You're reading from Serverless Analytics with Amazon Athena

Product typeBook

Published inNov 2021

Reading LevelBeginner

PublisherPackt

ISBN-139781800562349

Edition1st Edition

Languages

Python

Tools

Amazon Athena

Concepts

Data Processing

Authors (3):

Anthony Virtuoso

Mert Turkay Hocanin

Aaron Wishnick

View More author details

Technical requirements

Wherever possible, we will provide samples or instructions to guide you through the setup. However, to complete the activities in this chapter, you will need to ensure you have the following prerequisites available. Our command-line examples will be executed using Ubuntu, but most flavors of Linux should also work without modification.

You will need internet access to GitHub, S3, and the AWS Console.

You will also require a computer with the following installed:

Chrome, Safari, or Microsoft Edge
The AWS CLI

In addition, this chapter requires you to have an AWS account and accompanying IAM user (or role) with sufficient privileges to complete the activities in this chapter. Throughout this book, we will provide detailed IAM policies that attempt to honor the age-old best practice of "least privilege." For simplicity, you can always run through these exercises with a user that has full access, but we recommend that you use scoped-down IAM policies to avoid making costly mistakes and to learn more about how to best use IAM to secure your applications and data. You can find the suggested IAM policy for this chapter in this book's accompanying GitHub repository, listed as chapter_1/iam_policy_chapter_1.json:

https://github.com/PacktPublishing/Serverless-Analytics-with-Amazon-Athena/tree/main/chapter_1

This policy includes the following:

Read and Write access to one S3 bucket using the following actions:
- s3:PutObject: Used to upload data and also for Athena to write query results.
- s3:GetObject: Used by Athena to read data.
- s3:ListBucketMultipartUploads: Used by Athena to write query results.
- s3:AbortMultipartUpload: Used by Athena to write query results.
- s3:ListBucketVersions
- s3:CreateBucket: Used by you if you don't already have a bucket you can use.
- s3:ListBucket: Used by Athena to read data.
- s3:DeleteObject: Used to clean up if you made a mistake or would like to reattempt an exercise from scratch.
- s3:ListMultipartUploadParts: Used by Athena to write a result.
- s3:ListAllMyBuckets: Used by Athena to ensure you own the results bucket.
- s3:ListJobs: Used by Athena to write results.
Read and Write access to one Glue Data Catalog database, using the following actions:
- glue:DeleteDatabase: Used to clean up if you made a mistake or would like to reattempt an exercise from scratch.
- glue:GetPartitions: Used by Athena to query your data in S3.
- glue:UpdateTable: Used when we import our sample data.
- glue:DeleteTable: Used to clean up if you made a mistake or would like to reattempt an exercise from scratch.
- glue:CreatePartition: Used when we import our sample data.
- glue:UpdatePartition: Used when we import our sample data.
- glue:UpdateDatabase: Used when we import our sample data.
- glue:CreateTable: Used when we import our sample data.
- glue:GetTables: Used by Athena to query your data in S3.
- glue:BatchGetPartition: Used by Athena to query your data in S3.
- glue:GetDatabases: Used by Athena to query your data in S3.
- glue:GetTable: Used by Athena to query your data in S3.
- glue:GetDatabase: Used by Athena to query your data in S3.
- glue:GetPartition: Used by Athena to query your data in S3.
- glue:CreateDatabase: Used to create a database if you don't already have one you can use.
- glue:DeletePartition: Used to clean up if you made a mistake or would like to reattempt an exercise from scratch.
Access to run Athena queries.
Important Note
We recommend against using Firefox with the Amazon Athena console as we have found, and reported, a bug associated with switching between certain elements in the UX.

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Serverless Analytics with Amazon Athena

Published in: Nov 2021Publisher: PacktISBN-13: 9781800562349

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Authors (3)

Anthony Virtuoso

Anthony Virtuoso works as a Principal Engineer at Amazon and holds multiple patents in distributed systems, software defined networks, and security. In his eight years at Amazon, he has helped launch several Amazon Web Services, the most recent of which was Amazon Managed Blockchain. As one of the original authors of Athena Query Federation, you'll often find him lurking on the Athena Federation GitHub repository answering questions and shipping bug fixes. When not at work, Anthony obsesses over a different set of customers, namely his wife and two little boys, aged 2 and 5. His kids enjoy doing science experiments with dad, like 3D printing toys, building with Lego, or searching the local pond for tardigrades.
Read more about Anthony Virtuoso

Mert Turkay Hocanin

Mert Turkay Hocanin is a Principal Big Data Architect at Amazon Web Services within the AWS Glue and AWS Lake Formation services and has previously worked for several other services including Amazon Athena, Amazon EMR, Amazon Managed Blockchain. During his time at AWS, he worked with several Fortune 500 companies on some of the largest data lakes in the world and was involved with the launching of three Amazon Web Services. Prior to being a Big Data Architect, he was a Senior Software Developer within Amazon's retail systems organization building one of the earliest data lakes in the company in 2013. When he is not helping customers build data lakes, he enjoys spending time with his wife-Subrina, son-Tristan, and exploring New York City.
Read more about Mert Turkay Hocanin

Aaron Wishnick

Aaron Wishnick works as a Senior Software Engineer at Amazon, where he has been for 7 years. During that time he has worked on Amazon's payment systems, financial intelligence systems, as well as working for AWS on Athena and AWS Proton. When not at work, Aaron and his fiance, Alyssa, are on a quest to determine just how much dog fur is too much, with their husky and malamute, Mina and Wally.
Read more about Aaron Wishnick

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