Big Data Analysis with Scala and Spark
Manipulating big data distributed over a cluster using functional concepts is rampant in industry, and is arguably one of the first widespread industrial uses of functional ideas. This is evidenced by the popularity of MapReduce and Hadoop, and most recently Apache Spark, a fast, in-memory distributed collections framework written in Scala. In this course, we’ll see how the data parallel paradigm can be extended to the distributed case, using Spark throughout. We’ll cover Spark’s programming model in detail, being careful to understand how and when it differs from familiar programming models, like shared-memory parallel collections or sequential Scala collections. Through hands-on examples in Spark and Scala, we’ll learn when important issues related to distribution like latency and network communication should be considered and how they can be addressed effectively for improved performance.
- 60 Hours Sessions
- Practical Classes on Each Topic
- Weekdays Mon-Fri Classes
- Weekends Saturday and Sunday Classes
- Corporate Training for IT Companies
- spark training online
- Feel & Decide After 1 FREE DEMO CLASS
- Lectures 30
- Duration 60hrs
- Skill Level All levels
- Language English
- Students 80
Trainer name: Mandal
- More than 9+ years of experience in Development ,Analysis , Buisness intelligence, Java & Salesforce , Hadoop Bigdata .Senior Big Data consultant with excellent knowledge of Hadoop, Big Data, HDFS, MapReduce, Pig, Hive, Sqoop, ZooKeeper, Hbase, Camel Java and ETL tools. Good knowledge on Hadoop Development as well as on Hadoop Admin. Multiyear expertise in delivering corporate training
- Working with various data owners and data stewards from Finance, Healthcare Quality, Customer Quality, Markets and IT to understand and drive delivery of data management objectives.
- Available for one to one training in Hadoop ecosystem, spark.
- Providing training and architectural solution for Big data Hadoop, spark, hive implementation