In this blog post, we will review the Essential AWS EMR interview questions frequently asked in interviews.
Question: What is Amazon EMR?
Answer: Amazon EMR (previously known as Amazon elastic MapReduce) is a managed data cluster platform that simplifies running Apache Hadoop, Spark, and other distributed data processing frameworks on AWS to process and analyze vast amounts of data. Amazon EMR allows the transformation and transfer of large amounts of data into and out of other AWS data stores and databases, such as Amazon Simple Storage Service and Amazon DynamoDB.
Question: What are the key components of Amazon EMR?
Answer: Amazon EMR has three key components.
- Clusters
- Steps
- Bootstrap actions.
Question: How Does Amazon EMR handle data storage and processing?
Answer: It uses Amazon S3 for long-term storage and HDFS (Hadoop distributed File System) for temporary storage during processing. It uses an AWS EC2 instance to process the data in the AWS cluster.
Question: Does Amazon EMR have an Auto Scaling feature?
Answer: Yes, Amazon EMR has the auto-scaling feature. It allows to automatic increase or decrease of the number of instances in one cluster based on certain metrics that are defined through CloudWatch. This feature helps in optimizing resources and reducing costs.
Question: How can an organization monitor Amazon EMR clusters?
Answer: Organizations can use Amazon CloudWatch to monitor cluster metrics, EMR logs stored in s3 for troubleshooting, and an EMR step console for monitoring job progress.
Question: What are different ways to secure data in Amazon EMR?
Answer: Amazon EMR provides various ways to secure data. Some of them are listed below.
- Encryption at rest using AWS Key Management Service(KMS) or customer-managed keys.
- Encryption in transit using TLS (Transport Layer Security)
- IAM (Identity and Access Management) policies to control access to the EMR cluster.
Question: What are the different AWS services Amazon EMR can integrate with?
Answer: Amazon EMR can integrate with various AWS services. Some of the popular ones are listed below.
- Amazon S3
- AWS Redshift
- AWS Glue
- AWS LAMBDA
- AWS Kinesis
- DynamoDB
- AWS Step function for orchestration of various data flows
Question: What is the difference between the core node and the Task node in the EMR cluster?
Answer: In Amazon EMR, core nodes store data through HDFS and Data processing. Task nodes in EMR are optional nodes that are only responsible for data processing and do not store data in HDFS.
Question: What are different file systems supported by the EMR storage layer?
Answer: Amazon EMR supports three file systems in the storage layer.
- HDFS(Hadoop Distributed File System)
- EMR File System (EMRFS)
- Local file system
Question: What are different data processing frameworks supported by Amazon EMR?
Answer: Amazon EMR supports two data processing frameworks.
- Hadoop MapReduce
- Apache Spark
Question: What are different applications supported by Amazon EMR?
Answer: Amazon EMR supports many applications to process workloads, leverage machine learning algorithms, and develop stream processing applications and data warehouses. These applications are mainly Hive, pig, and Spark libraries (Spark SQL, MLib and Graphx) while using Java as a programming language.
Question: What are the different Big data-based open-source applications supported in Amazon EMR?
Answer: Amazon EMR supports different open-source applications. They are listed below.
- Apache Spark
- Apache Hadoop
- Apache HBase
- Presto
Question: What are different use cases where Amazon EMR can be used?
Answer: Amazon EMR can be used in a variety of use cases, as given below.
- Clickstream Analysis
- Extract, Transform and Load(ETL) Process
- Real-Time Analytics
- Log Analytics
- Prediction Analytics
- Genomics
Question: What are different programming Languages supported by Amazon EMR?
Answer: Amazon EMR supports different types of programming languages.
- SQL
- Java
- Python
- R
