Apache Spark Use Cases

1. Streaming – processing data continuously. The entire output is in a continuous data stream instead of regular batch processing.
2. Data Backup and Disaster Recovery – Spark is more used for backup and disaster recovery than most other distributed technologies due to its high speed in large-scale data handling.
3. Machine Learning – Spark provides a very effective, fast form parallel computation that is being used by many machine learning startups such as Skymind and many more people in the field of related technologies such as GraphX, MLlib, etc.

Apache Spark Alternatives

• Apache Hadoop is a platform for distributed processing of big data volumes across computer clusters using basic programming models. 
• Apache Flink is a framework and a distributed processing engine designed to perform stateful computations on unbounded and constrained data streams.
• Apache Sqoop is a tool for moving large amounts of data between Apache Hadoop and structured datastores like relational databases.
• Apache Storm is a networked real-time computer system that is open source.
• Lumify is a well-known big data fusion, analysis, and visualization tool that aids in the creation of actionable insight.

Apache Spark Books

• Spark: The Definitive Guide
• Mastering Spark with R
• Hands-On Deep Learning with Apache Spark
• Learning Spark: Lightning-Fast Data Analytics
• Learning Apache Spark 2

8 steps for a developer to learn Apache Spark

1. Explore the top Apache Spark books. These books help you learn Apache Spark and Hadoop concepts in general.
2. Find online tutorials, blogs, and web posts for Spark. You can use these resources to refresh your understanding of the top Apache Spark books or learn the basics of Apache Spark.
3. Videos are excellent tools for learning Apache Spark. Find Apache Spark videos on YouTube or other websites to learn more about Apache Spark.
4. Take some hands-on exercises and tests to learn Apache Spark. Find various projects and quizzes on websites.
5. Enroll in Apache Spark courses. There are a few courses that you can learn from to make yourself productive in Apache Spark.
6. Study on your own by developing applications based on Spark technology. This will help you not just learn the technical aspects but also understand the concept behind them and how to implement them in your projects.
7. Join an online community of developers and experts to discuss techniques, tips, hacks, and more about Apache Spark technology.
8. Take a training course or program to learn Apache Spark. When you complete your program, you will be certified. Having this certification will allow you to stand out from the crowd. Certification is a great way to showcase your skills and level of expertise. If you can provide the skills necessary for a project, the company will likely consider you for an opportunity.

Apache Spark Questions and Answers

•  Download and install Java 8.
• Install Python in the second step. 
• Get Apache Spark.
• Double-check the Spark Software File
• Download and install Apache Spark. 
• Install the winutils.exe program
• Configure Environment Variables 
•  Startup Spark

• Install Homebrew first. Open the Terminal application. In Terminal, type the following command:     $ /usr/bin/ruby -e “$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)” 
• Download and install xcode-select. We’ll use xcode-select to install Java, Scala, and Apache Spark using the Terminal command-line interface. In Terminal, type and run the following command.     $ xcode-select –install 
• Download and install Java. Enter and run the following command in the Terminal to install Java through the command line:     $ brew cask install java
• Download and install Scala. Enter and run the following command in Terminal to install Scala through the command line:    $ brew install scala 
• Download and install Spark Enter and run the following command in the Terminal to install Apache Spark through the command line:    $ brew install apache-spark 
• Double-checking the installation Run the Spark with the following command in Terminal to see if the installation was successful:    $ spark-shell

• Download and install the Java Runtime Environment. To run Apache Spark, we’ll need to ensure we have Java installed on our Ubuntu system.
• Get Apache Spark. You may get the most recent version of Apache Spark from the downloads page. 
• Launch a master server on its own. The start-master.sh command can now be used to start a standalone master server. 
• Begin the Spark Worker procedure Spark Worker Process is started with the start-slave.sh command. 
• Make use of the Spark shell. To access Spark Shell, use the spark-shell command.

Pros and Cons at a Glance

Sample Apache Spark Practice Questions

Try these questions from our free Apache Spark practice tests. The correct answer and an explanation follow each question.

1. What does the foreachBatch() sink do in Structured Streaming?

   • A. Processes each micro-batch as a static DataFrame with a user-defined function
   • B. Iterates over each row in the stream individually
   • C. Divides the stream into fixed-size batches for processing
   • D. Writes each batch to a different output path

   Answer: A. Processes each micro-batch as a static DataFrame with a user-defined function

   foreachBatch() allows you to apply arbitrary operations on each micro-batch DataFrame, useful for writing to non-native sinks.

2. What is the Tungsten execution engine in Spark?

   • A. A new streaming engine replacing DStream
   • B. A low-level execution engine that uses off-heap memory and code generation for performance
   • C. A GPU-accelerated computation backend
   • D. A distributed SQL planner for Spark SQL

   Answer: B. A low-level execution engine that uses off-heap memory and code generation for performance

   Tungsten is Spark's physical execution engine that uses off-heap memory management and whole-stage code generation to maximize performance.

3. Which of the following statements about Spark R is correct?

   • A. It enables users to run SQL / HQL queries on the top of Spark.
   • B. It allows data scientists to analyze large datasets and interactively run jobs
   • C. It is the kernel of Spark
   • D. It is the scalable machine learning library which delivers efficiencies

   Answer: B. It allows data scientists to analyze large datasets and interactively run jobs

   SparkR is an R package that provides a lightweight frontend to use Apache Spark from R. It enables R users to interact with Spark, perform data analysis on large datasets, and leverage Spark's distributed computing capabilities within their familiar R environment. This integration empowers data scientists to scale their R workflows.

4. What does the BinaryClassificationEvaluator use by default to evaluate model performance in Spark ML?

   • A. Accuracy
   • B. F1 Score
   • C. Area Under ROC Curve (AUC-ROC)
   • D. Precision

   Answer: C. Area Under ROC Curve (AUC-ROC)

   BinaryClassificationEvaluator defaults to Area Under ROC Curve (areaUnderROC) as its evaluation metric.

Take the full Apache Spark practice test