Apache Spark with Scala - Hands On with Big Data!

Apache Spark tutorial with 20+ hands-on examples of analyzing large data sets, on your desktop or on Hadoop with Scala!

4.55 (18269 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Develop distributed code using the Scala programming language

Transform structured data using SparkSQL, DataSets, and DataFrames

Frame big data analysis problems as Apache Spark scripts

Optimize Spark jobs through partitioning, caching, and other techniques

Build, deploy, and run Spark scripts on Hadoop clusters

Process continual streams of data with Spark Streaming

Traverse and analyze graph structures using GraphX

Analyze massive data set with Machine Learning on Spark

Course Gallery

Apache Spark with Scala - Hands On with Big Data! – Screenshot 1 — Screenshot 1 – Apache Spark with Scala - Hands On with Big Data!

Apache Spark with Scala - Hands On with Big Data! – Screenshot 2 — Screenshot 2 – Apache Spark with Scala - Hands On with Big Data!

Apache Spark with Scala - Hands On with Big Data! – Screenshot 3 — Screenshot 3 – Apache Spark with Scala - Hands On with Big Data!

Apache Spark with Scala - Hands On with Big Data! – Screenshot 4 — Screenshot 4 – Apache Spark with Scala - Hands On with Big Data!

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Comidoc Review

Our Verdict

The Apache Spark with Scala course on Udemy provides wide-ranging examples and an in-depth look into processing big data sets. Despite the minor shortcomings related to repetitive information and insufficient exposure to certain concepts, this course serves as a solid foundation for those starting their journey or scaling expertise in big data analysis using Spark and Scala. Given its recent update, this course also incorporates up-to-date practices ensuring learners can seamlessly apply the knowledge to modern projects.

What We Liked

Comprehensive coverage of Apache Spark and Scala with 20+ hands-on examples
In-depth explanations of transforming structured data using SparkSQL, DataSets, and DataFrames
Real-life case studies throughout the course to enable better understanding
Explains optimization techniques like partitioning, caching, and more for efficient Spark jobs

Potential Drawbacks

Lack of detailed explanations on certain concepts and repetition of code snippets
Minimal examples covering Spark Streaming and Hadoop integration
Some feedback suggests insufficient coverage of RDD's (Resilient Distributed Datasets) compared to DataFrames and Datasets
Instances of missing real-life explanations, leading to unclear understanding and implementation