This video is a comprehensive tutorial to help you learn all the
fundamentals of Apache Spark, one of the trending big data processing
frameworks on the market today. We will introduce you to the various
components of the Spark framework to efficiently process, analyze, and
You will also get the brief introduction of Apache Hadoop and Scala
programming language before start writing with Spark programming. You
will learn about the Apache Spark programming fundamentals such as
Resilient Distributed Datasets (RDD) and See which operations can be
used to perform a transformation or action operation on the RDD. We'll
show you how to load and save data from various data sources as
different type of files, No-SQL and RDBMS databases etc.. We’ll also
explain Spark advanced programming concepts such as managing Key-Value
pairs, accumulators etc. Finally, you'll discover how to create an
effective Spark application and execute it on Hadoop cluster to the data
and gain insights to make informed business decisions.
By the end of this video, you will be well-versed with all the fundamentals of Apache Spark and implementing them in Spark.
About The Author
Nishant Garg has over 16 years of software architecture and
development experience in various technologies, such as Java Enterprise
Edition, SOA, Spring, Hadoop, Hive, Flume, Sqoop, Oozie, Spark, YARN,
Impala, Kafka, Storm, Solr/Lucene, NoSQL databases (such as HBase,
Cassandra, and MongoDB), and MPP databases (such as GreenPlum).
He received his MS in software systems from the Birla Institute of
Technology and Science, Pilani, India, and is currently working as a
senior technical architect for the Big Data R&D Labs with Impetus
Infotech Pvt. Ltd. Previously, Nishant has enjoyed working with some of
the most recognizable names in IT services and financial industries,
employing full software life cycle methodologies such as Agile and
Nishant has also undertaken many speaking engagements on big data
technologies and is also the author of Learning Apache Kafka & HBase
Essestials, Packt Publishing.