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Simplify Big Data Analytics with Amazon EMR

You're reading from  Simplify Big Data Analytics with Amazon EMR

Product type Book
Published in Mar 2022
Publisher Packt
ISBN-13 9781801071079
Pages 430 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Sakti Mishra Sakti Mishra
Profile icon Sakti Mishra

Table of Contents (19) Chapters

Preface Section 1: Overview, Architecture, Big Data Applications, and Common Use Cases of Amazon EMR
Chapter 1: An Overview of Amazon EMR Chapter 2: Exploring the Architecture and Deployment Options Chapter 3: Common Use Cases and Architecture Patterns Chapter 4: Big Data Applications and Notebooks Available in Amazon EMR Section 2: Configuration, Scaling, Data Security, and Governance
Chapter 5: Setting Up and Configuring EMR Clusters Chapter 6: Monitoring, Scaling, and High Availability Chapter 7: Understanding Security in Amazon EMR Chapter 8: Understanding Data Governance in Amazon EMR Section 3: Implementing Common Use Cases and Best Practices
Chapter 9: Implementing Batch ETL Pipeline with Amazon EMR and Apache Spark Chapter 10: Implementing Real-Time Streaming with Amazon EMR and Spark Streaming Chapter 11: Implementing UPSERT on S3 Data Lake with Apache Spark and Apache Hudi Chapter 12: Orchestrating Amazon EMR Jobs with AWS Step Functions and Apache Airflow/MWAA Chapter 13: Migrating On-Premises Hadoop Workloads to Amazon EMR Chapter 14: Best Practices and Cost-Optimization Techniques Other Books You May Enjoy

Test your knowledge

Before moving on to the next chapter, test your knowledge with the following questions:

  1. Assume you have integrated the complete ETL pipeline but when your input file gets pushed to the input S3 bucket, the Lambda function does not launch the EMR cluster. When you plan to debug the Lambda function execution, you don't find any logs for the Lambda function in CloudWatch log groups. What might be the problem that stops the Lambda function from writing logs in CloudWatch and how would you resolve it?
  2. Assume you have multiple data sources that are sending input files for processing. Instead of triggering an EMR cluster launch on an S3 file arrival event, you would like to schedule a PySpark job to run at a particular time of the day, so that it picks up all the input files available at that point of time for processing. How would you schedule the cluster creation and job execution?
  3. You have integrated Amazon EMR for your batch analytics workload...
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