Taming Big Data with Apache Spark and Python – Hands On!
Taming Big Data with Apache Spark and Python – Hands On! Udemy Coupon by Sundog Education by Frank Kane with 7.5 hours on-demand video course and free 25 downloadable resources. PySpark tutorial with 20+ hands-on examples of analyzing large data sets on your desktop or on Hadoop with Python!
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Taming Big Data with Apache Spark and Python – Hands On!
New! Updated for Spark 3, more hands-on exercises, and a stronger focus on Data Frames and Structured Streaming.
“Big data” analysis is a hot and highly valuable skill – and this course will teach you the hottest technology in big data: Apache Spark and specifically PySpark. Employers including Amazon, eBay, NASA JPL, and Yahoo all use Spark to quickly extract meaning from massive data sets across a fault-tolerant Hadoop cluster. You’ll learn those same techniques, using your own Windows system right at home. It’s easier than you might think.
Learn and master the art of framing data analysis problems as Spark problems through over 20 hands-on examples and then scale them up to run on cloud computing services in this course. You’ll be learning from an ex-engineer and senior manager from Amazon and IMDb.
- Learn the concepts of Spark’s DataFrames and Resilient Distributed Datastores
- Develop and run Spark jobs quickly using Python and pyspark
- Translate complex analysis problems into iterative or multi-stage Spark scripts
- Scale up to larger data sets using Amazon’s Elastic MapReduce service
- Understand how Hadoop YARN distributes Spark across computing clusters
- Learn about other Spark technologies, like Spark SQL, Spark Streaming, and GraphX
By the end of this course, you’ll be running code that analyzes gigabytes worth of information – in the cloud – in a matter of minutes.
This course uses the familiar Python programming language; if you’d rather use Scala to get the best performance out of Spark, see my “Apache Spark with Scala – Hands On with Big Data” course instead.
We’ll have some fun along the way. You’ll get warmed up with some simple examples of using Spark to analyze movie ratings data and text in a book. Once you’ve got the basics under your belt, we’ll move to some more complex and interesting tasks. We’ll use a million movie ratings to find movies that are similar to each other, and you might even discover some new movies you might like in the process! We’ll analyze a social graph of superheroes and learn who the most “popular” superhero is – and develop a system to find “degrees of separation” between superheroes. Are all Marvel superheroes within a few degrees of being connected to The Incredible Hulk? You’ll find the answer.
This course is very hands-on; you’ll spend most of your time following along with the instructor as we write, analyze, and run real code together – both on your own system, and in the cloud using Amazon’s Elastic MapReduce service. 7 hours of video content is included, with over 20 real examples of increasing complexity you can build, run and study yourself. Move through them at your own pace, on your own schedule. The course wraps up with an overview of other Spark-based technologies, including Spark SQL, Spark Streaming, and GraphX.
Wrangling big data with Apache Spark is an important skill in today’s technical world. Enroll now!
What you’ll learn
- Use DataFrames and Structured Streaming in Spark 3
- Use the MLLib machine learning library to answer common data mining questions
- Understand how Spark Streaming lets your process continuous streams of data in real time
- Frame big data analysis problems as Spark problems
- Use Amazon’s Elastic MapReduce service to run your job on a cluster with Hadoop YARN
- Install and run Apache Spark on a desktop computer or on a cluster
- Use Spark’s Resilient Distributed Datasets to process and analyze large data sets across many CPU’s
- Implement iterative algorithms such as breadth-first-search using Spark
- Understand how Spark SQL lets you work with structured data
- Tune and troubleshoot large jobs running on a cluster
- Share information between nodes on a Spark cluster using broadcast variables and accumulators
- Understand how the GraphX library helps with network analysis problems
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Taming Big Data with Apache Spark and Python – Hands On! Course Reviews
Everything You Need to Know About Taming Big Data with Apache Spark and Python – Hands On!
This course offers a comprehensive and well-structured introduction to Python. Frank Kane the instructor, brings a wealth of expertise in Development, making this course both informative and engaging.
The course structure is easy to follow. Each section, for example, covers a different aspect of Big Data Course, ensuring a logical progression through the material. It includes video lectures, readings, and hands-on exercises, which make complex concepts accessible and practical.
Moreover, the instructor explains each topic clearly and concisely. He supports the lessons with plenty of examples and exercises, which help students grasp the material effectively.
What I appreciated most about this course is its practical focus. For instance, the instructor emphasizes teaching skills and knowledge that are directly applicable to real-world scenarios. Additionally, students gain access to helpful resources such as templates, checklists, and cheat sheets.
Another standout feature is the platform itself. Udemy Coupons offers flexibility, allowing students to learn at their own pace and access course materials from anywhere with an internet connection. Furthermore, the multiple payment options make it easy for students to choose a plan that suits their budget.
In addition, the course community is highly active, with forums where students can ask questions and engage with peers. The instructor, consequently, is very responsive and addresses student inquiries and feedback promptly.
Overall, I highly recommend the Taming Big Data with Apache Spark and Python – Hands On! to anyone looking to learn Python. This well-organized and practical course equips you with the skills and knowledge you need to succeed in this field.
