Talha Malik

Talha Malik

1572254762

Apache Spark Tutorial - Apache Spark Full Course - Learn Apache Spark

This video will help you understand and learn Apache Spark in detail. This Spark tutorial is ideal for both beginners as well as professionals who want to master Apache Spark concepts. Below are the topics covered in this Spark tutorial for beginners:

2:44 Introduction to Apache Spark

3:49 What is Spark?

5:34 Spark Eco-System

7:44 Why RDD?

16:44 RDD Operations

18:59 Yahoo Use-Case

21:09 Apache Spark Architecture

24:24 RDD

26:59 Spark Architecture

31:09 Demo

39:54 Spark RDD

41:09 Spark Applications

41:59 Need For RDDs

43:34 What are RDDs?

44:24 Sources of RDDs

45:04 Features of RDDs

46:39 Creation of RDDs

50:19 Operations Performed On RDDs

50:49 Narrow Transformations

51:04 Wide Transformations

51:29 Actions

51:44 RDDs Using Spark Pokemon Use-Case

1:05:19 Spark DataFrame

1:06:54 What is a DataFrame?

1:08:24 Why Do We Need Dataframes?

1:09:54 Features of DataFrames

1:11:09 Sources Of DataFrames

1:11:34 Creation Of DataFrame

1:24:44 Spark SQL

1:25:14 Why Spark SQL?

1:27:09 Spark SQL Advantages Over Hive

1:31:54 Spark SQL Success Story

1:33:24 Spark SQL Features

1:37:15 Spark SQL Architecture

1:39:40 Spark SQL Libraries

1:42:15 Querying Using Spark SQL

1:45:50 Adding Schema To RDDs

1:55:05 Hive Tables

1:57:50 Use Case: Stock Market Analysis with Spark SQL

2:16:50 Spark Streaming

2:18:10 What is Streaming?

2:25:46 Spark Streaming Overview

2:27:56 Spark Streaming workflow

2:31:21 Streaming Fundamentals

2:33:36 DStream

2:38:56 Input DStreams

2:40:11 Transformations on DStreams

2:43:06 DStreams Window

2:47:11 Caching/Persistence

2:48:11 Accumulators

2:49:06 Broadcast Variables

2:49:56 Checkpoints

2:51:11 Use-Case Twitter Sentiment Analysis

3:00:26 Spark MLlib

3:00:31 MLlib Techniques

3:01:46 Demo

3:11:51 Use Case: Earthquake Detection Using Spark

3:24:01 Visualizing Result

3:25:11 Spark GraphX

3:26:01 Basics of Graph

3:27:56 Types of Graph

3:38:56 GraphX

3:40:42 Property Graph

3:48:37 Creating & Transforming Property Graph

3:56:17 Graph Builder

4:02:22 Vertex RDD

4:07:07 Edge RDD

4:11:37 Graph Operators

4:24:37 GraphX Demo

4:34:24 Graph Algorithms

4:34:40 PageRank

4:38:29 Connected Components

4:40:39 Triangle Counting

4:44:09 Spark GraphX Demo

4;57:54 MapReduce vs Spark

5:13:03 Kafka with Spark Streaming

5:23:38 Messaging System

5:21:15 Kafka Components

2:23:45 Kafka Cluster

5:24:15 Demo

5:48:56 Kafka Spark Streaming Demo

6:17:16 PySpark Tutorial

6:21:26 PySpark Installation

6:47:06 Spark Interview Questions

#Apache #data-sciecne

What is GEEK

Buddha Community

Apache Spark Tutorial - Apache Spark Full Course - Learn Apache Spark
Gunjan  Khaitan

Gunjan Khaitan

1619227525

Apache Spark Full Course | Spark Tutorial For Beginners | Complete Spark Tutorial

This Apache Spark full course will help you learn the basics of Big Data, what Apache Spark is, and the architecture of Apache Spark. Then, you will understand how to install Apache Spark on Windows and Ubuntu. You will look at the important components of Spark, such as Spark Streaming, Spark MLlib, and Spark SQL. Finally, you will get an idea about implement Spark with Python in PySpark tutorial and look at some of the important Apache Spark interview questions.

