from telecom datasets. Through hands-on exercises and SQL-based queries in Hive, learners gain practical experience in data warehousing, business intelligence, and decision support systems tailored to the telecommunications domain.

Section 26: Hadoop Project:10 – HIVE/MapReduce – Customers Complaints Analysis

This project revolves around analyzing customer complaints data using a combination of Hive and MapReduce techniques. Students engage in tasks such as data preprocessing, sentiment analysis, and trend identification to understand customer feedback patterns and improve service quality. By leveraging Hive for data querying and MapReduce for complex analytics, learners gain valuable skills in customer analytics and enhancing customer experience in various industries.

Section 27: Hadoop Project 11 – HIVE/PIG/MapReduce/Sqoop – Social Media Analysis

In this project, students tackle the analysis of social media data using a combination of Hadoop ecosystem tools including Hive, Pig, MapReduce, and Sqoop. They work on tasks such as data extraction, sentiment analysis, and user behavior modeling to understand trends and patterns in social media interactions. Through practical exercises and data processing tasks, learners gain insights into social media analytics, content optimization, and audience engagement strategies.

Section 28: Hadoop Project 12 – HIVE/PIG – Sensor Data Analysis

This project focuses on analyzing sensor data using Apache Hive and Pig for data processing and analytics. Students engage in tasks such as data cleaning, anomaly detection, and predictive modeling to extract actionable insights from sensor-generated data streams. By applying Hadoop-based solutions to sensor data analysis, learners gain practical experience in IoT (Internet of Things) analytics and leveraging sensor data for various applications such as predictive maintenance and environmental monitoring.

Section 29: Hadoop Project 13 – PIG/MapReduce – Youtube Data Analysis

In this project, students undertake the analysis of YouTube data using a combination of Pig and MapReduce. They work on tasks such as data preprocessing, trend identification, and user behavior analysis to uncover insights into YouTube content consumption patterns and audience engagement. By leveraging Pig for data transformation and MapReduce for complex analytics, learners gain practical experience in big data analytics applied to digital media platforms.

Section 30: Hadoop and HDFS Fundamentals on Cloudera

This section provides foundational knowledge about Hadoop and HDFS (Hadoop Distributed File System) using the Cloudera environment. Students learn about big data concepts, distributed storage, and processing, along with practical aspects such as metadata configuration and accessing HDFS through various interfaces. Through hands-on exercises and exploration of Cloudera’s Hadoop ecosystem, learners gain a solid understanding of Hadoop fundamentals and its practical applications in real-world scenarios.

Section 31: Log Data Analysis with Hadoop

In this section, students delve into log data analysis using Hadoop tools and techniques. They learn to summarize and process log files efficiently using MapReduce programs, gaining insights into system performance, user behavior, and security incidents. By writing MapReduce programs and executing them on log data, learners develop skills in log data analysis, troubleshooting, and system optimization essential for IT operations and security management.