5 Apache Spark Courses to Accelerate Big Data Analytics for Data Scientists

Apache Spark is a powerful distributed computing framework for large-scale data processing and analytics. It offers significant advantages over traditional systems like Hadoop MapReduce. Spark utilizes in-memory processing through its Resilient Distributed Datasets (RDDs), and performs computations much faster. Spark integrates seamlessly with storage systems like HDFS, YARN, and Apache Mesos, making it a versatile choice for modern data architectures.

Apache Spark is a valuable tool for data scientists, allowing them to analyze large datasets efficiently. Spark provides an accessible platform for developing complex data workflows. By leveraging Spark's capabilities, data scientists can gain timely insights and drive impactful decision-making in today's fast-paced, data-driven environments. To help you choose the right course, we have listed some handpicked Spark courses which can be helpful for data scientists.

Advantages of using Spark in Big Data

Spark has several advantages over other big data solutions. It is a highly dynamic tool and supports in-memory computing of RDDs. Here are some of the advantages of using Spark in big data - 

• One of the highlights of Apache Spark is undoubtedly its exceptional speed. It can process data up to 100 times faster than older tools like MapReduce.
• Spark is designed to scale horizontally. It can handle huge data volumes seamlessly to meet your needs, whether working with gigabytes or even petabytes of information.
• Spark supports many programming languages, such as Python, Java, and Scala, making it easy for data scientists to write code in the language they are most comfortable with.
• Spark has a diverse ecosystem of additional libraries, such as Spark SQL for SQL queries, Spark MLlib for machine learning, and Spark Streaming for real-time data processing.

5 Spark Courses for Data Scientists

1. Big Data Analysis with Scala and Spark
2. Machine Learning with Apache Spark
3. Apache Spark with Scala - Hands On with Big Data!
4. Learn Spark & Data Lakes
5. Spark Basics

Must Explore - Big Data Analytics

1. Big Data Analysis with Scala and Spark 

The Big Data Analysis with Scala and Spark course introduces the use of Apache Spark for distributed data processing. You will learn how the data parallel approach works in a distributed environment and how it differs from familiar programming models like shared-memory collections or standard Scala collections. You will explore topics like latency and network communication, discovering ways to improve performance, read data from storage, manipulate it with Spark and Scala, write data analysis algorithms in a functional style, and avoid common issues like shuffles and recomputation.

Course Name	Big Data Analysis with Scala and Spark
Duration	27 hours
Provider	Coursera
Course Fee	Subscription-based - Rs. 4,117/month (Audit for free)
Trainer	Prof. Heather Miller - École Polytechnique Fédérale de Lausanne
Skills Gained	Apache Spark, SQL, Big Data, Scala Programming
Students Enrolled	100,600+
Total Reviews	4.6/5 (2500+ reviews)

2. Machine Learning with Apache Spark

The Machine Learning with Apache Spark course covers essential concepts and practical applications of machine learning (ML) within the context of big data. Participants will start by learning the fundamentals of ML, including supervised and unsupervised learning techniques. The course emphasizes the role of data engineering in preparing and managing data for ML applications. Through hands-on labs,

Learners will use SparkML to perform regression, classification, and clustering tasks, enabling them to build predictive models effectively. The course will also discuss integrating Spark with various data engineering processes, including connecting to Spark clusters and performing ETL (Extract, Transform, Load) activities. They will gain experience constructing ML pipelines, including feature extraction, transformation, and model persistence.

Course Name	Machine Learning with Apache Spark
Duration	3 weeks at 5 hours a week
Provider	Coursera
Course Fee	Subscription-based - Rs. 4,117/month (Audit for free)
Trainer	Prof. Heather Miller - École Polytechnique Fédérale de Lausanne
Skills Gained	Apache Spark, Machine Learning, ML Pipelines, Data Engineering, SparkML
Students Enrolled	12,000+
Total Reviews	4.5/5 (2500+ reviews)

3. Apache Spark with Scala - Hands On with Big Data! 

The course on Apache Spark with Scala focuses on analyzing and processing large datasets using the Spark framework. It covers key concepts such as Resilient Distributed Datasets (RDDs), DataFrames, and Datasets, which are essential tools for handling big data. The course includes a crash course in Scala, the programming language that works best with Spark. Learners will practice framing data analysis problems as Spark problems and learn how to run Spark jobs on their systems.

You will also learn how to scale data processing tasks using cloud computing services like Amazon's Elastic MapReduce, insight into how Hadoop YARN manages resources across computing clusters, Spark technologies, including Spark SQL for querying data, Spark Streaming for real-time data processing, and machine learning capabilities with MLlib. 

Course Name	Apache Spark with Scala - Hands On with Big Data!
Duration	9 hours
Provider	Udemy
Course Fee	Rs. 649 (Original Price Rs. 3,999, currently available at a discount of 84% )
Trainer	Frank Kane, Ex-Amazon Sr. Engineer and Sr. Manager, CEO Sundog Education; Sundog Education by Frank Kane,
Skills Gained	Spark SQL, DataFrames, DataSets, Spark Streaming, Machine Learning, and GraphX
Rating	4.6/5 (17,900+ ratings)
Students Enrolled	99,000+

Explore - Big Data Courses

4. Learn Spark & Data Lakes 

The course on Spark and Data Lakes provides a solid foundation for understanding the big data ecosystem and how to work with large datasets using Apache Spark effectively. You will learn how Spark processes and transforms data through distributed computing, the basics of data lakes and lakehouses, Spark architecture, its role in big data, and the specific challenges it addresses. Learners will also gain practical skills in using Spark for data wrangling, filtering, and transformation using PySpark and Spark SQL.

Furthermore, learners will learn to leverage AWS to manage data lakes effectively and work with AWS tools like S3 and AWS Glue. With the help of a hands-on project, learners can apply their knowledge by working with sensor data to train a machine learning model. 

Course Name	Learn Spark & Data Lakes
Duration	2 weeks
Provider	Udacity
Course Fee	All Access monthly - Rs. 20,500/month
Trainer	Sean Murdock - Professor at Brigham Young University Idaho
Skills Gained	Apache Spark, AWS data lakes, ELT, Big data fluency, Data wrangling, Data Lakehouse Architecture, Data format fundamentals, etc.
Rating	4.6/5 (36,400+ ratings)
Students Enrolled	184,000+

5. Spark Basics

The Spark Basics course introduces participants to the fundamentals of Apache Spark, including its architecture and the differences between Spark and Hadoop. The course also covers Resilient Distributed Datasets (RDDs), essential for processing large datasets across a distributed system. By understanding how Spark leverages in-memory computation, learners will see how it can outperform Hadoop, especially in iterative machine learning tasks and interactive queries.

Learners will learn to frame data analysis problems as Spark problems and gain experience in building Spark applications. The course curriculum also covers various topics, including how to run Spark jobs, manage resources with Hadoop YARN, and utilize other Spark technologies like Spark SQL and Spark Streaming.  

Course Name	Spark Basics
Duration	3 hours
Provider	Great Learning
Course Fee	Free
Trainer	Great Learning Academy
Skills Gained	Spark, Resilient Distributed Datasets (RDDs), Hadoop
Rating	4.5/5
Students Enrolled	17,000+