Below topics are explained in this Apache Spark Full Course:

  1. Animated Video
  2. History of Spark
  3. What is Spark
  4. Hadoop vs spark
  5. Components of Apache Spark
  6. Spark Architecture
  7. Applications of Spark
  8. Spark Use Case
  9. Running a Spark Application
  10. Apache Spark installation on Windows
  11. Apache Spark installation on Ubuntu
  12. What is Spark Streaming
  13. Spark Streaming data sources
  14. Features of Spark Streaming
  15. Working of Spark Streaming
  16. Discretized Streams
  17. caching/persistence
  18. checkpointing in spark streaming
  19. Demo on Spark Streaming
  20. What is Spark MLlib
  21. What is Machine Learning
  22. Machine Learning Algorithms
  23. Spark MLlib Tools
  24. Spark MLlib Data Types
  25. Machine Learning Pipelines
  26. Spark MLlib Demo
  27. What is Spark SQL
  28. Spark SQL Features
  29. Spark SQL Architecture
  30. Spark SQL Data Frame
  31. Spark SQL Data Source
  32. Spark SQL Demo
  33. What is PySpark
  34. PySpark Features
  35. PySpark with Python and Scala
  36. PySpark Contents
  37. PySpark Sub packages
  38. Companies using PySpark
  39. PySpark Demo
  40. Spark Interview Questions

#apache-spark #big-data #developer #apache #spark

Gunjan  Khaitan

Gunjan Khaitan

1582649280

Apache Spark Tutorial For Beginners - Apache Spark Full Course

This full course video on Apache Spark will help you learn the basics of Big Data, what Apache Spark is, and the architecture of Apache Spark. Then, you will understand how to install Apache Spark on Windows and Ubuntu. You will look at the important components of Spark, such as Spark Streaming, Spark MLlib, and Spark SQL. Finally, you will get an idea about implement Spark with Python in PySpark tutorial and look at some of the important Apache Spark interview questions. Now, let’s get started and learn Apache Spark in detail.

Below topics are explained in this Apache Spark Full Course:

  1. Animated Video 01:15
  2. History of Spark 06:48
  3. What is Spark 07:28
  4. Hadoop vs spark 08:32
  5. Components of Apache Spark 14:14
  6. Spark Architecture 33:26
  7. Applications of Spark 40:05
  8. Spark Use Case 42:08
  9. Running a Spark Application 44:08
  10. Apache Spark insallation on Windows 01:01:03
  11. Apache Spark insallation on Ubuntu 01:31:54
  12. What is Spark Streaming 01:49:31
  13. Spark Streaming data sources 01:50:39
  14. Features of Spark Streaming 01:52:19
  15. Working of Spark Streaming 01:52:53
  16. Discretized Streams 01:54:03
  17. caching/persistence 02:02:17
  18. checkpointing in spark streaming 02:04:34
  19. Demo on Spark Streaming 02:18:27
  20. What is Spark MLlib 02:47:29
  21. What is Machine Learning 02:49:14
  22. Machine Learning Algorithms 02:51:38
  23. Spark MLlib Tools 02:53:01
  24. Spark MLlib Data Types 02:56:42
  25. Machine Learning Pipelines 03:09:05
  26. Spark MLlib Demo 03:18:38
  27. What is Spark SQL 04:01:40
  28. Spark SQL Features 04:03:52
  29. Spark SQL Architecture 04:07:43
  30. Spark SQL Data Frame 04:09:59
  31. Spark SQL Data Source 04:11:55
  32. Spark SQL Demo 04:23:00
  33. What is PySpark 04:52:03
  34. PySpark Features 04:58:02
  35. PySpark with Python and Scala 04:58:54
  36. PySpark Contents 05:00:35
  37. PySpark Subpackages 05:40:10
  38. Companies using PySpark 05:41:16
  39. PySpark Demo 05:41:49
  40. Spark Interview Questions 05:50:43

