introduction to Big Data with Spark and Hadoop

Begin your acquisition of Big Data knowledge with the most up-to-date definition of Big Data. You’ll explore the impact of Big Data on everyday personal tasks and business transactions with Big Data Use Cases. Learn how Big Data uses Parallel Processing, Scaling, and Data Parallelism. Learn about commonly used Big Data tools. Then, go beyond the hype and explore additional Big Data viewpoints.

Learning Objectives
Define what Big Data is and identify key Big Data characteristics.
Relate examples of Big Data related technologies, describe the impact of Big Data on businesses and people, and define the relationship between the Internet of Things (IoT) and Big Data.
Compare linear and parallel processing and explain why Big Data requires parallel processing.
Describe scalability, including horizontal scaling, embarrassing parallel calculations and fault tolerance as they relate to Big Data.
Explain the role of open-source for Big Data and list related platforms and open-source frameworks.
Relate real-world Big Data use cases.
List the key Big Data ecosystem tooling categories and their associated major tools and vendors.
#Spark#Hadoop#