Advanced Apache Spark for Data Scientists and Developers

Advanced Apache Spark for Data Scientists and Developers , available at $19.99, has an average rating of 3.65, with 71 lectures, 7 quizzes, based on 59 reviews, and has 518 subscribers.

You will learn about Understand the functionality of Sparks four built-in libraries Create real-world applications using Spark’s libraries Understand how to develop, debug and optimize the performance of Spark applications This course is ideal for individuals who are Data Scientists or Developers or Data Engineers It is particularly useful for Data Scientists or Developers or Data Engineers.

Enroll now: Advanced Apache Spark for Data Scientists and Developers

Summary

Title: Advanced Apache Spark for Data Scientists and Developers

Price: $19.99

Average Rating: 3.65

Number of Lectures: 71

Number of Quizzes: 7

Number of Published Lectures: 71

Number of Published Quizzes: 7

Number of Curriculum Items: 78

Number of Published Curriculum Objects: 78

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the functionality of Sparks four built-in libraries

Create real-world applications using Spark’s libraries

Understand how to develop, debug and optimize the performance of Spark applications

Who Should Attend

Data Scientists

Developers

Data Engineers

Target Audiences

Data Scientists

Developers

Data Engineers

Apache Spark is an open source data processing engine. Spark is designed to provide fast processing of large datasets, and high performance for a wide range of analytics applications. Unlike MapReduce, Spark enables in-memory cluster computing which greatly improves the speed of iterative algorithms and interactive data mining tasks.

Adastra Academy’s Advanced Apache Spark includes illuminating video lectures, thorough application examples, a guide to install the NetBeans Integrated Development Environment, and quizzes. Through this course, you will learn about Spark’s four built-in libraries – SparkStreaming, DataFrames (SparkSQL), MLlib and GraphX – and how to develop, build, tune, and debug Spark applications. The course exercises will enable you to become proficient at creating fully functional real-world applications using the Apache Spark libraries. Unlike other courses, we give you the guided and ground-up approach to learning Spark that you need in order to become an expert.

Course Curriculum

Chapter 1: Introduction to Advanced Apache Spark

Lecture 1: Introduction to Apache Spark

Lecture 2: Spark Installation

Lecture 3: IDE Installation

Chapter 2: Tuning and Debugging

Lecture 1: Introduction and Topics

Lecture 2: Spark Configuration with SparkConf

Lecture 3: Web User-Interface and Log Files

Lecture 4: Data Serialization

Lecture 5: Memory Tuning

Lecture 6: Level of Parallelism

Lecture 7: Section Topics

Chapter 3: Spark Streaming

Lecture 1: Introduction and Topics

Lecture 2: Overview of Spark Streaming

Lecture 3: Linking Input Sources

Lecture 4: Streaming Context

Lecture 5: Discretized Streams (DStreams)

Lecture 6: Input DStreams

Lecture 7: Hands-on Exercise 1: Spark Streaming

Lecture 8: Stateless Transformations on DStreams

Lecture 9: Stateful Transformations

Lecture 10: Hands-on Exercise 2: Spark Streaming

Lecture 11: Output Operations

Lecture 12: Hands-on Exercise 3: Spark Streaming

Lecture 13: Checkpointing

Lecture 14: Caching and Persisting

Lecture 15: Tuning and Debugging

Lecture 16: Section Topics

Chapter 4: Spark SQL

Lecture 1: Introduction to Spark SQL

Lecture 2: Spark SQL Overview

Lecture 3: The Spark Shell hands-on

Lecture 4: Hands-on Exercise 1: part a) Import CSV

Lecture 5: Schema Inference

Lecture 6: Data Query Select

Lecture 7: DataFrame.Reader DataFrame.Writer

Lecture 8: Hands-on Exercise 1: part b) Import JSON

Lecture 9: Data Query INNER JOINs

Lecture 10: Group By, Order By, Window Functions

Lecture 11: Data Query OUTER JOINs, SEMI JOIN

Lecture 12: Custom UDF (User Defined Function)

Lecture 13: API or SQL?

Lecture 14: Hands-on Exercise 2: Spark SQL

Chapter 5: Spark MLlib

Lecture 1: Introduction and Topics

Lecture 2: Machine Learning

Lecture 3: MLlib

Lecture 4: Basic Statistics

Lecture 5: Optimization

Lecture 6: Classification

Lecture 7: Hands-on Exercise 1: Spark MLlib: Classification

Lecture 8: Validation

Lecture 9: Regression

Lecture 10: Clustering

Lecture 11: Hands-on Exercise 2: Spark MLlib: Clustering

Lecture 12: Feature Extraction and Transformation

Lecture 13: Dimensionality Reduction

Lecture 14: Collaborative Filtering

Lecture 15: Evaluation Metrics

Chapter 6: Spark GraphX

Lecture 1: Introduction to Spark GraphX

Lecture 2: Graph creation examples

Lecture 3: Graph Operators Overview, Information about a Graph

Lecture 4: Information about a graph example

Lecture 5: Transform Graph Items

Lecture 6: Transform graph items examples

Lecture 7: Modify Graph Structure

Lecture 8: Modify graph structure example

Lecture 9: Graph Neighborhood Aggregations

Lecture 10: Neighborhood Aggregations Examples

Lecture 11: Graph Algorithms

Lecture 12: Triangle Count Example

Lecture 13: Pregel- Graph Parallel Computation

Lecture 14: Pregel Example

Lecture 15: Optimized Graph Representation

Lecture 16: Hands-on Exercise: Spark GraphX

Instructors

Adastra Academy
Emerging Data Management and Analytics Technology Educators

Rating Distribution

1 stars: 6 votes

2 stars: 7 votes

3 stars: 14 votes

4 stars: 18 votes

5 stars: 14 votes

Frequently Asked Questions

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