#bigdata #apache #spark #apache-spark

kiran sam

1619408437

Apache Spark Training Course Online - Learn Scala

R is perhaps the most popular computer dialects in information science, explicitly committed to measurable investigation with a number of augmentations, for example, RStudio addins and other R packages, for information processing and machine learning assignments. Moreover, it empowers information researchers to effortlessly imagine their informational collection.

By using SparkR in Apache SparkTM, R code can without much of a stretch be scaled. To interactively run occupations, you can without much of a stretch run the distributed calculation by running a R shell.

At the point when SparkR doesn’t require interaction with the R process, the performance is virtually indistinguishable from other language APIs like Scala, Java and Python. However, huge performance degradation happens when SparkR occupations interact with local R capacities or information types.

Databricks Runtime introduced vectorization in SparkR to improve the performance of information I/O among Spark and R. We are eager to declare that using the R APIs from Apache Arrow 0.15.1, the vectorization is presently accessible in the upcoming Apache Spark 3.0 with the significant performance improvements.

This blog entry outlines Spark and R interaction inside SparkR, the current local execution and the vectorized execution in SparkR with benchmark results.Native implementation

The calculation on SparkR DataFrame gets distributed across every one of the hubs accessible on the Spark cluster. There’s no correspondence with the R processes above in driver or executor sides on the off chance that it doesn’t have to collect information as R data.frame or to execute R native capacities. At the point when it requires R data.frame or the execution of R native capacity, they convey using attachments among JVM and R driver/executors.

It (de)serializes and transfers information row by row among JVM and R with an inefficient encoding format, which doesn’t consider the modern CPU plan, for example, CPU pipelining.

Vectorized implementation

In Apache Spark 3.0, another vectorized implementation is introduced in SparkR by leveraging Apache Arrow to trade information directly among JVM and R driver/executors with minimal (de)serialization cost

Instead of (de)serializing the information row by row using an inefficient format among JVM and R, the new implementation leverages Apache Arrow to permit pipelining and Single Instruction Multiple Data (SIMD) with a productive columnar format.

The new vectorized SparkR APIs are not empowered as a matter of course yet can be empowered by setting spark.sql.execution.arrow.sparkr.enabled to true in the upcoming Apache Spark 3.0. Note that vectorized dapplyCollect() and gapplyCollect() are not executed at this point. It is encouraged for users to utilize dapply() and gapply() instead.

Benchmark results

The benchmarks were performed with a basic informational collection of 500,000 records by executing similar code and comparing the all out passed times when the vectorization is empowered and impaired.

If there should be an occurrence of collect() and createDataFrame() with R DataFrame, it turned out to be approximately 17x and 42x faster when the vectorization was empowered. For dapply() and gapply(), it was 43x and 33x faster than when the vectorization is handicapped, respectively.

There was a performance improvement of up to 17x–43x when the streamlining was empowered by spark.sql.execution.arrow.sparkr.enabled to true. The larger the information was, the higher performance anticipated

End

The upcoming Apache Spark 3.0, supports the vectorized APIs, dapply(), gapply(), collect() and createDataFrame() with R DataFrame by leveraging Apache Arrow. Enabling vectorization in SparkR improved the performance up to 43x faster, and more lift is normal when the size of information is larger.

Concerning future work, there is an ongoing issue in Apache Arrow, ARROW-4512. The correspondence among JVM and R isn’t completely in a streaming manner currently. It needs to (de)serialize in clump since Arrow R API doesn’t support this out of the container. Also, dapplyCollect() and gapplyCollect() will be supported in Apache Spark 3.x releases. Users can work around through dapply() and collect(), and gapply() and collect() individually in the interim.

