Data Engineering, Hadoop, Apache Spark, Apache Kafka, MapReduce, ETL, Machine Learning, Data Analysts
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
Why take this course?
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.