Big Data and Hadoop: Essential Skills for Data Engineers

Learn to manage and process large datasets with Hadoop and its ecosystem tools. Gain practical skills in HDFS, Hive, Pig, and Spark to become job-ready as a data engineer.

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Mode of Training

Online – Virtual (Live, Instructor Led, Real-Time Learning with Q&A and Discussions)

Certification

After the completion of the course and the exam, you will be awarded with a course completion certificate.

Duration

32 hours (16 hours of Instructor-led training plus 16 hours of student practice)

This course is ideal for:

Data engineers, analysts, and IT professionals seeking hands-on expertise in Big Data processing with Hadoop.
Students and job seekers pursuing roles in Data Engineering or Hadoop administration.
Developers and database professionals transitioning to distributed data systems.
Teams aiming to strengthen their data analytics and large-scale data management skills.

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.

Course Content

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!

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.

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.

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

Hadoop Developer

Big Data Engineer

Data Scientist

Data Analyst

Data Architect

Module 1: Introduction to Big Data & Hadoop

Definition and Characteristics of Big Data

Overview of the Hadoop Ecosystem

Use Cases of Big Data in Various Industries

Module 2: Hadoop & HDFS Architecture

Understanding Hadoop Distributed File System (HDFS) and its design principles

The role of NameNode and DataNode

High Availability and Data Replication in HDFS

Module 3: MapReduce Framework

Overview of the MapReduce process (Map and Reduce phases)

Writing and executing MapReduce jobs

Understanding Input/Output formats and related configurations

Module 4: Advanced MapReduce

Combiner functions and their advantages

Optimizing MapReduce jobs (partitioning, combiners, and reducers)

Common pitfalls and best practices in MapReduce development

Module 5: Apache PIG

Introduction to Pig and its data flow model

Writing Pig Latin scripts for data manipulation

Using Pig to execute MapReduce jobs transparently

Module 6: Apache Hive

Overview of Hive architecture and its components

Writing HiveQL queries for data retrieval and manipulation

Understanding Hive tables, partitions, and buckets

Module 7: Advanced Hive & HBase

Advanced Hive features: UDFs, custom SerDes, and transactions

Introduction to HBase: Architecture and use cases

Integrating Hive with HBase

Module 8: Distributed Data Processing with Apache Spark

Overview of Spark’s architecture and core components

Comparing Spark with Hadoop MapReduce

Introduction to Spark RDD (Resilient Distributed Dataset)

Module 9: Eco Frameworks for Integrations

Overview of frameworks like Flink, Storm, and Kafka

Data ingestion strategies and tools (Apache Nifi, Sqoop)

Real-time vs. batch processing frameworks

Module 10: SPARK

Working with Spark SQL, DataFrames, and Spark MLlib for machine learning

Advanced Spark programming concepts and optimization techniques

Demand for 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.

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FAQs

1. Who should take this course?

This course is designed for aspiring data engineers, data analysts, and IT professionals who want to deepen their understanding of Big Data technologies and Hadoop.

2. What is Hadoop?

Hadoop is a tool that helps process large amounts of data across many computers. It can handle big data efficiently and is designed to be reliable and scalable.

3. What are the key components of Hadoop?

The key components include Hadoop Distributed File System (HDFS) for storage, MapReduce for data processing, and tools like Apache Pig and Apache Hive for data manipulation and querying.

4. Is Hadoop free to use?

Yes, Hadoop is free to use. It is an open-source framework, which means you can download, use, and modify it without any cost.

5. What is the duration of the course?

The course is designed to be completed in approximately 48 hours, which includes 24 hours of instructor-led training and 24 hours of student practice.

6. Do I need prior experience with Hadoop or Big Data?

No prior experience with Hadoop or Big Data is required, understanding SQL (Structured Query Language) is essential for working with databases and querying data.

7. What topics will be covered in this course?

The course will cover topics such as Hadoop architecture, data modeling, MapReduce framework, Apache Hive, Apache Pig, advanced DAX calculations, and integrating artificial intelligence in Power BI.

8. Will I receive a certificate upon completion of the course?

Yes, participants will receive a certificate of completion, which can enhance your resume and demonstrate your proficiency in Big Data and Hadoop.

9. Is the course hands-on?

Yes, the course includes practical exercises and projects to help you apply what you learn to real-world scenarios.

10. What resources will be provided during the course?

Participants will have access to instructor support throughout the course, along with resources to facilitate learning, including assignments, and exercises.

11. How can I register for the course?

To enroll in this course, please email us at enroll@ohiocomputeracademy.com.

12. Are group discounts available?

Yes, discounts may be available for group registrations. Please contact us at enroll@ohiocomputeracademy.com for more details on group pricing options.