Big Data

Spark Basics

4.6 (62 Ratings)


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About this course

A new framework was proposed which is called Spark that supports these applications while retaining the scalability and fault tolerance of MapReduce. To achieve these goals, Spark introduces an abstraction called resilient distributed datasets (RDDs). An RDD is a read-only collection of objects partitioned across a set of machines that can be rebuilt if a partition is lost. Spark can outperform Hadoop by 10x in iterative machine learning jobs, and can be used to interactively query a very large dataset with the sub-second response time.

Skills covered

  • Spark
  • RDDs

Course Syllabus

Spark Basics and Streaming

  • Introduction to Spark
  • Spark vs Hadoop
  • Spark architecture
  • RDDs
  • Spark terminologies

Course Certificate

Get Spark Basics course completion certificate from Great learning which you can share in the Certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.

GL Academy Sample Certificate

Spark is a parallel processing framework just like Hadoop. However, it is quite different from other parallel processing frameworks. While Hadoop has many shortcomings in the area of machine learning, spark fulfils them all. Spark makes a lot of tasks much easier which were troublesome while performing with other parallel processing frameworks. 


What is Spark?

Apache spark is a fast, in-memory data processing engine that with development APIs allow executing streaming, machine learning, and SQL. It is a fast, expressive, cluster computing system that is compatible with Apache Hadoop. It improves efficiency through:

  • General Computation graphics, 
  • In-memory computing primitives
  • It is up to 100 times faster

Spark also improves usability because of an interactive shell, rich APIs in Java, Scala, and Python, and with much lesser coding. It is an open-source parallel process computational framework that is primarily used for analytics and data engineering. 


Apache Spark Features

Apache Spark was initiated at UC Berkeley in the year 2009. The open-source cluster computing framework is written on Scala that gives it the power of functional programming. It provides high-level APIs in Java, Scala, Python, and R.

Spark can be integrated with Hadoop and its ecosystem and can read the existing data. It is designed to be fast for interactive queries and iterative algorithms for which MapReduce is rendered inefficient. 

The most popular application of Spark is for running iterative machine learning algorithms. It has the support for in-memory storage and provides efficient fault recovery.

Now, let us see what is Scala?

Scala is a high-level programming language that supports the functional style of programming and the object-oriented style of programming. It is a multi-paradigm language.


Spark vs. Hadoop

If you already know Hadoop, then comparing Hadoop and Spark is the easiest way to learn Spark. Hadoop and Spark have a lot of similarities, but it is the difference between the two that brings out the importance of Spark. 

The high-level architecture of Hadoop and Spark is very similar i.e. the Master-Slave architecture with several data nodes and some master nodes.

Many drawbacks of Hadoop are fulfilled by Spark which gives a 10x-100x performance improvement over Hadoop. 

Spark is better suited for interactive and real-time analytics streaming data and big data as compared to Hadoop. 

Since Spark outperforms Hadoop in iterative workloads, there are many ML Spark applications and it is highly popular among machine learning professionals. The reason is that Spark’s framework itself facilitates to work better on iterative workloads, whereas the Hadoop framework fails because of intensive disk I/O operations. While in Spark, many mechanisms are placed to minimise the disk I/O operations.


About The Program

Spark Basics is a free course by Great Learning Academy that will help you in learning Spark from the scratch The course curriculum includes topics such as introduction to Spark, Spark Architecture, Hadoop vs. Spark, RDDs, and Spark terminologies. You will be starting from the very basics such as installing Spark and then move up to understand more advanced concepts such as scala spark, databricks spark, java spark and more. 

The course is delivered in the form of video content which is of 2 hours duration, hence it is a short course and you will be clear with Spark basics in no time. The video content is followed by a quiz where you can measure your learning and rework on the concepts that you are not perfectly clear with. 

Upon completion, you will get a certificate of completion from Great Learning which will add value to your resume and your professional profile in the field of big data and analytics. 


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