What is Bigdata?

You will start learning about big data in this section by reading the most recent definition of the term. Through Big Data Use Cases, you’ll investigate how Big Data affects both routine daily routines and commercial transactions. Additionally, you will discover how data parallelism, scaling, and parallel processing are used in big data. As you proceed, you will examine frequently utilised Big Data tools and elucidate the function of open-source in Big Data. Lastly, you’ll go past the hoopla and consider several Big Data perspectives.

 What will you learn in this Bigdata Hadoop training with us?

Resilient Distributed Datasets (RDDs), their applications in Apache Spark, and RDD transformations and actions are covered in this module. You will contrast using Spark’s most recent data abstraction, DataFrames, with using datasets. It will teach you how to recognise and use fundamental DataFrame operations. You will investigate Apache Spark SQL optimisation and discover the advantages of utilising Catalyst and Tungsten for Spark SQL and memory optimisation.

Lastly, a guided hands-on lab will strengthen your skills in applying data aggregation techniques and creating a table view.

Advantages of Hadoop

• Cost Effective
• Scalable
• Flexible
• Fast
• Resilient to Failure