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