Apache Spark with Scala - Hands On with Big Data!
Why take this course?
🌟 Apache Spark with Scala - Hands On with Big Data! 🌟
Course Headline:
💻 Apache Spark tutorial with 20+ hands-on examples of analyzing large data sets, on your desktop or on Hadoop with Scala!
New! Completely updated and re-recorded for Spark 3, IntelliJ, Structured Streaming, and a stronger focus on the DataSet API. Dive into the world of "Big Data" analysis with this comprehensive course that's designed to get you up to speed with Apache Spark using Scala, the language of choice for handling large-scale data processing at Amazon, EBay, NASA JPL, Yahoo, and many others! 🚀
About the Course:
Learn from an Expert: This course is crafted by Frank Kane, an ex-engineer and senior manager from Amazon and IMDb with years of experience in big data technologies. He's here to guide you through the complexities of Spark and Scala with ease and expertise.
Course Inclusions:
- Scala Crash Course: A primer on Scala, even if Python is more familiar to you.
- Hands-On Learning: Over 20 practical examples to solidify your understanding of Spark's core concepts.
- Real-World Applications: Learn to analyze movie ratings, social graph data, and more using Spark.
- Technologies Covered: Resilient Distributed Datasets (RDDs), DataFrames, Datasets, Spark SQL, DataSets, Spark Streaming, Machine Learning, and GraphX.
- Cloud Integration: Scale your data analysis up to the cloud using Amazon's Elastic MapReduce (EMR).
- Hadoop YARN: Understand how Spark runs on Hadoop clusters.
Course Breakdown:
- Conceptual Foundation: Learn the foundational concepts of Spark, including RDDs, DataFrames, and Datasets.
- Scala Programming Language: Get a crash course in Scala to help you write efficient Spark scripts.
- Development Environment Setup: Develop and run Spark jobs using Scala, IntelliJ, and SBT.
- Complex Problem Solving: Translate real-world analysis problems into iterative or multi-stage Spark scripts.
- Cloud Computing: Scale your data analysis to gigabyte-scale data sets using Amazon EMR.
- Hadoop Distributed Computing: Explore how Spark is distributed across computing clusters with Hadoop YARN.
- Spark Technologies: Dive deeper into Spark SQL, DataFrames, Datasets, Spark Streaming, Machine Learning, and GraphX.
Hands-On Experience:
This course is packed with over 8 hours of video content, providing you with over 20 real examples to work through. These hands-on examples will guide you from the basics to complex Spark applications. You can learn at your own pace and on your own schedule.
Student Testimonial:
📚 "I studied Spark for the first time using Frank's course 'Apache Spark 2 with Scala - Hands On with Big Data!'. It was a great starting point for me, gaining knowledge in Scala and most importantly practical examples of Spark applications. It gave me an understanding of all the relevant Spark core concepts, RDDs, Dataframes & Datasets, Spark Streaming, AWS EMR. Within a few months of completion, I used the knowledge gained from the course to propose working primarily on Spark applications in my current company. Since then, I have continued to work with Spark." - Joey Faherty
Enroll now to embark on your journey into the world of big data with Apache Spark and Scala! 📈💾
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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