Learn Apache Spark (Apache Spark Tutorials for Beginners)
Apache Spark is a general data processing engine with multiple modules for batch processing, SQL and machine learning. As a general platform, it can be used in different languages like Java, Python, and Scala. It’s used by banks, games companies, telecommunications companies, and governments.
A lot of people compare Spark to Hadoop when this comparison is actually misplaced. Hadoop distributions nowadays include Spark, as Spark has proven dominant in terms of speed thanks to its in-memory data engine, and being user-friendly with its API.
How to learn Apache Spark?
There are multiple resources when it comes to data science, from books and blogs to online videos and courses. While having multiple resources to choose from is a huge advantage, it presents the inconvenience of choosing the best resource, especially in a fast-paced and quickly evolving industry.
This is why Hackr programming communitywould like to recommend its top 10 Spark resources list to save you the hassle of making a pick.
1. Apache Spark in Python: Beginner’s Guide
This community guide on DataCamp is one of the best guides out there for all beginners. Datacamp is a leading data-science and big data analytics learning platform with the best instructors from all over the industry.
The guide provides a hands-on understanding of Spark, why do you need and the usage case, and then proceeds on explaining the Spark APIs that are used, RDD, Dataset and DataFrame.
The guide goes from the very early learning steps, laying down the building blocks of the process, to explaining the pros and cons of using different languages with this platform and how to formulate your opinion regarding the matter. The guide aims to help you get acquainted with Spark before diving head-on with a course or an ebook purchase. In the end, it also recommends the Introduction to PySpark.
2. Taming Big Data with Apache Spark and Python
This Spark course is a go-to resource, being a best-seller on Udemy with over 28,000 enrolled students and 4.5 rating. This course covers the basics of Spark and builds around using the RDD (Resilient Distributed Datasets) which are the main building block of Spark.
The course also explores deployment and how to run Spark on a cluster using Amazon Web Services. MLLIB is also explored in this course to further explore the capabilities of Apache Spark. Furthermore in this course:
- Learn the concepts of Spark’s Resilient Distributed Datastores
- Develop and run Spark jobs quickly using Python
- 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
3. Apache Spark SQL
This 4 hours course is presented by an experienced instructor, Dr. Mark Plutowski.
The course requires some programming knowledge, while it’s not good news if you have no programming experience if you do have it then you can expect the course to progress faster than normal and build up your technical expertise of Spark.
The downside of it is that it’s somewhat expensive in comparison with the other resources we have on this list, as it is being provided at $129.
4. Machine Learning with Apache Spark
This Spark course is a multi-module Apache Spark course within the budget. Each module tackles a certain cornerstone of Spark up and explores Spark’s capabilities in Machine Learning in 3 modules.
5. Intro to Spark
Similar to the previous course, this an introduction to Spark on this Thinkific channel for Spark. It builds up toward the powerful 3 modules of the last series and aims to get you well acquainted with Spark before you jump into its ML applications.
6. Apache Spark 2 with Scala
This course is pretty similar to our no. 2 on this list. It is one of the best courses when it comes to Scala with a rating of 4.5 from over 5000 reviews and approximately 28,000 enrolled students.
You can expect to learn the following off of this 7.5 hours course:
- How to tackle big data analysis problems with Spark scripts and become able to approach Spark problems.
- Learn handful techniques such as partitioning and caching which are useful in optimizing Sparks jobs.
- Create Apache Spark scripts and be able to ship them by deploying and running them on Hadoop clusters.
- Use Graphx to deal with graph structures and be able to analyze them.
7. Learn Apache Spark from Scratch for Beginners
This one is a paid Eduonix course with over a hundred reviews and a 4.4 rating. It is a 4 hours course that aim to familiarize you with Spark components, runtime modes such as Yarn and Mesos, the Lambda architecture and the different Spark APIs.
The course only requires knowledge of programming language, anything from R, Python, and Scala, but Java is the preferred language.
8. Spark Fundamentals
This one is a free 4 hours Spark course on cognitiveclass.ai, led by two world-class Data scientists from IBM.
The course gives you access to the IBM data science experience along with all of the IBM services so that you can get to know and use the world leading technologies and be familiar with production platforms. It’s a priceless opportunity given that it’s a free course, with 5 dense modules that go through the Spark application Architecture, how to develop one, RDD and more.
9. Spark Overview for Scala Analytics
This one is yet another free course offered on cogniteclass.ai and offers 7 hours of well-tuned content to get you to understand Spark.
The course requires no prior knowledge of data science concepts as they will be explained along the way and attempts to talk about how Spark came to be, why is it useful, with a big focus on Spark’s RDD which is the main API used in Spark.
10. Spark and Python for Big Data with PySpark
Our last course on the list is this powerful Udemy course with around 21000 enrolled students and a 4.5 rating. The course is heavily focused on ML development and tackling ML problems with Spark. The course uses several AWS services to create and run Spark clusters which familiarizes you with the Spark environment and what you’ll be using when you create and run your own applications in Spark.
This course will teach the basics with a crash course in Python, continuing on to learning how to use Spark DataFrames with the latest Spark 2.0 syntax! Once you’ve done that you’ll go through how to use the MLlib Machine Library with the DataFrame syntax and Spark. All along the way you’ll have exercises and Mock Consulting Projects that put you right into a real world situation where you need to use your new skills to solve a real problem!
This course also covers the latest Spark Technologies, like Spark SQL, Spark Streaming, and advanced models like Gradient Boosted Trees!
These are the top 10 Apache Spark courses and tutorials on Hackr. If you want to know how many upvotes each of these tutorials got from community, and other details you can visit the link below for more:
Is there any course we are missing? Let us know and we will be glad to add that to the list. Thank you!
