Big Data Fundamentals with Hadoop and Spark
Master big data processing with Hadoop and Spark in IBM's comprehensive course. Learn parallel processing and analytics for large datasets.
Master big data processing with Hadoop and Spark in IBM's comprehensive course. Learn parallel processing and analytics for large datasets.
Discover the power of big data technologies with IBM's foundational course. Learn to process and analyze massive datasets using industry-standard tools like Hadoop and Apache Spark. Explore distributed processing, parallel programming, and data parallelism concepts. Master practical skills in PySpark, Spark SQL, and streaming analytics. Perfect for IT professionals looking to understand big data processing tools and their applications. Gain hands-on experience with real-world scenarios and learn to leverage these technologies for efficient data analysis.
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What you'll learn
Master fundamental concepts of big data and its impact on organizations
Understand Hadoop architecture and ecosystem components including HDFS and MapReduce
Develop skills in Apache Spark programming and parallel processing
Gain practical experience with PySpark and Spark SQL applications
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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There are 7 modules in this course
This course provides a comprehensive introduction to big data technologies and practices. Students learn about the fundamentals of big data processing, including parallel processing, scaling, and data parallelism. The curriculum covers major platforms like Hadoop and Spark, exploring their architectures, components, and applications. Through hands-on labs and practical exercises, participants gain experience with distributed file systems, MapReduce, PySpark, and Spark SQL. The course also covers advanced topics like performance monitoring and tuning, making it valuable for aspiring data engineers and IT professionals.
What is Big Data
Module 1
Introduction to the Hadoop Ecosystem
Module 2
Introduction to Apache Spark
Module 3
DataFrames and SparkSQL
Module 4
Development and Runtime Environment Options
Module 5
Monitoring and Tuning
Module 6
Final Quiz
Module 7
Fee Structure
Instructors
Data Scientist Aije Egwaikhide: Empowering Women in STEM and Innovating AI Solutions at IBM
Aije Egwaikhide is a fantastic example of how dedication and passion can lead to a successful career in tech! With her background in Economics and Statistics, paired with advanced qualifications in Business and Management Analytics, she’s truly paving the way in the field of data science. Her work at IBM, particularly in creating innovative machine learning solutions for the Oil and Gas sector, is an inspiring achievement.
2 Courses
A Distinguished AI Engineer Advancing Open Source Machine Learning
Karthik Muthuraman serves as a Data Scientist and Developer Advocate at IBM's Center for Open Source Data & AI Technologies (CODAIT), where he focuses on democratizing AI through open-source technologies. After earning his Master's degree in Electrical and Computer Engineering from the University of Michigan, Ann Arbor, with a focus on machine learning and computer vision, he has established himself as an expert in deep learning and AI systems. His work at CODAIT includes developing open-source deep learning models, contributing to frameworks like TensorFlow, and creating innovative applications such as automatic image cropping and age estimation systems
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Frequently asked questions
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