Orchestration of Spark Jobs on EMR Cluster
Question
A company has a large number of spark jobs that need to run on an EMR cluster in AWS.
They want some way to orchestrate the series of jobs.
Which of the following can be used for this purpose?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Answer - A.
An example of this is given in the AWS Documentation.
########
What if you have a simple use case, in which you want to run a few Spark jobs in a specific order, but you don't want to spend time orchestrating those jobs or maintaining a separate application? You can do that today in a serverless fashion using AWS Step Functions.
You can create the entire workflow in AWS Step Functions and interact with Spark on Amazon EMR through Apache Livy.
########
Option B is invalid since this is queue-based service.
Option C is invalid since this is an open-source, data warehouse, and analytic package that runs on top of a Hadoop cluster.
Option D is invalid since this is an open-source Apache library that runs on top of Hadoop, providing a scripting language that you can use to transform large data sets without having to write complex code in a lower level computer language like Java.
For more information on this use case, please visit the url.
https://aws.amazon.com/blogs/big-data/orchestrate-apache-spark-applications-using-aws-step-functions-and-apache-livy/The answer to this question is A. AWS Step Functions.
AWS Step Functions is a fully managed service that makes it easy to coordinate the components of distributed applications and microservices using visual workflows. It provides a graphical interface for defining and running a series of steps or tasks, called a state machine, that represent the steps needed to complete a specific task or process.
In the case of the question, Step Functions can be used to orchestrate the series of Spark jobs running on an EMR cluster. By defining a state machine, the user can specify the sequence of Spark jobs to run and the conditions for their execution. Step Functions also provides error handling and retry mechanisms, making it easy to handle failures and timeouts.
AWS SQS (Simple Queue Service) is a fully managed message queuing service that enables decoupling and scaling of microservices, distributed systems, and serverless applications. It is not suitable for orchestrating a series of Spark jobs.
Apache Hive and Apache Pig are big data processing tools that are used to process large amounts of data. They are not orchestration tools and cannot be used to manage the execution of a series of Spark jobs.
In summary, AWS Step Functions is the best choice for orchestrating a series of Spark jobs running on an EMR cluster.
