As we approach 2023, internet users generate 2.5 quintillion bytes of data daily (a constantly growing figure).
There is an increase in demand for data and DevOps engineers. Organizations can manage, wrangle, clean, and analyze this priceless data resource by assembling a knowledgeable data team and using the appropriate technologies.
Big data analysis is an attractive and in-demand skill. Without a doubt, learning Apache Spark will be the need of the hour in 2023.
Apache Spark is one of the trendiest big data technologies that have an open-source analytics tool for large-scale data processing. Successful companies like Yahoo, Nasa JPL, eBay, and Amazon use Spark to analyze enormous data sets on the Hadoop cluster for insights. Let us dig deep into Apache Spark.
A Brief Overview of Apache Spark
In order to train a machine learning system in the real world, data might take hours or even days. The issue is resolved by Apache Spark, which offers quick access to data for machine learning and SQL load. While doing calculations in memory, it performs 100 times faster than Hadoop, and when performing MapReduce off of a disc, it performs 10 times faster.
More than 80 high-level operators are available in Spark, and they may be used interactively from the Scala, Python, R, and SQL shells. A number of libraries, including SQL and DataFrames, MLib for machine learning, GraphX, and Spark Streaming, are powered by the engine. Spark may be used solo, on Hadoop, Apache Mesos, Kubernetes, or in the cloud.
Apache Spark is a unified analytics engine for analyzing enormous amounts of data. As it makes use of a DAG scheduler, a query optimizer, and a physical execution engine, it is renowned for its outstanding performance for both batch and streaming data.
Prerequisite Before Learning Apache Spark
Prerequisites to learn Apache Spark can enable to work with big data more efficiently. However, it is important to note that you can learn these concepts and skills while you learn Spark. There are many courses and resources available that provide a comprehensive introduction to these topics and will help you get started with Apache Spark.
1. Programming language: You should have a good understanding of at least one programming language, such as Java, Python, or Scala, as Apache Spark is written in Scala and provides APIs in these languages.
2. Big data concepts: You should have a basic understanding of big data concepts such as Hadoop Distributed File System (HDFS), MapReduce, and data processing frameworks such as Apache Hadoop.
3. Distributed computing: Apache Spark is designed for distributed computing, so it is important to have a good understanding of distributed systems, including the challenges associated with processing data in a distributed environment.
4. SQL: A good understanding of SQL is also important, as Spark provides SQL-like APIs for working with structured data.
5. Data structures and algorithms: Familiarity with data structures such as arrays, lists, and maps, as well as algorithms such as sorting and searching, can be helpful in working with Spark’s APIs.
Choosing the Best Apache Course
Choosing the best Apache course online can be overwhelming with the multitude of options available. The best 8 Apache Spark courses for 2023 are listed below, along with justifications for each selection and an overview of the most important information. You can choose each course based on the following factors:
-
Reputable Platform: Choose a reputable online learning platform that offers high-quality courses and has positive reviews from past students. Platforms such as Udemy, Coursera, and edX are popular options.
-
Course Content: Look for courses that cover the specific Apache technology you want to learn, such as Apache Spark, Apache Kafka, or Apache Hadoop. The course should have comprehensive and updated content that meets your learning needs.
-
Instructor Expertise: The course instructor should have practical experience and expertise in using Apache technology. Check the instructor’s profile and reviews from past students.
-
Course Duration: The course duration should fit your schedule and learning pace. Choose a course that allows you to learn at your own pace, with flexible schedules and lifetime access.
-
Learning Format: Choose a learning format that suits your learning style, such as video tutorials, hands-on exercises, quizzes, and assignments.
-
Cost: Choose a course that offers value for money. Look for courses with affordable pricing, discounts, and free trials.
You can learn about these technologies at home on your computer. All you need to do is enroll in Apache Spark training for this. We have compiled that can help you learn apache courses online. These Apache Spark courses cover all technologies and learning levels, regardless of what programming language you are familiar with.
