Packt
Apache Spark with Scala – Hands-On with Big Data!

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Apache Spark with Scala – Hands-On with Big Data!

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

10 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

10 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Design and implement advanced Spark applications tailored to complex data processing needs.

  • Develop and execute Spark scripts to process large datasets and stream real-time data.

  • Compare and optimize Spark algorithms for better performance in big data applications.

  • Assess machine learning models' effectiveness using Spark MLlib for accurate predictions.

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Recently updated!

October 2024

Assessments

4 assignments

Taught in English

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There are 10 modules in this course

In this module, we will focus on installing the necessary tools like IntelliJ and Scala on your local system, followed by a brief introduction to the Apache Spark framework and its key concepts.

What's included

2 videos1 reading

In this module, we will dive into Scala programming, covering its unique syntax, control flow, and key data structures like Map and List. You’ll get plenty of practice to ensure you’re comfortable with the language before jumping into Spark.

What's included

4 videos

In this module, we will break down the structure and usage of RDDs, the building blocks of Spark applications. You’ll work on real-world examples, including building histograms and analyzing social network data.

What's included

13 videos1 assignment

In this module, we will introduce SparkSQL, DataFrames, and Datasets, which provide a higher-level abstraction for working with structured data. You’ll also compare and contrast the use of RDDs with these APIs.

What's included

9 videos

In this module, we will tackle advanced Spark programming examples, such as finding popular movies and superheroes in a social graph. We will also cover optimization techniques using broadcast variables and accumulators.

What's included

11 videos

In this module, we will move from running Spark on a local desktop to deploying and scaling it on a real cluster using Amazon Elastic MapReduce (EMR). You’ll learn how to submit jobs, manage dependencies, and troubleshoot issues.

What's included

9 videos1 assignment

In this module, we will explore Spark MLlib for machine learning. You’ll work through examples like generating movie recommendations and performing linear regression on large datasets.

What's included

6 videos

In this module, we will focus on real-time data processing using Spark Streaming. You’ll build streaming applications and learn to handle data in motion, both with DStreams and the newer Structured Streaming API.

What's included

6 videos

In this module, we will cover Spark’s GraphX library for graph-parallel processing, using it to explore social network data and understand how connected entities are through complex algorithms like breadth-first search.

What's included

3 videos1 assignment

In this final module, we will provide additional learning resources and give you tips on leveraging your new skills to advance your career in the field of big data.

What's included

1 video1 assignment

Instructor

Packt - Course Instructors
Packt
373 Courses11,888 learners

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Packt

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