Try out these new abilities today on Databricks, through our DBR 7.0 Beta, which includes a preview of the upcoming Spark 3.0 release. Learn more about Spark 3.0 in our Spark Certification

Spark and R interaction

SparkR supports not just a rich arrangement of ML and SQL-like APIs yet additionally a bunch of APIs normally used to directly interact with R code — for instance, the consistent conversion of Spark DataFrame from/to R DataFrame, and the execution of R local capacities on Spark DataFrame in a distributed manner.

In many cases, the performance is virtually steady across other language APIs in Spark — for instance, when user code relies on Spark UDFs or potentially SQL APIs, the execution happens entirely inside the JVM with no performance punishment in I/O. See the cases beneath which take ~1 second similarly.

/Scala API

/~1 second

sql(“SELECT id FROM range(2000000000)”).filter(“id > 10”).count()

R API

~1 second

count(filter(sql(“SELECT * FROM range(2000000000)”), “id > 10”))

However, in situations where it requires to execute the R local capacity or convert it from/to R local sorts, the performance is immensely different as underneath.

/Scala API

val ds = (1L to 100000L).toDS

/~1 second

ds.mapPartitions(iter => iter.filter(_ < 50000)).count()

R API

df <-createDataFrame(lapply(seq(100000), work (e) list(value=e)))

~15 seconds - multiple times slower

count(dapply(

df, function(x) as.data.frame(x[x$value < 50000,]), schema(df)))

Albeit this basic case above filters the qualities lower than 50,000 for each partition, SparkR is 15x slower.

/Scala API

/~0.2 seconds

val df = sql(“SELECT * FROM range(1000000)”).collect()

R API

~8 seconds - multiple times slower

df <-collect(sql(“SELECT * FROM range(1000000)”))

The case above is far and away more terrible. It just collects a similar information into the driver side, yet it is 40x slower in SparkR.

This is on the grounds that the APIs that require the interaction with R local capacity or information types and its execution are not very productive. There are six APIs that have the striking performance punishment:

createDataFrame()

collect()

dapply()

dapplyCollect()

gapply()

gapplyCollect()

In short, createDataFrame() and collect() require to (de)serialize and convert the information from JVM from/to R driver side. For instance, String in Java becomes character in R. For dapply() and gapply(), the conversion among JVM and R executors is required in light of the fact that it needs to (de)serialize both R local capacity and the information. If there should arise an occurrence of dapplyCollect() and gapplyCollect(), it requires the overhead at both driver and executors among JVM and R

Native implementation

The calculation on SparkR DataFrame gets distributed across every one of the hubs accessible on the Spark cluster. There’s no correspondence with the R processes above in driver or executor sides on the off chance that it doesn’t have to collect information as R data.frame or to execute R native capacities. At the point when it requires R data.frame or the execution of R native capacity, they convey using attachments among JVM and R driver/executors.

It (de)serializes and transfers information row by row among JVM and R with an inefficient encoding format, which doesn’t consider the modern CPU plan, for example, CPU pipelining.

Vectorized implementation

In Apache Spark 3.0, another vectorized implementation is introduced in SparkR by leveraging Apache Arrow to trade information directly among JVM and R driver/executors with minimal (de)serialization cost

Instead of (de)serializing the information row by row using an inefficient format among JVM and R, the new implementation leverages Apache Arrow to permit pipelining and Single Instruction Multiple Data (SIMD) with a productive columnar format.

The new vectorized SparkR APIs are not empowered as a matter of course yet can be empowered by setting spark.sql.execution.arrow.sparkr.enabled to true in the upcoming Apache Spark 3.0. Note that vectorized dapplyCollect() and gapplyCollect() are not executed at this point. It is encouraged for users to utilize dapply() and gapply() instead.

