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You're reading from  Serverless ETL and Analytics with AWS Glue

Product typeBook
Published inAug 2022
Reading LevelExpert
PublisherPackt
ISBN-139781800564985
Edition1st Edition
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Authors (6):
Vishal Pathak
Vishal Pathak
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Vishal Pathak

Vishal Pathak is a Data Lab Solutions Architect at AWS. Vishal works with customers on their use cases, architects solutions to solve their business problems, and helps them build scalable prototypes. Prior to his journey in AWS, Vishal helped customers implement business intelligence, data warehouse, and data lake projects in the US and Australia.
Read more about Vishal Pathak

Subramanya Vajiraya
Subramanya Vajiraya
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Subramanya Vajiraya

Subramanya Vajiraya is a Big data Cloud Engineer at AWS Sydney specializing in AWS Glue. He obtained his Bachelor of Engineering degree specializing in Information Science & Engineering from NMAM Institute of Technology, Nitte, KA, India (Visvesvaraya Technological University, Belgaum) in 2015 and obtained his Master of Information Technology degree specialized in Internetworking from the University of New South Wales, Sydney, Australia in 2017. He is passionate about helping customers solve challenging technical issues related to their ETL workload and implementing scalable data integration and analytics pipelines on AWS.
Read more about Subramanya Vajiraya

Noritaka Sekiyama
Noritaka Sekiyama
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Noritaka Sekiyama

Noritaka Sekiyama is a Senior Big Data Architect on the AWS Glue and AWS Lake Formation team. He has 11 years of experience working in the software industry. Based in Tokyo, Japan, he is responsible for implementing software artifacts, building libraries, troubleshooting complex issues and helping guide customer architectures
Read more about Noritaka Sekiyama

Tomohiro Tanaka
Tomohiro Tanaka
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Tomohiro Tanaka

Tomohiro Tanaka is a senior cloud support engineer at AWS. He works to help customers solve their issues and build data lakes across AWS Glue, AWS IoT, and big data technologies such Apache Spark, Hadoop, and Iceberg.
Read more about Tomohiro Tanaka

Albert Quiroga
Albert Quiroga
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Albert Quiroga

Albert Quiroga works as a senior solutions architect at Amazon, where he is helping to design and architect one of the largest data lakes in the world. Prior to that, he spent four years working at AWS, where he specialized in big data technologies such as EMR and Athena, and where he became an expert on AWS Glue. Albert has worked with several Fortune 500 companies on some of the largest data lakes in the world and has helped to launch and develop features for several AWS services.
Read more about Albert Quiroga

Ishan Gaur
Ishan Gaur
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Ishan Gaur

Ishan Gaur has more than 13 years of IT experience in soft ware development and data engineering, building distributed systems and highly scalable ETL pipelines using Apache Spark, Scala, and various ETL tools such as Ab Initio and Datastage. He currently works at AWS as a senior big data cloud engineer and is an SME of AWS Glue. He is responsible for helping customers to build out large, scalable distributed systems and implement them in AWS cloud environments using various big data services, including EMR, Glue, and Athena, as well as other technologies, such as Apache Spark, Hadoop, and Hive.
Read more about Ishan Gaur

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Summary

In this chapter, we discussed the fundamental concepts and importance of data preparation within a data integration workflow. We explored how we can prepare data in AWS Glue using both visual interfaces and source code.

We explored different features of AWS Glue DataBrew and saw how we can implement profile jobs to profile the data and gather insights about the dataset being processed, as well as how to use a DQ Ruleset to enrich the data profile, use PII detection and redaction, and perform column encryption using deterministic and probabilistic encryption. We also discussed how we can apply transformations, build a recipe using those transformations, create a job using that recipe, and run the job.

Then, we discussed source code-based ETL development using AWS Glue ETL jobs and the different features of AWS Glue Studio before exploring some of the popular transformations and extensions available in AWS Glue ETL. We saw how these transformations can be used in specific...

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Serverless ETL and Analytics with AWS Glue
Published in: Aug 2022Publisher: PacktISBN-13: 9781800564985

Authors (6)

author image
Vishal Pathak

Vishal Pathak is a Data Lab Solutions Architect at AWS. Vishal works with customers on their use cases, architects solutions to solve their business problems, and helps them build scalable prototypes. Prior to his journey in AWS, Vishal helped customers implement business intelligence, data warehouse, and data lake projects in the US and Australia.
Read more about Vishal Pathak

author image
Subramanya Vajiraya

Subramanya Vajiraya is a Big data Cloud Engineer at AWS Sydney specializing in AWS Glue. He obtained his Bachelor of Engineering degree specializing in Information Science & Engineering from NMAM Institute of Technology, Nitte, KA, India (Visvesvaraya Technological University, Belgaum) in 2015 and obtained his Master of Information Technology degree specialized in Internetworking from the University of New South Wales, Sydney, Australia in 2017. He is passionate about helping customers solve challenging technical issues related to their ETL workload and implementing scalable data integration and analytics pipelines on AWS.
Read more about Subramanya Vajiraya

author image
Noritaka Sekiyama

Noritaka Sekiyama is a Senior Big Data Architect on the AWS Glue and AWS Lake Formation team. He has 11 years of experience working in the software industry. Based in Tokyo, Japan, he is responsible for implementing software artifacts, building libraries, troubleshooting complex issues and helping guide customer architectures
Read more about Noritaka Sekiyama

author image
Tomohiro Tanaka

Tomohiro Tanaka is a senior cloud support engineer at AWS. He works to help customers solve their issues and build data lakes across AWS Glue, AWS IoT, and big data technologies such Apache Spark, Hadoop, and Iceberg.
Read more about Tomohiro Tanaka

author image
Albert Quiroga

Albert Quiroga works as a senior solutions architect at Amazon, where he is helping to design and architect one of the largest data lakes in the world. Prior to that, he spent four years working at AWS, where he specialized in big data technologies such as EMR and Athena, and where he became an expert on AWS Glue. Albert has worked with several Fortune 500 companies on some of the largest data lakes in the world and has helped to launch and develop features for several AWS services.
Read more about Albert Quiroga

author image
Ishan Gaur

Ishan Gaur has more than 13 years of IT experience in soft ware development and data engineering, building distributed systems and highly scalable ETL pipelines using Apache Spark, Scala, and various ETL tools such as Ab Initio and Datastage. He currently works at AWS as a senior big data cloud engineer and is an SME of AWS Glue. He is responsible for helping customers to build out large, scalable distributed systems and implement them in AWS cloud environments using various big data services, including EMR, Glue, and Athena, as well as other technologies, such as Apache Spark, Hadoop, and Hive.
Read more about Ishan Gaur