Apache Spark with Scala useful for Databricks Certification

Apache Spark with Scala useful for Databricks Certification, available at $59.99, has an average rating of 4.3, with 79 lectures, based on 57 reviews, and has 15246 subscribers.

You will learn about Apache Spark ( Spark Core, Spark SQL, Spark RDD and Spark DataFrame) Databricks Certification syllabus included in the Course An overview of the architecture of Apache Spark. Work with Apache Spark's primary abstraction, resilient distributed datasets(RDDs) to process and analyze large data sets. Develop Apache Spark 3.0 applications using RDD transformations and actions and Spark SQL. Analyze structured and semi-structured data using Datasets and DataFrames, and develop a thorough understanding about Spark SQL. This course is ideal for individuals who are Apache Spark Beginners, Beginner Apache Spark Developer, Bigdata Engineers or Developers, Software Developer, Machine Learning Engineer, Data Scientist It is particularly useful for Apache Spark Beginners, Beginner Apache Spark Developer, Bigdata Engineers or Developers, Software Developer, Machine Learning Engineer, Data Scientist.

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Summary

Title: Apache Spark with Scala useful for Databricks Certification

Price: $59.99

Average Rating: 4.3

Number of Lectures: 79

Number of Published Lectures: 79

Number of Curriculum Items: 79

Number of Published Curriculum Objects: 79

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Apache Spark ( Spark Core, Spark SQL, Spark RDD and Spark DataFrame)
Databricks Certification syllabus included in the Course
An overview of the architecture of Apache Spark.
Work with Apache Spark's primary abstraction, resilient distributed datasets(RDDs) to process and analyze large data sets.
Develop Apache Spark 3.0 applications using RDD transformations and actions and Spark SQL.
Analyze structured and semi-structured data using Datasets and DataFrames, and develop a thorough understanding about Spark SQL.

Who Should Attend

Apache Spark Beginners, Beginner Apache Spark Developer, Bigdata Engineers or Developers, Software Developer, Machine Learning Engineer, Data Scientist

Target Audiences

Apache Spark Beginners, Beginner Apache Spark Developer, Bigdata Engineers or Developers, Software Developer, Machine Learning Engineer, Data Scientist

Apache Spark with Scalauseful for Databricks Certification(Unofficial)

Apache Spark with Scala its a Crash Course for Databricks Certification Enthusiast (Unofficial) for beginners

“Big data” analysis is a hot and highly valuable skill – and this course will teach you the hottest technology in big data: Apache Spark. Employers including Amazon, eBay, NASA, Yahoo, and many more. All are using Spark to quickly extract meaning from massive data sets across a fault-tolerant Hadoop cluster. You’ll learn those same techniques, using your own Operating system right at home.

So, What are we going to cover in this course then?

Learn and master the art of framing data analysis problems as Spark problems through over 30+ hands-on examples, and then execute them up to run on Databricks cloud computing services (Free Service) in this course. Well, the course is covering topics which are included for certification:

1) Spark Architecture Components

Driver,
Core/Slots/Threads,
Executor
Partitions

2) Spark Execution

Jobs
Tasks
Stages

3) Spark Concepts

Caching,
DataFrame Transformations vs. Actions, Shuffling
Partitioning, Wide vs. Narrow Transformations

4) DataFrames API

DataFrameReader
DataFrameWriter
DataFrame [Dataset]

5) Row & Column (DataFrame)

6) Spark SQL Functions

In order to get started with the course And to do that you’re going to have to set up your environment.

So, the first thing you’re going to need is a web browser that can be (Google Chrome or Firefox, or Safari, or Microsoft Edge (Latest version)) on Windows, Linux, and macOS desktop

This is completely Hands-on Learning with the Databricks environment.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Download Resources

Lecture 1: Download Resources

Chapter 3: Introduction to Spark and Spark Architecture Components

Lecture 1: Introduction to Spark

Lecture 2: (Old) Free Account creation in Databricks

Lecture 3: (New) Free Account creation in Databricks

Lecture 4: Provisioning a Spark Cluster

Lecture 5: Basics about notebooks

Lecture 6: Tips to Improve Your Course Taking Experience

Lecture 7: Why we should learn Apache Spark?

