Apache Spark In-Depth (Spark with Scala)

Apache Spark In-Depth (Spark with Scala), available at $59.99, has an average rating of 4.35, with 27 lectures, based on 478 reviews, and has 30775 subscribers.

You will learn about Apache Spark from scratch to in-depth, starting from simple word count program to Batch Processing to Spark Structure Streaming, Performance Tuning, Optimization, Application Development and Deployment. Completing this course will also make you ready for most interview questions Includes Optional Project and path to success This course is ideal for individuals who are People looking to advance their career in Data Engineering, Big Data, Hadoop, Spark or Already working on Big Data Hadoop/ Spark and want to clear the concepts It is particularly useful for People looking to advance their career in Data Engineering, Big Data, Hadoop, Spark or Already working on Big Data Hadoop/ Spark and want to clear the concepts.

Enroll now: Apache Spark In-Depth (Spark with Scala)

Summary

Title: Apache Spark In-Depth (Spark with Scala)

Price: $59.99

Average Rating: 4.35

Number of Lectures: 27

Number of Published Lectures: 27

Number of Curriculum Items: 27

Number of Published Curriculum Objects: 27

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Apache Spark from scratch to in-depth, starting from simple word count program to Batch Processing to Spark Structure Streaming, Performance Tuning, Optimization, Application Development and Deployment.
Completing this course will also make you ready for most interview questions
Includes Optional Project and path to success

Who Should Attend

People looking to advance their career in Data Engineering, Big Data, Hadoop, Spark
Already working on Big Data Hadoop/ Spark and want to clear the concepts

Target Audiences

People looking to advance their career in Data Engineering, Big Data, Hadoop, Spark
Already working on Big Data Hadoop/ Spark and want to clear the concepts

Learn Apache Spark From Scratch To In-Depth

From the instructor of successful Data Engineering courses on “Big Data Hadoop and Spark with Scala” and “Scala Programming In-Depth”

From Simple program on word count to Batch Processing to Spark Structure Streaming.
From Developing and Deploying Spark application to debugging.
From Performance tuning, Optimization to Troubleshooting

Contents all you need for in-depth study of Apache Spark and to clear Spark interviews.

Taught in very simple English language so any one can follow the course very easily.

No Prerequisites, Good to know basics about Hadoop and Scala

Perfect place to start learning Apache Spark

Apache Spark is a unified analytics engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

Speed

Run workloads 100x faster.

Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine.

Ease of Use

Write applications quickly in Java, Scala, Python, R, and SQL.

Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells.

Generality

Combine SQL, streaming, and complex analytics.

Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application.

Runs Everywhere

Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources.

Course Curriculum

Chapter 1: Apache Spark In-Depth (With Scala)

Lecture 1: Introduction to Data Engineering Career Path

Lecture 2: Day 1 – Introduction to Spark

Lecture 3: Day 2 – Introduction to Spark

Lecture 4: Day 3 – Spark Installation on Linux VM

Lecture 5: Day 4 – RDD Day 1

Lecture 6: Day 5 – RDD Day 2

Lecture 7: Day 6 – RDD Day 3

Lecture 8: Day 7 – RDD Day 4

Lecture 9: Day 8 – RDD Day 5

Lecture 10: Day 9 – Dataframe Day 1

Lecture 11: Day 10 – Dataframe Day 2

Lecture 12: Day 11 – Dataframe Day 3

Lecture 13: Day 12 – Dataframe Day 4

Lecture 14: Day 13 – Dataframe Day 5

Lecture 15: Day 14 – Dataframes Day 6

Lecture 16: Day 15 – Dataframes – Spark SQL

Lecture 17: Day 16 – Datasets

Lecture 18: Day 17 – Spark Application Development and Deployment

Lecture 19: Day 18 – Spark Application Development and Deployment

Lecture 20: Day 19 – Performance Tuning and Optimization

Lecture 21: Day 20 – Common Errors and Debugging

Lecture 22: Day 21 – Spark Streaming D 1

Lecture 23: Day 22 – Spark Streaming D 2

Lecture 24: Day 23 – Spark Streaming D 3

Lecture 25: Day 24 – Project

Lecture 26: Day 25 – What Next, Job Assistance and How to Prepare for Interview

Lecture 27: Career Guidance

Instructors

Harish Masand
Data Engineering and Data Science(Big Data, ML, AI )

Rating Distribution

1 stars: 4 votes
2 stars: 8 votes
3 stars: 38 votes
4 stars: 162 votes
5 stars: 266 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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