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Serverless Analytics with Amazon Athena
Serverless Analytics with Amazon Athena

Serverless Analytics with Amazon Athena: Query structured, unstructured, or semi-structured data in seconds without setting up any infrastructure

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Serverless Analytics with Amazon Athena

Chapter 1: Your First Query

This chapter is all about introducing you to the serverless analytics experience offered by Amazon Athena. Data is one of the most valuable assets you and your company generate. In recent years, we have seen a revolution in data retention, where companies are capturing all manner of data that was once ignored. Everything from logs to clickstream data, to support tickets are now routinely kept for years. Interestingly, the data itself is not what is valuable. Instead, the insights that are buried in that mountain of data are what we are after. Certainly, increased awareness and retention have made the information we need to power our businesses, applications, and decisions more available but the explosion in data sizes has made the insights we seek less accessible. What could once fit nicely in a traditional RDBMS, such as Oracle, now requires a distributed filesystem such as HDFS and an accompanying Massively Parallel Processing (MPP) engine such as Spark to run even the most basic of queries in a timely fashion.

Enter Amazon Athena. Unlike traditional analytics engines, Amazon Athena is a fully managed offering. You will never have to set up any servers or tune cryptic settings to get your queries running. This allows you to focus on what is most important: using data to generating insights that drive your business. You can just focus on getting the most out of your data. This ease of use is precisely why this first chapter is all about getting hands-on and running your first query. Whether you are a seasoned analytics veteran or a newcomer to the space, this chapter will give you the knowledge you need to be running your first Athena query in less than 30 minutes. For now, we will simplify things to demonstrate why so many people choose Amazon Athena for their workloads. This will help establish your mental model for the deeper discussions, features, and examples of later sections.

In this chapter, we will cover the following topics:

  • What is Amazon Athena?
  • Obtaining and preparing sample data
  • Running your first query

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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Key benefits

  • Explore the promising capabilities of Amazon Athena and Athena’s Query Federation SDK
  • Use Athena to prepare data for common machine learning activities
  • Cover best practices for setting up connectivity between your application and Athena and security considerations

Description

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using SQL, without needing to manage any infrastructure. This book begins with an overview of the serverless analytics experience offered by Athena and teaches you how to build and tune an S3 Data Lake using Athena, including how to structure your tables using open-source file formats like Parquet. You’ll learn how to build, secure, and connect to a data lake with Athena and Lake Formation. Next, you’ll cover key tasks such as ad hoc data analysis, working with ETL pipelines, monitoring and alerting KPI breaches using CloudWatch Metrics, running customizable connectors with AWS Lambda, and more. Moving on, you’ll work through easy integrations, troubleshooting and tuning common Athena issues, and the most common reasons for query failure. You will also review tips to help diagnose and correct failing queries in your pursuit of operational excellence. Finally, you’ll explore advanced concepts such as Athena Query Federation and Athena ML to generate powerful insights without needing to touch a single server. By the end of this book, you’ll be able to build and use a data lake with Amazon Athena to add data-driven features to your app and perform the kind of ad hoc data analysis that often precedes many of today’s ML modeling exercises.

Who is this book for?

Business intelligence (BI) analysts, application developers, and system administrators who are looking to generate insights from an ever-growing sea of data while controlling costs and limiting operational burden, will find this book helpful. Basic SQL knowledge is expected to make the most out of this book.

What you will learn

  • Secure and manage the cost of querying your data
  • Use Athena ML and User Defined Functions (UDFs) to add advanced features to your reports
  • Write your own Athena Connector to integrate with a custom data source
  • Discover your datasets on S3 using AWS Glue Crawlers
  • Integrate Amazon Athena into your applications
  • Setup Identity and Access Management (IAM) policies to limit access to tables and databases in Glue Data Catalog
  • Add an Amazon SageMaker Notebook to your Athena queries
  • Get to grips with using Athena for ETL pipelines
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Table of Contents

19 Chapters
Section 1: Fundamentals Of Amazon Athena Chevron down icon Chevron up icon
Chapter 1: Your First Query Chevron down icon Chevron up icon
Chapter 2: Introduction to Amazon Athena Chevron down icon Chevron up icon
Chapter 3: Key Features, Query Types, and Functions Chevron down icon Chevron up icon
Section 2: Building and Connecting to Your Data Lake Chevron down icon Chevron up icon
Chapter 4: Metastores, Data Sources, and Data Lakes Chevron down icon Chevron up icon
Chapter 5: Securing Your Data Chevron down icon Chevron up icon
Chapter 6: AWS Glue and AWS Lake Formation Chevron down icon Chevron up icon
Section 3: Using Amazon Athena Chevron down icon Chevron up icon
Chapter 7: Ad Hoc Analytics Chevron down icon Chevron up icon
Chapter 8: Querying Unstructured and Semi-Structured Data Chevron down icon Chevron up icon
Chapter 9: Serverless ETL Pipelines Chevron down icon Chevron up icon
Chapter 10: Building Applications with Amazon Athena Chevron down icon Chevron up icon
Chapter 11: Operational Excellence – Monitoring, Optimization, and Troubleshooting Chevron down icon Chevron up icon
Section 4: Advanced Topics Chevron down icon Chevron up icon
Chapter 12: Athena Query Federation Chevron down icon Chevron up icon
Chapter 13: Athena UDFs and ML Chevron down icon Chevron up icon
Chapter 14: Lake Formation – Advanced Topics Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Full star icon Half star icon 4.9
(9 Ratings)
5 star 88.9%
4 star 11.1%
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1 star 0%
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BeeDee May 14, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
There are plenty of useful topics and content within this book. It goes through exploring data from data lakes, data warehouses, and other types of transactional and non transactional data. It provides Amazon Athena serverless analytic methods to produce SQL queries to your structured and un-structured data within your current BI tools. This book can be used as a technical reference to gain valuable insight into your data by using Athena as your main source to truth.
Amazon Verified review Amazon
Jai Chhugani Apr 16, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great reference on how to use Athena to do complex analytics on the cheap.Goes well beyond the reference docs to show how to get things done.Awesome writing that gets into the inside history of how features were built with example code to illustrate usage.
Amazon Verified review Amazon
Karthik K May 25, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I really liked that the book gives a simple and concise intro to the topics before diving deep into the technologies.. I am a big fan of cloud-native, managed services and serverless tech, prefer minimal devops.. and find this book catering to my taste. If you are building data analytics, ETL, ML apps on top of AWS native cloud, then this book could be a good reference
Amazon Verified review Amazon
Nivas Shankar Jan 06, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I was recommended to this book by a friend of mine. I already had some background on Athena but I still got a lot of value of this book. It was a good blend of on-hands exercises and technical material which kept it interesting throughout the book. The tone of the book differed from chapter to chapter, probably because there are multiple authors. Overall, I would highly recommend this.
Amazon Verified review Amazon
AmzeReviewer Dec 13, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very well written book. Covers all the topics required to make effective use of AWS service.
Amazon Verified review Amazon
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