APACHE SPARK AND SCALA CERTIFICATION TRAINING

PROFESSIONAL TRAINING MADE EASY

Website: www.quorse.com

APACHE SPARK AND SCALA CERTIFICATION TRAINING WHAT YOU WILL LEARN With Simplilearn’s Apache Spark and Scala certification training you would advance your expertise in Big Data Hadoop Ecosystem. With this Apache Spark certification you will master the essential skills such as Spark Streaming, Spark SQL, Machine Learning Programming, GraphX Programming, Shell Scripting Spark. And with real life industry project coupled with 30 demos you would be ready to take up Hadoop developer job requiring Apache Spark expertise. AUDIENCE  Professionals aspiring for a career in field of real time Big data analytics  Analytics professionals  Research professionals  IT developers and testers  Data scientists  BI and reporting professionals  Students who wish to gain a thorough understanding of Apache Spark COURSE OBJECTIVES With Certification in Apache Spark and Scala training, you will be able to Get clear understanding of the limitations of MapReduce and role of Spark in overcoming these limitations  Understand fundamentals of Scala Programming Language and it’s features  Explain & master the process of installing Spark as a standalone cluster  Expertise in using RDD for creating applications in Spark  Mastering SQL queries using SparkSQL  Gain thorough understanding of Spark Streaming features  Master & describe the features of Spark ML Programming and GraphX Programming

COURSE OUTLINES Lesson 1: Course Preview  Course overview  Objectives Lesson 2: Introduction to Spark  Limitations of MapReduce in Hadoop Objectives  Batch vs. Real-time analytics  Application of stream processing  How to install Spark  Spark vs. Hadoop Eco-system Lesson 3: Introduction to Programming in Scala  Features of Scala  Basic data types and literals used  List the operators and methods used in Scala  Concepts of Scala Lesson 4: Using RDD for Creating Applications in Spark  Features of RDDs  How to create RDDs  RDD operations and methods  How to run a Spark project with SBT  Explain RDD functions and describe how to write different codes in Scala Lesson 5: Running SQL queries Using SparkSQL  Explain the importance and features of SparkSQL  Describe methods to convert RDDs to DataFrames  Explain concepts of SparkSQL  Describe the concept of hive integration Lesson 6: Spark Streaming  Explain a concepts of Spark Streaming  Describe basic and advanced sources  Explain how stateful operations work  Explain window and join operations Lesson 7: Spark ML Programming  Explain the use cases and techniques of Machine Learning (ML)  Describe the key concepts of Spark ML  Explain the concept of an ML Dataset, and ML algorithm, model selection via cross validation Lesson 8: Spark GraphX Programming  Explain the key concepts of Spark GraphX programming  Limitations of the Graph Parallel system  Describe the operations with a graph  Graph system optimizations

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