17 OCT 2024 - Welcome Back to TorrentFunk!
Get your pirate hat back out. Streaming is dying and torrents are the new trend.
Account Registration works again and so do Torrent Uploads.
We invite you all to start uploading torrents again!

TORRENT DETAILS

[FreeCoursesOnline.Me] [Packt] Troubleshooting Apache Spark [FCO]

TORRENT SUMMARY

	Status:	All the torrents in this section have been verified by our verification system
Category:	Other
Infohash	fb8212715653d4732ef1cf05a176253de1fc7b58
Size:	1 GB in 44 files
Release Date(s):	Torrent Release: 16-10-2019 by user
Swarm:	0 Seeds & 1 Peers
Vote:	No votes yet.

DESCRIPTION

For More Udemy Free Courses >>> https://freetutorials.us/
For more Lynda and other Courses >>> https://www.freecoursesonline.me/
Forum for discussion >>> https://1hack.us/

By : Tomasz Lelek
Released : November 28, 2018
Caption (CC) : Included
Torrent Contains : 49 Files, 1 Folders
Course Source : https://www.packtpub.com/application-development/troubleshooting-apache-spark-video

Quick, simple solutions to common development issues and Debugging techniques with Apache Spark.

Video Details

ISBN 9781789805253
Course Length 1 hour 43 minutes

Table of Contents

• Common Problems and Troubleshooting the Spark Distributed Engine
• Distributed DataFrames Optimization Pitfalls
• Distributed Joins in Cluster
• Solving Problems with Non-Efficient Transformations
• Troubleshooting Real-Time Processing Jobs in Spark Streaming

Learn

• Solve long-running computation problems by leveraging lazy evaluation in Spark
• Avoid memory leaks by understanding the internal memory management of Apache Spark
• Rework problems due to not-scaling out pipelines by using partitions
• Debug and create user-defined functions that enrich the Spark API
• Choose a proper join strategy depending on the characteristics of your input data
• Troubleshoot APIs for joins - DataFrames or DataSets
• Write code that minimizes object creation using the proper API
• Troubleshoot real-time pipelines written in Spark Streaming

About

Apache Spark has been around quite some time, but do you really know how to solve the development issues and problems you face with it? This course will give you new possibilities and you'll cover many aspects of Apache Spark; some you may know and some you probably never knew existed. If you take a lot of time learning and performing tasks on Spark, you are unable to leverage Apache Spark's full capabilities and features, and face a roadblock in your development journey. You'll face issues and will be unable to optimize your development process due to common problems and bugs; you'll be looking for techniques which can save you from falling into any pitfalls and common errors during development. With this course you'll learn to implement some practical and proven techniques to improve particular aspects of Apache Spark with proper research

You need to understand the common problems and issues Spark developers face, collate them, and build simple solutions for these problems. One way to understand common issues is to look out for Stack Overflow queries. This course is a high-quality troubleshooting course, highlighting issues faced by developers in different stages of their application development and providing them with simple and practical solutions to these issues. It supplies solutions to some problems and challenges faced by developers; however, this course also focuses on discovering new possibilities with Apache Spark. By the end of this course, you will have solved your Spark problems without any hassle.

All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Troubleshooting-Apache-Spark

Style and Approach

This course takes a question-and-answer approach, identifying key problems faced by Apache Spark developers and providing straightforward solutions.

Features:

• Optimize resources and costs by utilizing Spark's speed
• Troubleshoot the Spark execution DAG by exploring Spark logical and physical query plans to perform the same logic on fewer executors and machines
• Solve the problem of slow-running jobs by speeding up feedback loops by creating efficient transformations and joins using Spark APIs

Author

Tomasz Lelek

Tomasz Lelek is a software engineer, programming mostly in Java and Scala. He has been working with the Spark and ML APIs for the past 6 years, with production experience in processing petabytes of data. He is passionate about nearly everything associated with software development and believes that we should always try to consider different solutions and approaches before attempting to solve a problem. Recently he was a speaker at conferences in Poland—Confitura and JDD (Java Developers Day)—and at Krakow Scala User Group. He has also conducted a live coding session at the Geecon Conference. Contacted on 10/09/2019 for Typescript book - author said he is not an expert on Typescript, his area of expertise is in back-end technologies like Java, Spring and Big Data - Spark.

