Analyzing Big Data in R using Apache Spark Cognitive Class Exam Answers

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Introduction to Analyzing Big Data in R using Apache Spark

Analyzing Big Data in R using Apache Spark combines the powerful data manipulation capabilities of R with the scalability and efficiency of Apache Spark’s distributed computing framework. This integration allows data scientists and analysts to work with large datasets that exceed the memory capacity of a single machine.

Key Concepts:

1. Apache Spark:
   • Apache Spark is an open-source distributed computing system that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.
   • It supports in-memory processing, which makes it much faster than traditional disk-based processing systems.
2. R and Spark Integration:
   • The sparklyr package in R provides an interface to Apache Spark from within the R environment.
   • sparklyr allows you to connect to a Spark cluster, manipulate data using familiar dplyr verbs, and perform advanced analytics.
3. Basic Workflow:
   • Connecting to Spark: Use spark_connect() to establish a connection to a Spark cluster.
   • Data Manipulation: Manipulate Spark DataFrames using dplyr functions (mutate(), filter(), group_by(), etc.) via sparklyr.
   • Machine Learning: Leverage Spark’s MLlib library for scalable machine learning tasks directly from R.

Steps to Get Started:

1. Set Up Apache Spark:
   • Install Apache Spark on your system or connect to a Spark cluster (local or remote).
2. Install Required Packages:
   • Install sparklyr package in R: install.packages("sparklyr").
   • Optionally, install dplyr for data manipulation: install.packages("dplyr").
3. Connecting to Spark:
   • Load sparklyr library: library(sparklyr).
   • Connect to Spark: sc <- spark_connect(master = "local") (replace "local" with your Spark master URL for a remote cluster).
4. Data Manipulation:
   • Load data into Spark: my_data_tbl <- spark_read_csv(sc, "path/to/data.csv").
   • Manipulate data using dplyr verbs: filtered_data <- my_data_tbl %>% filter(column_name > threshold).
5. Machine Learning with Spark MLlib (optional):
   • Train machine learning models: Use ml_* functions (e.g., ml_linear_regression(), ml_decision_tree()) from sparklyr.
6. Disconnecting from Spark:
   • When done, disconnect from Spark: spark_disconnect(sc).

Benefits:

• Scalability: Handle large datasets that exceed memory capacity.
• Speed: Utilize in-memory processing for faster computations.
• Integration: Leverage R’s extensive data manipulation and visualization capabilities with Spark’s distributed computing power.

Analyzing Big Data in R using Apache Spark thus empowers data scientists to tackle complex analyses and machine learning tasks that were previously impractical with traditional R-based approaches.

Analyzing Big Data in R using Apache Spark Cognitive Class Certification Answers

Module 1: Introduction to SparkR Quiz Answers

Question 1: What shells are available for running SparkR?

• Spark-shell
• SparkSQL shell
• SparkR shell
• RSpark shell
• None of the options is correct

Question 2: What is the entry point into SparkR?

• SRContext
• SparkContext
• RContext
• SQLContext

Question 3: When would you need to call sparkR.init?

• using the R shell
• using the SR-shell
• using the SparkR shell
• using the Spark-shell

Module 2: Data Manipulation in SparkR Quiz Answers

Question 1: dataframes make use of Spark RDDs

• False
• True

Question 2: You need read.df to create dataframes from data sources?

• True
• False

Question 3: What does the groupBy function output?

• An Aggregate Order object
• A Grouped Data object
• An Order By object
• A Group By object

Module 3: Machine Learning in SparkR Quiz Answers

Question 1: What is the goal of MLlib?

• Integration of machine learning into SparkSQL
• To make practical machine learning scalable and easy
• Visualization of Machine Learning in SparkR
• Provide a development workbench for machine learning
• All of the options are correct

Question 2: What would you use to create plots? check all that apply

• pandas
• Multiplot
• Ggplot2
• matplotlib
• all of the above are correct

Question 3: Spark MLlib is a module of Apache Spark

• False
• True

Analyzing Big Data in R using Apache Spark Final Exam Answers

Question 1: Which of these are NOT characteristics of Spark R?

• it supports distributed machine learning
• it provides a distributed data frame implementation
• is a cluster computing framework
• a light-weight front end to use Apache Spark from R
• None of the options is correct

Question 2: True or false? The client connection to the Spark execution environment is created by the shell for users using Spark:

• True
• False

Question 3: Which of the following are not features of Spark SQL?

• performs extra optimizations
• works with RDDs
• is a distributed SQL engine
• is a Spark module for structured data processing
• None of the options is correct

Question 4: True or false? Select returns a SparkR dataframe:

• False
• True

Question 5: SparkR defines the following aggregation functions:

• sumDistinct
• Sum
• count
• min
• All of the options are correct

Question 6: We can use SparkR sql function using the sqlContext as follows:

• head(sql(sqlContext, “SELECT * FROM cars WHERE cyl > 6”))
• SparkR:head(sql(sqlContext, “SELECT * FROM cars WHERE cyl > 6”))
• SparkR::head(sql(sqlContext, “SELECT * FROM cars WHERE cyl > 6”))
• SparkR(head(sql(sqlContext, “SELECT * FROM cars WHERE cyl > 6”)))
• None of the options is correct

Question 7: Which of the following are pipeline components?

• Transformers
• Estimators
• Pipeline
• Parameter
• All of the options are correct

Question 8: Which of the following is NOT one of the steps in implementing a GLM in SparkR:

• Evaluate the model
• Train the model
• Implement model
• Prepare and load data
• All of the options are correct

Question 9: True or false? Spark MLlib is a module SparkR to provide distributed machine learning algorithms.

• True
• False