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
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Driver,
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Core/Slots/Threads,
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Executor
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Partitions
2) Spark Execution
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Jobs
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Tasks
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Stages
3) Spark Concepts
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Caching,
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DataFrame Transformations vs. Actions, Shuffling
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Partitioning, Wide vs. Narrow Transformations
4) DataFrames API
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DataFrameReader
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DataFrameWriter
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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
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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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