List of Best Apache Spark Courses to Enroll in 2023
1. Introduction to Spark with Sparklyr in R [DataCamp]
R is primarily designed to make it easier for you to build clear and concise data analysis programs. Apache Spark is made to analyze large datasets swiftly. You can get the best of both worlds by writing dplyr R code that runs on a Spark cluster using the sparkly library.
This course teaches you how to handle Spark DataFrames using both the dplyr interface and the native interface to Spark and will also cover machine learning methods using SparkML. You will learn about the Sparklyr package, which enables you to create R code in dplyr that can run on a Spark cluster.
For experienced R programmers who want to combine Spark’s speed and scalability with R’s data analysis optimization, this is one of the more sophisticated Spark classes.
Price: Paid Course (Only 1st chapter is Free)
Duration: 4 Hours
Certification: No
Level: Advanced
Pros:
- It covers 50 exercises and 4 videos.
- Offers a hands-on approach to learning Spark with Sparklyr in R.
- The course covers a wide range of topics, including Spark data frames, Spark SQL, Spark machine learning, and Spark streaming.
Cons:
- You need to have some basic knowledge of R programming language.
- The course covers most of the essential aspects of Sparklyr in R, but it does not cover advanced topics.
- Limited Focus on Big Data
2. Learn Spark at Udacity [Udacity]
This course is designed to provide an introduction to Apache Spark, a fast and flexible big data processing engine. Working with Big Data and developing scalable Big Data pipelines for machine learning are the main topics covered in this course.
The first lecture will introduce you to big data and how Spark fits into the big data ecosystem.
In the second lesson, you will process and clean datasets to become acquainted with Spark’s SQL and data frame APIs.
The third session will teach you how to debug and optimize your Spark code when it is running on a cluster.
In lesson four, you’ll learn how to train machine learning models at scale using Spark’s Machine Learning Library.
Price: Free
Duration: 10 Hours
Certification: No
Level: Intermediate
Pros:
- You will have rich content and interactive quiz.
- The course is taught by industries pros like David Drummon and Judit Lantos
- Self-paced process.
Cons:
- You must have a basic understanding of programming, particularly in Python.
- It does not go into advanced machine learning concepts or techniques.
- The content basically covers Data Science.
3. Apache Spark And Scala Certification Training [Edureka]
The Apache Spark Certification Training Course is designed to provide you with the knowledge and skills needed to become a successful Big Data & Spark Developer. You may prepare for the Cloudera Hadoop and Spark Developer Certification Test with Apache Spark and Scala Certification Courses (CCA175).
Live, instructor-led training that includes practical exercises will assist you in learning important Apache Spark concepts. You can enroll in this online course on Spark and Scala, as the flexible batches are available for both online classrooms and corporate training options.
Price: Paid
Duration: 10 Hours
Certification: Yes
Level: Intermediate
Pros:
- It provides lifetime access to the course.
- Its a hands-on, project-based learning.
- 60 days of free cloud lab access.
Cons:
- The course fees are relatively high.
- The course level is much more advanced for beginners.
4. Spark Starter Kit [Udemy]
This is one of the greatest free courses to start learning Apache Spark because it covers the essentials. The course aims to close the knowledge gap between what developers need and what is offered in the documentation for Apache Spark and other classes. You will get a thorough knowledge of some of the key ideas that underlie Spark’s execution engine and the secret to its effectiveness
This course is for anyone with an interest in big data, distributed systems, and related technologies. It makes an attempt to address a number of the often-asked issues about Apache Spark on StackOverflow and other forums.
Price: Free
Duration: 3 Hours
Certification: No
Level: Beginner
Pros:
- 15k students registered for that course.
- Superior to a number of other paid courses.
- The course provides hands-on exercises and examples
Cons:
- Some of the material may be out of date, as it was updated in 2017.
- No certification or credential is offered upon completion.
