UdemyIT CertificationsPaid courseAll LevelsCertificate

MongoDB (Udemy.com)

Data Engineering, Hadoop, Apache Spark, Apache Kafka, MapReduce, ETL, Machine Learning, Data Analysts

4.4

49 reviews

Created by: Akash Sunil Pawar

Last updated September 2024

What you will learn

Understand the fundamental concepts of Big Data and its significance in modern data analytics.
Learn about the core components of Big Data architecture, including Hadoop, Spark, and data storage systems.
Gain insights into the differences between batch processing and real-time processing in Big Data.
Explore key Big Data tools and technologies such as Hive, Pig, Apache Kafka, and Apache Flink.
Understand how machine learning integrates with Big Data for predictive analytics and decision-making.
Analyze Big Data use cases and applications in industries like IoT, predictive maintenance, and more.
Master best practices for Big Data project management, performance optimization, and cost management.
Learn Data Engineering technologies
Learn Kafka Fundamentals

How it compares

Against the other 848 rated Udemy courses in IT Certifications on CourseDuck:

Rating4.4 out of 5higher than 28% of them
Reviews49#793 of 849 by number of reviews
Length1.2 hoursshorter than 94% of them
Last updatedSeptember 2024earlier than 75% of them

Course Description

Welcome to the "Big Data Foundation for Data Engineers, Scientists, and Analysts" course on Udemy! This comprehensive, theory-focused course is designed to provide you with a deep understanding of Big Data concepts, frameworks, and applications without the need for hands-on coding or practical exercises. Whether you're a data engineer, scientist, analyst, or a professional looking to advance your career in the Big Data domain, this course will equip you with the knowledge to excel.

Why Big Data?

Big Data has revolutionized the way organizations handle and analyze vast amounts of information. With the exponential growth of data, the ability to process and extract meaningful insights has become critical in various industries, from healthcare to finance, retail, and beyond. This course delves into the foundational principles of Big Data, helping you understand its significance and how it differentiates itself from traditional data processing systems.

Key Topics Covered:

Introduction to Big Data: Understand the definition, significance, and the 5 Vs (Volume, Variety, Velocity, Veracity, Value) that define Big Data's complexity.
Big Data vs Traditional Systems: Learn how Big Data differs from traditional data processing systems, focusing on data volume, speed, and diversity.
Big Data Architecture: Explore the architecture components, including batch processing, stream processing, and the Hadoop ecosystem (HDFS, MapReduce, YARN).
Apache Spark: Discover the advantages of in-memory processing in Apache Spark and how it compares to Hadoop.
Data Storage and Management: Analyze various data storage systems like NoSQL databases and distributed file systems, including HDFS and data replication.
MapReduce and Processing Techniques: Delve into the MapReduce paradigm and understand key differences between batch and real-time processing.
Big Data Tools: Learn about Hive, Pig, Impala, and Apache Kafka for efficient data processing and streaming.
Machine Learning in Big Data: Explore machine learning concepts, predictive analytics, and how tools like Apache Mahout enable scalable learning.
Big Data Use Cases: Examine real-world applications in predictive maintenance, IoT, and future trends in cloud computing for Big Data.
Best Practices and Optimization: Learn strategies to optimize Big Data workflows and balance performance with cost.

All Levels

Time: 1.2h

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Instructor Details

Akash Sunil Pawar

4.4 Rating
49 Reviews

Akash Pawar is a Certified Google cloud Associate engineer. He has completed Bachelor of Technology in Electronics Engineering from National Institute of Technology (NIT, Rourkela) and has years of experience as a professional data engineer and trainer for ETL operations using PySpark. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data engineering to help other people learn the power of programming, the ability to analyze data, and the skills needed to present the data in clear and beautiful visualizations. Previously he used to work as the Data Engineer in Fractal Analytics.