Big Data and Hadoop: Essential Skills for Data Engineers

Course Overview

The “Big Data and Hadoop: Essential Skills for Data Engineers” course is designed to equip participants with the knowledge and practical skills necessary to navigate the complexities of Big Data technologies, specifically focusing on the Hadoop ecosystem.

Throughout this comprehensive training program, participants will explore the foundational concepts of Big Data and how Hadoop serves as a powerful framework for distributed data processing. The course covers key topics including the architecture of Hadoop, the MapReduce framework, data ingestion, and advanced data modeling techniques. Participants will also gain proficiency in using essential tools such as Apache Hive, Apache Pig, and Apache Spark to analyze and visualize data.

Join us in this engaging and informative course to unlock your potential in the world of Big Data and Hadoop!

Launch your career in Big Data and Hadoop by developing in-demand skills and become job-ready in 30 hours or less.  

Highlights

Upgrade your career with top notch training 

• Enhance Your Skills: Gain invaluable training that prepares you for success.
• Instructor-Led Training: Engage in interactive sessions that include hands-on exercises for practical experience.
• Flexible Online Format: Participate in the course from the comfort of your home or office.
• Accessible Learning Platform: Access course content on any device through our Learning Management System (LMS).
• Flexible Schedule: Enjoy a schedule that accommodates your personal and professional commitments.
• Job Assistance: Benefit from comprehensive support, including resume preparation and mock interviews to help you secure a position in the industry.

Outcomes 

By the end of this course, participants will be equipped with:  

• Proficient Understanding of Big Data Concepts: Participants will have a clear understanding of what Big Data is, its characteristics, significance, and applications across various industries.
• Mastery of Hadoop Architecture: Learners will be able to explain and navigate the Hadoop ecosystem, including its architecture and components like HDFS, MapReduce, and YARN.
• Ability to Perform Data Ingestion and Transformation: Participants will effectively connect to diverse data sources and perform data ingestion, transformation, and cleansing techniques using tools like Apache Pig and Hive.
• Advanced Data Modeling Skills: Learners will create complex relationships between tables and set up data models that support robust data analysis.
• Proficiency in MapReduce Programming: Participants will develop and optimize MapReduce jobs, leveraging advanced techniques for efficient data processing.
• Utilization of Apache Hive: Learners will write and execute HiveQL queries to perform data analysis, create tables, and effectively manage large datasets in Hive.
• Experience with Apache Spark: Participants will gain foundational skills in using Apache Spark for distributed data processing, including the creation and manipulation of RDDs and DataFrames.
• Performance Optimization Techniques: Learners will understand best practices for optimizing performance in both Hadoop and Spark environments to ensure efficient data processing and analysis.
• Familiarity with Ecosystem Tools: Learners will gain insights into various ecosystem tools and frameworks for Big Data processing, such as Apache Kafka, Flink, and real-time processing options.

Key Learnings

• Grasp the essential concepts of Big Data, including its characteristics, challenges, and significance in today’s data-driven environments.
• Learn the architecture of Hadoop, including its key components such as HDFS (Hadoop Distributed File System), MapReduce, and YARN (Yet Another Resource Negotiator).
• Gain skills in connecting to various data sources, performing data ingestion, and transforming data using Hadoop tools.
• Develop the ability to create complex data models by establishing and managing relationships between different data tables.
• Understand the MapReduce programming model and learn to write, optimize, and troubleshoot MapReduce jobs for efficient data processing. Gain proficiency in using Apache Pig to write scripts that facilitate data processing tasks across Hadoop.
• Learn to use Apache Hive for creating and executing queries using HiveQL to analyze large datasets stored in Hadoop. 
• Explore the integration of Apache Spark for distributed data processing, including working with DataFrames and Spark SQL for enhanced analysis.

Pre-requisites 

• Understanding SQL (Structured Query Language) is essential for working with databases and querying data.
• An understanding of data modeling, data types, and data handling techniques will be helpful.

Job roles and career paths 

This training will equip you for the following job roles and career paths: 

• Hadoop Developer
• Big Data Engineer
• Data Scientist
• Data Analyst
• Data Architect

Big Data Hadoop Training 

The need for Big Data and Hadoop experts is growing because businesses are using large-scale data processing more. Companies want professionals to manage big data, improve processing systems, and find valuable insights. Jobs like Big Data Engineer and Hadoop Developer are in high demand and will keep increasing as data and analysis needs expand.