5. Advance your Data Skills in Apache Spark [LinkedIn Learning]
LinkedIn Learning is a great resource for learning Apache Spark and developing your data skills. This Apache Spark training is a complete learning route for aspiring data professionals that contains 11 LinkedIn courses. The fundamentals of Big Data and Apache Spark are covered in this excellent Spark course for beginners.
Whether you’re new to Spark or an experienced user, there are courses and tutorials available to help you advance your skills and take your career to the next level. This learning path is a great way to strengthen your CV and your skill set.
Price: Paid Course (1-month Free Access)
Duration: 18 Hours
Certification: Yes
Level: Beginner
Pros:
- It offers a broad range of content for beginners.
- There are many options available based on the level, from beginner to advanced.
- Instructors are highly skilled and experienced.
Cons:
- It requires a subscription to access its courses.
- The courses are lengthy and demand several hours to complete.
6. Learn Apache Sparks Basics [Simplilearn]
This course is created for complete novices who want to study Spark online as they attempt to enter the Big Data industry. The installation of Apache Spark on Windows and Ubuntu will be explained in this course.
Spark beginners can begin by studying the fundamentals first. You can move on to more advanced topics once you have mastered the fundamentals. The mentors who developed the videos included in the course are well-known business experts.
Price: 90 Days Free Access
Duration: 7 Hours
Certification: Yes
Level: Beginner
Pros:
- A Youtube option is available.
- Completely free for 90 days, so try to complete the course fast.
- You get a completion certificate in the free course.
Cons:
- The course is beginner friendly.
- The course was updated in 2018, so the content might be outdated.
7. Apache Spark Fundamentals [PluralSight]
This Pluralsight course on Apache Spark is excellent if you want to start using it from scratch. In this course, you’ll learn how to use Apache Spark to analyze your massive data at lightning-fast speeds, leaving Hadoop.
To boost your understanding, you’ll also develop a Wikipedia analysis application. Using the tools from this course, you may build your own Spark application with maximum speed.
Price: Paid Course (10-Days Free Access)
Duration: 4 Hours
Certification: No
Level: Beginner
Pros:
- On subscription, it gives you access to more than 5000+ courses on various other technologies.
- The instructor is quite experienced in the field of Scala.
- Covers the core concept of Apache Spark to analyze Big Data.
Cons:
- No certificate on the completion of the course.
- Only 10 days of free access; after that, you need to pay $29 on a monthly basis.
8. Apache Spark SQL [Exprefy Training]
Exprefy Training provides hands-on training with real-world examples and exercises that will help you gain practical experience working with Spark SQL. The course is for data-driven professionals who want to build, deploy, and optimize end-to-end Spark programs. You’ll learn how to create Spark apps and standalone clusters. Also, there is a thorough explanation of SparkSQL with 30+ Spark commands and 900+ lines of Spark code to go through.
Whether you’re new to Spark SQL or looking to advance your skills, Exprefy Training can help you achieve your goals.
Price: Paid Course
Duration: 4 Hours
Certification: Yes
Level: Beginner
Pros:
- It includes quizzes and puzzles that make the course interactive.
- Upon completion of all courses, you may add an industry-recognized certification to your resume.
- The instructor of the course has 20+ years of experience.
Cons:
- Prior knowledge of Uniox command lines and Python is necessary.
- You need to pay a monthly or yearly fee to enroll in this course.
Conclusion
Apache Spark gives us an unmatched opportunity to create cutting-edge applications. In terms of its impact on the big data world, it happens to be one of the fascinating technologies of the past ten years.
This concludes our discussion of some of the top Java, Scala, and Python courses for learning Apache Spark. Making the appropriate choice is crucial when evaluating massive data. Even Hadoop’s batch-processing architecture is unable to handle the size of modern data. The lightning speed of Apache Spark is currently required for the analysis of contemporary massive data collections.
Anyone from the top Apache Spark courses and certifications available online in 2023 will put you in solid hands. You can choose to follow our advice or evaluate these online courses for Apache Spark on your own.
