[center]Published 12/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 640.96 MB | Duration: 1h 55m

A Basic to Advanced Overview for processing Big Data with Spark[/center]

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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