Benchmark results

The benchmarks were performed with a basic informational collection of 500,000 records by executing similar code and comparing the all out passed times when the vectorization is empowered and impaired.

If there should be an occurrence of collect() and createDataFrame() with R DataFrame, it turned out to be approximately 17x and 42x faster when the vectorization was empowered. For dapply() and gapply(), it was 43x and 33x faster than when the vectorization is handicapped, respectively.

There was a performance improvement of up to 17x–43x when the streamlining was empowered by spark.sql.execution.arrow.sparkr.enabled to true. The larger the information was, the higher performance anticipated

End

The upcoming Apache Spark 3.0, supports the vectorized APIs, dapply(), gapply(), collect() and createDataFrame() with R DataFrame by leveraging Apache Arrow. Enabling vectorization in SparkR improved the performance up to 43x faster, and more lift is normal when the size of information is larger.

Concerning future work, there is an ongoing issue in Apache Arrow, ARROW-4512. The correspondence among JVM and R isn’t completely in a streaming manner currently. It needs to (de)serialize in clump since Arrow R API doesn’t support this out of the container. Also, dapplyCollect() and gapplyCollect() will be supported in Apache Spark 3.x releases. Users can work around through dapply() and collect(), and gapply() and collect() individually in the interim.

Try out these new abilities today on Databricks, through our DBR 7.0 Beta, which includes a preview of the upcoming Spark 3.0 release.

#apache spark #apache spark training #apache spark course

Roberta  Ward

Roberta Ward

1595344320

Wondering how to upgrade your skills in the pandemic? Here's a simple way you can do it.

Corona Virus Pandemic has brought the world to a standstill.

Countries are on a major lockdown. Schools, colleges, theatres, gym, clubs, and all other public places are shut down, the country’s economy is suffering, human health is on stake, people are losing their jobs and nobody knows how worse it can get.

Since most of the places are on lockdown, and you are working from home or have enough time to nourish your skills, then you should use this time wisely! We always complain that we want some ‘time’ to learn and upgrade our knowledge but don’t get it due to our ‘busy schedules’. So, now is the time to make a ‘list of skills’ and learn and upgrade your skills at home!

And for the technology-loving people like us, Knoldus Techhub has already helped us a lot in doing it in a short span of time!

If you are still not aware of it, don’t worry as Georgia Byng has well said,

“No time is better than the present”

– Georgia Byng, a British children’s writer, illustrator, actress and film producer.

No matter if you are a developer (be it front-end or back-end) or a data scientisttester, or a DevOps person, or, a learner who has a keen interest in technology, Knoldus Techhub has brought it all for you under one common roof.

From technologies like Scala, spark, elastic-search to angular, go, machine learning, it has a total of 20 technologies with some recently added ones i.e. DAML, test automation, snowflake, and ionic.

How to upgrade your skills?

Every technology in Tech-hub has n number of templates. Once you click on any specific technology you’ll be able to see all the templates of that technology. Since these templates are downloadable, you need to provide your email to get the template downloadable link in your mail.

These templates helps you learn the practical implementation of a topic with so much of ease. Using these templates you can learn and kick-start your development in no time.

Apart from your learning, there are some out of the box templates, that can help provide the solution to your business problem that has all the basic dependencies/ implementations already plugged in. Tech hub names these templates as xlr8rs (pronounced as accelerators).

xlr8rs make your development real fast by just adding your core business logic to the template.

If you are looking for a template that’s not available, you can also request a template may be for learning or requesting for a solution to your business problem and tech-hub will connect with you to provide you the solution. Isn’t this helpful 🙂

Confused with which technology to start with?

To keep you updated, the Knoldus tech hub provides you with the information on the most trending technology and the most downloaded templates at present. This you’ll be informed and learn the one that’s most trending.

Since we believe:

“There’s always a scope of improvement“

If you still feel like it isn’t helping you in learning and development, you can provide your feedback in the feedback section in the bottom right corner of the website.

