A Basic to Advanced Overview for processing Big Data with Spark What you'll learn OOPS and Functional Programming in Scala Apache Spark Framework Advanced Spark Programming Integrating Spark with Kafka Spark MLib - Machine Learning Spark Streaming, SparkSQL, Spark GraphX etc. Requirements Intermediate programming experience in Python or Scala Beginner experience with the DataFrame API Basic understanding of Machine Learning concepts Description Apache Spark is a cluster computing platform designed to be fast and general-purpose. On the speed side, Spark extends the popular MapReduce model to efficiently support more types of computations, including interactive queries and stream processing. Speed is important in processing large datasets, as it means the difference between exploring data interactively and waiting minutes or hours. One of the main features Spark offers for speed is the ability to run computations in memory, but the system is also more efficient than MapReduce for complex applications running on disk. On the generality side, Spark is designed to cover a wide range of workloads that previously required separate distributed systems, including batch applications, iterative algorithms, interactive queries, and streaming. By supporting these workloads in the same engine, Spark makes it easy and inexpensive to combine different processing types, which is often necessary in production data analysis pipelines. In addition, it reduces the management burden of maintaining separate tools. Spark is designed to be highly accessible, offering simple APIs in Python, Java, Scala, and SQL, and rich built-in libraries. It also integrates closely with other Big Data tools. In particular, Spark can run in Hadoop clusters and access any Hadoop data source, including Cassandra. Overview Section 1: Module 1 Lecture 1 Functions and Procedures in Scala Lecture 2 Call By Name Parameter Lecture 3 Functions with Named Arguments Lecture 4 Functions with Variable Arguments Lecture 5 Recursion Functions Lecture 6 Default Parameters for a Function Lecture 7 Nested Functions Lecture 8 Anonymous Functions Lecture 9 Strings in Scala Lecture 10 Arrays in Scala Lecture 11 Scala Collections Lecture 12 Lists in Scala Lecture 13 Sets in Scala Lecture 14 Maps in Scala Lecture 15 Tuples in Scala Lecture 16 Options in Scala Lecture 17 Exception Handling in Scala Lecture 18 Pattern Matching Lecture 19 Scala Traits Lecture 20 Scala Files Input Output Lecture 21 Extractors in Scala Professionals aspiring to learn the basics of Big Data Analytics,Spark Developer,Analytics Professionals,ETL Developers
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