Taming Big Data with Apache Spark and Python – Hands On!
Original price was: $24.99.$15.99Current price is: $15.99.
What you’ll learn
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Use DataFrames and Structured Streaming in Spark 3
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Frame big data analysis problems as Spark problems
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Use Amazon’s Elastic MapReduce service to run your job on a cluster with Hadoop YARN
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Install and run Apache Spark on a desktop computer or on a cluster
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Use Spark’s Resilient Distributed Datasets to process and analyze large data sets across many CPU’s
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Implement iterative algorithms such as breadth-first-search using Spark
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Use the MLLib machine learning library to answer common data mining questions
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Understand how Spark SQL lets you work with structured data
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Understand how Spark Streaming lets your process continuous streams of data in real time
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Tune and troubleshoot large jobs running on a cluster
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Share information between nodes on a Spark cluster using broadcast variables and accumulators
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Understand how the GraphX library helps with network analysis problems
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New! Updated for Spark 3, more hands-on exercises, and a stronger focus on DataFrames 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. 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.
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Learn the concepts of Spark’s DataFrames and Resilient Distributed Datastores
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Develop and run Spark jobs quickly using Python
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Translate complex analysis problems into iterative or multi-stage Spark scripts
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Scale up to larger data sets using Amazon’s Elastic MapReduce service
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Understand how Hadoop YARN distributes Spark across computing clusters
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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!
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” I studied “Taming Big Data with Apache Spark and Python” with Frank Kane, and helped me build a great platform for Big Data as a Service for my company. I recommend the course! ” – Cleuton Sampaio De Melo Jr.
Who this course is for:
- People with some software development background who want to learn the hottest technology in big data analysis will want to check this out. This course focuses on Spark from a software development standpoint; we introduce some machine learning and data mining concepts along the way, but that’s not the focus. If you want to learn how to use Spark to carve up huge datasets and extract meaning from them, then this course is for you.
- If you’ve never written a computer program or a script before, this course isn’t for you – yet. I suggest starting with a Python course first, if programming is new to you.
- If your software development job involves, or will involve, processing large amounts of data, you need to know about Spark.
- If you’re training for a new career in data science or big data, Spark is an important part of it.
12 reviews for Taming Big Data with Apache Spark and Python – Hands On!
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Original price was: $24.99.$15.99Current price is: $15.99.
Sebastian Teo –
1) not able to get the code running for structured streaming in python, in section 7. I believe lots of student face the same problem. Especially error message of ‘logs’ folder, which we or I had created and by check multiple times using visual studio code. Moreover, if I were to read in the files by using sc.textFile.
2) more can be discuss with using pyspark dataframe.
3) I think will be best if there is a section on logging out the error message. On real case scenario, like python where we can use try exception error.
Sultan Laskar –
It was an excellent course to go through. I have learnt spark with python.
Tushar Singh –
This gives some insight to spark with some real interesting problems loved the marvel one was fun to learn from it please make a follow up course with more material focused on streaming and more examples
Jacson Chong –
This course was too general without concentration on specific material. Concepts were not clearly explained and up to date, the lecturer just read the written code, which could be tough for beginner to understand the beauty of spark applications. Besides, the TA’s response was slow.
Radi –
Very good course, really hands on! Maybe some upgrades here and there in the code would be a good idea…
Elías Samuel Patiño Fernandez –
The course is awsome. The best that I’ve found about Pyspark. It’s easy to understand. I suggest that you add more exercises/solution with different types of problems. Thak you!
Akhilesh Kumar9 –
Course was good. But it was very fast. I think it could be more better if it was slow and more elaborated. It could be more better if there were explanation to save output, ML model and reuse it etc etc.
Bruno Fernandes –
Very well structured and nice examples with code. Thank you very much for your work.
As a suggestion I would recommend using white background in the slides. It would make nicier in the documents for folks like me that like to take notes and do some screenshots from the video.
Jennifer Bryson –
I really enjoyed the course. The easy start of the first four sections was great. I also appreciated seeing the complex examples in the later sections. It was a great course, and I would recommend it. One thing I would’ve liked more information on is how the simple examples get split up among the cores/nodes and what happens behind the scenes in these easy cases. Also I personally would like the directions for Mac instead of Windows, but I was able to make it work on my Mac with the Windows directions.
Valente Cruz Vazquez –
It’s sounds so much interesting, this topic about big data is a new concept and is a good opportunity to take this course
Abhinav Srivastva –
Awesome course! Covers RDD as well as dataframe which are both important to understand spark’s capabilities. Good examples to keep in handy for future reference.
Victor Negron –
This is a good course and one I recommend for learning how to handle data with Apache Spark and Python