Amazon EMR and AWS Glue

5 minutes 5 Questions

AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to move data between different data stores. Amazon EMR integrates with AWS Glue Data Catalog, which stores metadata about data sources and provides a persistent metadata store. Users can run Apache Hive, Apach…

A Complete Guide on Amazon EMR and AWS Glue

Presenting a full guidance on Amazon EMR and AWS Glue, both highly essential components of AWS Solution Architect.
Importance: They're critical because they provide scalable, flexible and cost-efficient methods to process data. The choice of EMR contributes to the robust processing of huge data loads, while AWS Glue simplifies the process of data preparation.
Understanding Amazon EMR and AWS Glue: Amazon EMR (Elastic MapReduce) is an AWS (Amazon Web Service) tool for big data processing and analysis. EMR is based, amongst others, on Apache Hadoop, Apache Spark and Presto, and it allows you to process data across dynamically scalable Amazon EC2 instances.
On the other hand, AWS Glue is a fully managed ETL (extract, transform, and load) service that makes it simple and cost-effective to categorize your data, clean it, enrich it, and move it reliably between various data stores.
How it Works: For Amazon EMR, you just need to launch a cluster and operate on it. AWS takes care of the rest, providing the processing capacity as needed.
In case of AWS Glue, data is pulled out from a source, transformed using Glue, and pushed into a target where insight generation happens.
Exam Tips: Answering Questions on Amazon EMR and AWS Glue: Understanding the fundamental features, use cases, benefits and process flow of both Amazon EMR and AWS Glue is essential. Differentiate where EMR is used for big data processing and Glue is for ETL purposes. A good tip is to focus on key elements such as how AWS Glue automates much of the effort in building, maintaining, and running ETL jobs. Recognize how Amazon EMR allows easy, fast and cost-effective processing of large scale data.
Remember the connections of EMR with other big data technologies like Hadoop, Hive, and Spark, and know why and when to use Glue for ETL workloads.
Building a strong conceptual knowledge will help you in addressing the questions correctly. Exploring practical examples and use cases can provide a more solid understanding.

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Question 1

Your company processes large datasets with an Amazon EMR cluster. You need to temporarily pause the cluster daily during a specified time window. Which approach provides the best solution?

Set a data limit on the data processed by EMR during the specified time window. Terminate the cluster and create a new one outside the time window. Use AWS Lambda to stop and start the cluster daily during the time window. Configure EMR automatic scaling with a custom scaling policy based on time.

Correct Answer: Configure EMR automatic scaling with a custom scaling policy based on time.

Configuring EMR automatic scaling with a custom scaling policy based on time allows you to pause the cluster during the specified time window. The other options either don't fulfil the pause requirement or don't use Amazon EMR-specific solutions.

Question 2

Your organization requires a custom ETL script to process data from S3 using AWS Glue. The script needs to integrate with an external library for data manipulation. How should you proceed?

Submit a data transfer request to AWS to manually add the library. Use AWS CodeStar to create the ETL script, and reference the external library from there. Add the .zip or .egg file of the library as a Python library in the AWS Glue Job. Copy the library files to the AWS Glue working directory.

Correct Answer: Add the .zip or .egg file of the library as a Python library in the AWS Glue Job.

AWS Glue supports adding custom Python libraries in the form of .zip or .egg files, so you can include the external library file as a Python library without modifying the Glue working directory or going through unsupported procedures.

Question 3

You are analyzing your company's web logs using an EMR cluster, and you want to reduce the data processing costs. Which action is most efficient?

Delete the excessive logs from Amazon S3. Reduce the size of the EMR cluster by selecting fewer instances. Use EMR File System (EMRFS) with Amazon S3 Select to read only the required data. Move the web logs to a cheaper storage class in Amazon S3.

Correct Answer: Use EMR File System (EMRFS) with Amazon S3 Select to read only the required data.

By using EMR File System (EMRFS) with Amazon S3 Select, you can read only the desired subsets of the data from Amazon S3, which reduces data processing costs. The other options don't effectively decrease the amount of data processed or are not sustainable.

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