#ai #akka #akka-http #akka-streams #amazon ec2 #angular 6 #angular 9 #angular material #apache flink #apache kafka #apache spark #api testing #artificial intelligence #aws #aws services #big data and fast data #blockchain #css #daml #devops #elasticsearch #flink #functional programming #future #grpc #html #hybrid application development #ionic framework #java #java11 #kubernetes #lagom #microservices #ml # ai and data engineering #mlflow #mlops #mobile development #mongodb #non-blocking #nosql #play #play 2.4.x #play framework #python #react #reactive application #reactive architecture #reactive programming #rust #scala #scalatest #slick #software #spark #spring boot #sql #streaming #tech blogs #testing #user interface (ui) #web #web application #web designing #angular #coronavirus #daml #development #devops #elasticsearch #golang #ionic #java #kafka #knoldus #lagom #learn #machine learning #ml #pandemic #play framework #scala #skills #snowflake #spark streaming #techhub #technology #test automation #time management #upgrade

Gunjan  Khaitan

Gunjan Khaitan

1621388005

Spark Full Course | Spark Tutorial For Beginners | Learn Apache Spark

This Apache Spark full course will help you learn the basics of Big Data, what Apache Spark is, and the architecture of Apache Spark. Then, you will understand how to install Apache Spark on Windows and Ubuntu. You will look at the important components of Spark, such as Spark Streaming, Spark MLlib, and Spark SQL. Finally, you will get an idea about implement Spark with Python in PySpark tutorial and look at some of the important Apache Spark interview questions.

Below topics are explained in this Apache Spark Full Course:

  1. Animated Video
  2. History of Spark
  3. What is Spark
  4. Hadoop vs spark
  5. Components of Apache Spark
  6. Spark Architecture
  7. Applications of Spark
  8. Spark Use Case
  9. Running a Spark Application
  10. Apache Spark installation on Windows
  11. Apache Spark installation on Ubuntu
  12. What is Spark Streaming
  13. Spark Streaming data sources
  14. Features of Spark Streaming
  15. Working of Spark Streaming
  16. Discretized Streams
  17. caching/persistence
  18. checkpointing in spark streaming
  19. Demo on Spark Streaming
  20. What is Spark MLlib
  21. What is Machine Learning
  22. Machine Learning Algorithms
  23. Spark MLlib Tools
  24. Spark MLlib Data Types
  25. Machine Learning Pipelines
  26. Spark MLlib Demo
  27. What is Spark SQL
  28. Spark SQL Features
  29. Spark SQL Architecture
  30. Spark SQL Data Frame
  31. Spark SQL Data Source
  32. Spark SQL Demo
  33. What is PySpark
  34. PySpark Features
  35. PySpark with Python and Scala
  36. PySpark Contents
  37. PySpark Sub packages
  38. Companies using PySpark
  39. PySpark Demo
  40. Spark Interview Questions

This Apache Spark and Scala certification training is designed to advance your expertise working with the Big Data Hadoop Ecosystem. You will master essential skills of the Apache Spark open source framework and the Scala programming language, including Spark Streaming, Spark SQL, machine learning programming, GraphX programming, and Shell Scripting Spark. This Scala Certification course will give you vital skillsets and a competitive advantage for an exciting career as a Hadoop Developer.

What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.

What are the course objectives?
Simplilearn’s Apache Spark and Scala certification training are designed to:

  1. Advance your expertise in the Big Data Hadoop Ecosystem
  2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark

What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:

  1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
  2. Understand the fundamentals of the Scala programming language and its features
  3. Explain and master the process of installing Spark as a standalone cluster

Who should take this Scala course?

  1. Professionals aspiring for a career in the field of real-time big data analytics
  2. Analytics professionals
  3. Research professionals
  4. IT developers and testers

Learn more at: https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training?utm_campaign=morioh.com

#spark #big-data #apache-spark