VISITOR COMMENTS (0

)

FILE LIST

Filename		Size
	0. Websites you may like/How you can help Team-FTU.txt	229 B
	1. The Course Overview.mp4	23.1 MB
	1. The Course Overview.vtt	3.2 KB
	10. Avoid Wrong Join Strategies Using a Join Type Based on Data Volume.mp4	85.5 MB
	10. Avoid Wrong Join Strategies Using a Join Type Based on Data Volume.vtt	7.7 KB
	11. Slow Joins Choosing an Execution Plan for Join.mp4	71.7 MB
	11. Slow Joins Choosing an Execution Plan for Join.vtt	5.7 KB
	12. Distributed Joins Problem DataFrame API.mp4	63.6 MB
	12. Distributed Joins Problem DataFrame API.vtt	5.5 KB
	13. TypeSafe Joins Problem The Newest DataSet API.mp4	41.4 MB
	13. TypeSafe Joins Problem The Newest DataSet API.vtt	3.8 KB
	14. Minimizing Object Creation Reusing Existing Objects.mp4	55.8 MB
	14. Minimizing Object Creation Reusing Existing Objects.vtt	5 KB
	15. Iterating Transformations – The mapPartitions() Method.mp4	40.7 MB
	15. Iterating Transformations – The mapPartitions() Method.vtt	4.2 KB
	16. Slow Spark Application Start Reducing Setup Overhead.mp4	47.1 MB
	16. Slow Spark Application Start Reducing Setup Overhead.vtt	4.6 KB
	17. Performing Unnecessary Recomputation Reusing RDDs.mp4	39.8 MB
	17. Performing Unnecessary Recomputation Reusing RDDs.vtt	4.4 KB
	18. Repeating the Same Code in Stream Pipeline Using Sources and Sinks.mp4	77.4 MB
	18. Repeating the Same Code in Stream Pipeline Using Sources and Sinks.vtt	6.8 KB
	19. Long Latency of Jobs Understanding Batch Internals.mp4	68.2 MB
	19. Long Latency of Jobs Understanding Batch Internals.vtt	5.4 KB
	2. Eager Computations Lazy Evaluation.mp4	46.1 MB
	2. Eager Computations Lazy Evaluation.vtt	5.4 KB
	20. Fault Tolerance Using Data Checkpointing.mp4	38.6 MB
	20. Fault Tolerance Using Data Checkpointing.vtt	3.7 KB
	21. Maintaining Batch and Streaming Using Structured Streaming Pros.mp4	16.1 MB
	21. Maintaining Batch and Streaming Using Structured Streaming Pros.vtt	3.9 KB
	22. Test Your Knowledge.html	167 B
	3. Caching Values In-Memory Persistence.mp4	80.1 MB
	3. Caching Values In-Memory Persistence.vtt	7 KB
	4. Unexpected API Behavior Picking the Proper RDD API.mp4	50.3 MB
	4. Unexpected API Behavior Picking the Proper RDD API.vtt	4.3 KB
	5. Wide Dependencies Using Narrow Dependencies.mp4	14.1 MB
	5. Wide Dependencies Using Narrow Dependencies.vtt	5 KB
	6. Making Computations Parallel Using Partitions.mp4	65.8 MB
	6. Making Computations Parallel Using Partitions.vtt	6.1 KB
	7. Defining Robust Custom Functions Understanding User-Defined Functions.mp4	52 MB
	7. Defining Robust Custom Functions Understanding User-Defined Functions.vtt	4.6 KB
	8. Logical Plans Hiding the Truth Examining the Physical Plans.mp4	75.9 MB
	8. Logical Plans Hiding the Truth Examining the Physical Plans.vtt	6.3 KB
	9. Slow Interpreted Lambdas Code Generation Spark Optimization.mp4	11.3 MB
	9. Slow Interpreted Lambdas Code Generation Spark Optimization.vtt	4.4 KB

RELATED DOWNLOADS

Torrent Name

Added

Size

Seeds

Peers

Uploader

Health