Table Of ContentSimplify Big Data
Analytics with
Amazon EMR
A beginner's guide to learning and implementing
Amazon EMR for building data analytics solutions
Sakti Mishra
BIRMINGHAM—MUMBAI
Simplify Big Data Analytics with Amazon EMR
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Contributors
About the author
Sakti Mishra is an engineer, architect, author, and technology leader with over 16 years
of experience in the IT industry. He is currently working as a senior data lab architect at
Amazon Web Services (AWS).
He is passionate about technologies and has expertise in big data, analytics, machine
learning, artificial intelligence, graph networks, web/mobile applications, and cloud
technologies such as AWS and Google Cloud Platform.
Sakti has a bachelor's degree in engineering and a master's degree in business
administration. He holds several certifications in Hadoop, Spark, AWS, and Google
Cloud. He is also an author of multiple technology blogs, workshops, white papers and is a
public speaker who represents AWS in various domains and events.
About the reviewers
Suvojit Dasgupta is a senior data architect with AWS, focusing on data engineering
and analytics. In his 17 years of experience, he has led multiple strategic initiatives to
design, build, migrate, modernize, and operate petabyte-scale data platforms for Fortune
500 companies. He is passionate about data architecture and takes pride in building
well-architected solutions. In his free time, he likes to explore new technologies and listen
to audio books. You can follow Suvojit on Twitter at @suvojitdasgupta.
Praveen Gupta is currently a data engineering manager with AWS, and has over 17 years
of experience in the IT industry. Praveen started his career as an ETL/reporting developer
working on traditional RDBMSs and reporting tools. Since 2014, he has been working on
the AWS cloud on projects related to data science/machine learning and building complex
data engineering pipelines on AWS. He specializes in data ingestion, big data processing,
reporting, and building massive data warehouses at the petabyte scale for his customers,
helping them make data-driven decisions. Praveen has an undergraduate degree and a
master's degree, both in computer science from UIUC, USA. Praveen lives in Portland,
USA with his wife and 8-year-old daughter.
Table of Contents
Preface
Section 1: Overview, Architecture, Big Data
Applications, and Common Use Cases of
Amazon EMR
1
An Overview of Amazon EMR
What is Amazon EMR? 4 Amazon DynamoDB 16
What is big data? 4 Amazon Redshift 16
Hadoop – processing framework to AWS Lake Formation 17
handle big data 5 AWS Identity and Access
Overview of Amazon EMR – managed Management (IAM) 17
and scalable Hadoop cluster in AWS 8 AWS Key Management Service (KMS) 17
A brief history of the major big Lake House architecture overview 17
data releases 9
EMR release history 19
Benefits of Amazon EMR 9 Comparing Amazon EMR
Decoupling compute and storage 11 with AWS Glue and AWS Glue
DataBrew 21
Persistent versus transient clusters 12
AWS Glue 21
Integration with other AWS
AWS Glue DataBrew 23
services 14
Choosing the right service for your
Amazon S3 with EMR File System (EMRFS) 15 use case 24
Amazon Kinesis Data Streams (KDS) 15
Summary 26
Amazon Managed Streaming for
Kafka (MSK) 15 Test your knowledge 27
AWS Glue Data Catalog 15 Further reading 27
Amazon Relational Database
Service (RDS) 16
viii Table of Contents
2
Exploring the Architecture and Deployment Options
EMR architecture deep dive 30 Submitting jobs to the cluster as
EMR steps 41
Distributed storage layer 31
YARN – cluster resource manager 32 Building Hadoop jobs with
Distributed processing frameworks 33 dependencies in a specific
Hadoop applications 33 EMR release version 43
Understanding clusters EMR deployment options 44
and nodes 34 Amazon EMR on Amazon EC2 44
Uniform instance groups 35 Amazon EMR on Amazon EKS 45
Instance fleet 36 Amazon EMR on AWS Outposts 49
EMR pricing for different
Using S3 versus HDFS for
deployment options 51
cluster storage 37
Monitoring and controlling your costs
HDFS as cluster-persistent storage 37 with AWS Budgets and Cost Explorer 54
Amazon S3 as a persistent data store 38
Summary 55
Understanding the cluster Test your knowledge 55
life cycle 39
Further reading 56
Options to submit work to the cluster 41
3
Common Use Cases and Architecture Patterns
Reference architecture for Reference architecture for
batch ETL workloads 58 interactive analytics and ML 66
Use case overview 59 Use case overview 67
Reference architecture walkthrough 59 Reference architecture walkthrough 68
Best practices to follow during Best practices to follow during
implementation 61 implementation 70
Reference architecture for Reference architecture for
clickstream analytics 62 real-time streaming analytics 71
Use case overview 63 Use case overview 72
Reference architecture walkthrough 63 Reference architecture walkthrough 72
Best practices to follow during Best practices to follow during
implementation 65 implementation 75
Table of Contents ix
Reference architecture for Use case overview 79
genomics data analytics 76 Reference architecture walkthrough 80
Use case overview 76 Best practices to follow during
Reference architecture walkthrough 76 implementation 83
Best practices to follow during
Summary 83
implementation 78
Test your knowledge 84
Reference architecture for
Further reading 84
log analytics 79
4
Big Data Applications and Notebooks Available in
Amazon EMR
Technical requirements 86 TensorFlow 111
Understanding popular big MXNet 112
data applications in EMR 86
Notebook options available
Hive 87 in EMR 112
Presto 92
EMR Notebooks 113
Spark 94
JupyterHub 115
HBase 98
EMR Studio 118
Hue 106
Zeppelin 120
Ganglia 110
Summary 120
Machine learning frameworks
Test your knowledge 121
available in EMR 111
Further reading 121
Section 2: Configuration, Scaling, Data
Security, and Governance
5
Setting Up and Configuring EMR Clusters
Technical requirements 126 Advanced configuration for
Setting up and configuring cluster hardware and software 129
clusters with the EMR console's Understanding the Software
quick create option 126 Configuration section 130
