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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 visualize data.
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 SCRUM.
Nishant has also undertaken many speaking engagements on big data technologies and is also the author of Learning Apache Kafka & HBase Essestials, Packt Publishing.
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