The Big Data Analytics course offers a deep dive into the technologies, tools, and techniques used to process and analyze large-scale data. Learners will explore the Hadoop and Spark ecosystems, gaining hands-on experience with essential components such as Hadoop Distributed File System (HDFS), MapReduce, Pig, and Hive. The course also covers both relational (SQL) and nonrelational (NoSQL) databases, helping learners understand the appropriate contexts for each type of data storage. A significant focus is placed on Apache Spark, known for its high-speed, in-memory data processing capabilities, which is vital for handling big data applications. Learners will also work through real-world exercises, including implementing and deploying a machine learning application that processes streaming data on the cloud. Designed for professionals with a background in predictive analytics, basic SQL, and Python programming, this course equips learners with the practical skills to manage data characterized by high volume, velocity, and variety. By the end of the course, participants will be able to derive actionable insights from big data and apply them in business contexts, contributing to improved decision-making and competitive advantage in data-driven environments.
HQL Commands and Uses
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Skills You'll Learn
Databases, Apache Hadoop, Data Manipulation, Real Time Data, Data Mining, Data Processing, Machine Learning Methods, Query Languages, PySpark, Distributed Computing, NoSQL, Analytics, Cloud Applications, Apache Spark, Applied Machine Learning, Big Data, Scripting Languages
From the lesson
Introduction to Data Mining with Hive
Taught By
Dr. Mohit Bhatnagar
Associate Professor