Lecture 8: Spark Architecture Components

Lecture 9: Driver

Lecture 10: Partitions

Lecture 11: Executors

Chapter 4: Spark Execution

Lecture 1: Spark Jobs

Lecture 2: Spark Stages

Lecture 3: Spark Tasks

Lecture 4: Practical Demonstration of Jobs, Tasks and Stages

Chapter 5: Spark SQL, DataFrames and Datasets

Lecture 1: Spark RDD (Create and Display Practical)

Lecture 2: Spark Dataframe (Create and Display Practical)

Lecture 3: Anonymus Functions in Scala

Lecture 4: Extra (Optional on Spark DataFrame)

Lecture 5: Extra (Optional on Spark DataFrame) in Details

Lecture 6: Spark Datasets (Create and Display Practical)

Lecture 7: Caching

Lecture 8: Notes on reading files with Spark

Lecture 9: Data Source CSV File

Lecture 10: Data Source JSON File

Lecture 11: Data Source LIBSVM File

Lecture 12: Data Source Image File

Lecture 13: Data Source Arvo File

Lecture 14: Data Source Parquet File

Lecture 15: Untyped Dataset Operations (aka DataFrame Operations)

Lecture 16: Running SQL Queries Programmatically

Lecture 17: Global Temporary View

Lecture 18: Creating Datasets

Lecture 19: Scalar Functions (Built-in Scalar Functions) Part 1

Lecture 20: Scalar Functions (Built-in Scalar Functions) Part 2

Lecture 21: Scalar Functions (Built-in Scalar Functions) Part 3

Lecture 22: User Defined Scalar Functions

Chapter 6: Spark RDD

Lecture 1: Operation in Apache Spark

Lecture 2: Transformations

Lecture 3: map(function)

Lecture 4: filter(function)

Lecture 5: flatMap(function)

Lecture 6: mapPartitions(func)

Lecture 7: mapPartitionsWithIndex(func)

Lecture 8: sample(withReplacement, fraction, seed)

Lecture 9: union(otherDataset)

Lecture 10: intersection(otherDataset)

Lecture 11: distinct([numPartitions]))

Lecture 12: groupby(func)

Lecture 13: groupByKey([numPartitions])

Lecture 14: reduceByKey(func, [numPartitions])

Lecture 15: aggregateByKey(zeroValue)(seqOp, combOp, [numPartitions])

Lecture 16: sortByKey([ascending], [numPartitions])

Lecture 17: join(otherDataset, [numPartitions])

Lecture 18: cogroup(otherDataset, [numPartitions])

Lecture 19: cartesian(otherDataset)

Lecture 20: coalesce(numPartitions)

Lecture 21: repartition(numPartitions)

Lecture 22: repartitionAndSortWithinPartitions(partitioner)

Lecture 23: Wide vs. Narrow Transformations

Lecture 24: Actions

Lecture 25: reduce(func)

Lecture 26: collect()

Lecture 27: count()

Lecture 28: first()

Lecture 29: take(n)

Lecture 30: takeSample(withReplacement, num, [seed])

Lecture 31: takeOrdered(n, [ordering])

Lecture 32: countByKey()

Lecture 33: foreach(func)

Lecture 34: Shuffling

Lecture 35: Persistence (Cache)

Lecture 36: Unpersist

Lecture 37: Broadcast Variables

Lecture 38: Accumulators

Lecture 39: Important Lecture

Lecture 40: Bonus

Instructors

Bigdata Engineer
Bigdata Engineer

Rating Distribution

1 stars: 3 votes
2 stars: 2 votes
3 stars: 6 votes
4 stars: 24 votes
5 stars: 22 votes

Frequently Asked Questions

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