Getting Started with Spark

Install Spark on your machine now and get started with Spark today.

 

 

Crack Your Next Interview

Want to make it through the next interview you will appear for? Hone your skills with our five-part series of interview questions widely asked in the industry. With basic to advanced questions, this is a great way to expand your repertoire and boost your confidence.

Spark Interview Questions
Spark Interview Questions
Spark Interview Questions
Spark Interview Questions
Test Your Skills

Think you have it in you? Test your skills with our series of Spark quizzes and measure yourself to your expectations. Improvise in the process with questions carefully curated for different levels of difficulty.

Spark Quiz
Spark Quiz
Spark Quiz
Spark Quiz
Spark Quiz
Spark Quiz

 

Things to Learn

Choose where to begin, learn at your own pace:

Check out more cool technologies

Exploring the Framework

Let’s take a look at some facts about Spark and its philosophies.

Spark first showed up at UC Berkeley’s AMPLab in 2014. In 2010, it was open-sourced under a BSD license. Then in 2013, Zaharia donated the project to the Apache Software Foundation under an Apache 2.0 license. By February of 2014, it was a top-level Apache project. Today, Spark is an open-source distributed general-purpose cluster-computing framework; the Apache Software Foundation maintains it. It gives us an interface for programming whole clusters implementing implicit data parallelism and fault tolerance.

Essentially, Apache Spark is a unified analytics engine for large-scale data processing.

Apache Spark Founder Matei Zaharia

Matei Zaharia

What makes Spark so popular?

The project lists the following benefits:

1. Speed- Spark runs workloads 100x faster. Making use of a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine, it establishes optimal performance for both batch and streaming data.
2. Ease of Use- Spark lets you quickly write applications in languages as Java, Scala, Python, R, and SQL. With over 80 high-level operators, it is easy to build parallel apps. These can be availed interactively from the Scala, Python, R, and SQL shells.
3. Generality- Spark combines SQL, streaming, and complex analytics. With a stack of libraries like SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming, it is also possible to combine these into one application.
4. Runs Everywhere- Spark runs on Hadoop, Apache Mesos, or on Kubernetes. It will also run standalone or in the cloud, and can access diverse data sources.