29 Apr Hadoop and Spark
Hadoop and Spark are two popular big data processing frameworks used for distributed storage and processing of large data sets. Hadoop is an open-source software framework used for distributed storage and processing of large data sets. It provides a distributed file system and a framework for the processing of large data sets across clusters of computers. Spark is also an open-source data processing framework that provides a fast and general-purpose engine for large-scale data processing.
Hadoop is designed to store and process large data sets that are distributed across clusters of computers. It is built on the concept of a distributed file system, which enables the storage and retrieval of large data sets. The Hadoop Distributed File System (HDFS) is a scalable and fault-tolerant file system that is designed to store and manage large data sets. Hadoop uses a programming model called MapReduce to process large data sets across clusters of computers. MapReduce breaks down the processing of large data sets into smaller tasks that can be executed in parallel across multiple computers in a cluster.
Spark, on the other hand, is designed to provide a fast and general-purpose engine for large-scale data processing. It uses an in-memory data processing model that allows it to process data much faster than Hadoop. Spark supports a variety of programming languages, including Java, Python, and Scala, and provides APIs for machine learning, graph processing, and stream processing.
While both Hadoop and Spark are used for big data processing, they have some key differences. Hadoop is typically used for batch processing of large data sets, while Spark is used for both batch processing and real-time data processing. Hadoop is also slower than Spark due to its reliance on disk-based storage, whereas Spark uses in-memory storage to process data much faster. Additionally, Spark provides more programming language options and more advanced APIs for machine learning and other tasks.
In summary, Hadoop and Spark are two popular big data processing frameworks that enable the distributed storage and processing of large data sets. While Hadoop is designed for batch processing of large data sets, Spark provides a faster and more versatile processing engine that supports both batch processing and real-time data processing.
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Instructions
The purpose of this project is to support your in-class understanding of how data analytics stacks work and get some hands-on experience in using them. You will need to deploy Apache Hadoop as the underlying file system and Apache Spark as the execution engine. You will then develop several small applications based on them.
Task 1: Launch a cluster of virtual machines in a cloud environment (AWS). You will need to have one node as the master and at least two nodes as workers (slaves).
Task 2: Deploy the HDFS service on the cluster.
Task 3: Download the text version of Pride and Prejudice from Project Gutenberg, and save it to the HDFS cluster.
Task 4: Deploy the Spark service on the cluster.
Task 5: Use the file in HDFS as input, run a wordcount program in Spark to count the number of occurrences of each word. Sort the words by count, in descending order, and return a list of the (word, count) pairs for the 20 most used words.
Task 6: Write a Spark program that uses Monte Carlo methods to estimate the value of $π$.
Since the area of a circle of radius r is $A = πr^2$ , one way to estimate π is to estimate the area of the unit circle. A Monte Carlo approach to this problem is to uniformly sample points in the square $[−1, 1] × [−1, 1]$ and then count the percentage of points that land within the unit circle. The percentage of points within the circle approximates the percentage of the area occupied by the circle. Multiplying this percentage by 4 (the area of the square $[−1, 1] × [−1, 1]$) gives an estimate for the area of the circle.
Write a report, describing the commands you run, your observations, and output from all the steps in each task. Also explain the purpose of each step in your report. A screenshot can be used as an explanation. An explanation does not have to be done in paragraph form, list is fine (One sentence).
Report Example:
Task 1: *screenshot of what is being done in AWS*
. Explanation 1
. Explanation 2
Task 2: *screenshot of what is being done in AWS*
. Explanation 1
. Explanation 2
Disclaimer
The sample projects provided on our website are intended to be used as a guide and reference for educational purposes only. While we have made every effort to ensure that the projects are accurate and up-to-date, we do not guarantee their accuracy or completeness. The projects should be used at your own discretion, and we are not responsible for any loss or damage that may result from their use.
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