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You're reading from  Amazon Redshift Cookbook

Product typeBook
Published inJul 2021
Reading LevelBeginner
PublisherPackt
ISBN-139781800569683
Edition1st Edition
Languages
Python
Tools
Amazon Redshift
Concepts
Data Analysis
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Authors (3):
Shruti Worlikar
Shruti Worlikar
author image
Shruti Worlikar

Shruti Worlikar is a cloud professional with technical expertise in data lakes and analytics across cloud platforms. Her background has led her to become an expert in on-premises-to-cloud migrations and building cloud-based scalable analytics applications. Shruti earned her bachelor's degree in electronics and telecommunications from Mumbai University in 2009 and later earned her masters' degree in telecommunications and network management from Syracuse University in 2011. Her work history includes work at J.P. Morgan Chase, MicroStrategy, and Amazon Web Services (AWS). She is currently working in the role of Manager, Analytics Specialist SA at AWS, helping customers to solve real-world analytics business challenges with cloud solutions and working with service teams to deliver real value. Shruti is the DC Chapter Director for the non-profit Women in Big Data (WiBD) and engages with chapter members to build technical and business skills to support their career advancements. Originally from Mumbai, India, Shruti currently resides in Aldie, VA, with her husband and two kids.
Read more about Shruti Worlikar

Thiyagarajan Arumugam
Thiyagarajan Arumugam
author image
Thiyagarajan Arumugam

Thiyagarajan Arumugam (Thiyagu) is a principal big data solution architect at AWS, architecting and building solutions at scale using big data to enable data-driven decisions. Prior to AWS, Thiyagu as a data engineer built big data solutions at Amazon, operating some of the largest data warehouses and migrating to and managing them. He has worked on automated data pipelines and built data lake-based platforms to manage data at scale for the customers of his data science and business analyst teams. Thiyagu is a certified AWS Solution Architect (Professional), earned his master's degree in mechanical engineering at the Indian Institute of Technology, Delhi, and is the author of several blog posts at AWS on big data. Thiyagu enjoys everything outdoors – running, cycling, ultimate frisbee – and is currently learning to play the Indian classical drum the mrudangam. Thiyagu currently resides in Austin, TX, with his wife and two kids.
Read more about Thiyagarajan Arumugam

Harshida Patel
Harshida Patel
author image
Harshida Patel

Harshida Patel is a senior analytics specialist solution architect at AWS, enabling customers to build scalable data lake and data warehousing applications using AWS analytical services. She has presented Amazon Redshift deep-dive sessions at re:Invent. Harshida has a bachelor's degree in electronics engineering and a master's in electrical and telecommunication engineering. She has over 15 years of experience architecting and building end-to-end data pipelines in the data management space. In the past, Harshida has worked in the insurance and telecommunication industries. She enjoys traveling and spending quality time with friends and family, and she lives in Virginia with her husband and son.
Read more about Harshida Patel

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Chapter 9: Lake House Architecture

The lake house is an architectural pattern that makes data easily accessible across customers' analytics solutions, thereby preventing data silos. Amazon Redshift is the backbone of the lake house architecture—it allows enterprise customers to query data across data lakes, operational databases, and multiple data warehouses to build an analytics solution without having to move data in and out of these different systems. In this chapter, you will learn how you can leverage the lake house architecture to extend the data warehouse to services outside Amazon Redshift to build your solution, while taking advantage of the built-in integration. For example, you can use the Federated Query capability to join the operational data in your relational systems to historical data in Amazon Redshift to analyze a promotional trend.

The following recipes are discussed in this chapter:

  • Building a data lake catalog using Amazon Web Services (AWS...

Technical requirements

Here are the technical requirements in order to complete the recipes in this chapter:

  • Access to the AWS Management Console.
  • AWS administrators should create an Identity and Access Management (IAM) user by following Recipe 1 – Creating an IAM user in the Appendix. This IAM user will be deployed to perform some of the recipes in this chapter.
  • AWS administrators should create an IAM role by following Recipe 3 – Creating an IAM Role for an AWS service in the Appendix. This IAM role will be deployed to perform some of the recipes in this chapter.
  • AWS administrators should deploy the AWS CloudFormation template (https://github.com/PacktPublishing/Amazon-Redshift-Cookbook/blob/master/Chapter09/chapter_9_CFN.yaml) to create two IAM policies:

    a. An IAM policy attached to the IAM user that will give them access to Amazon Redshift, Amazon Elastic Compute Cloud (Amazon EC2), Amazon Simple Storage Service (Amazon S3), Amazon Simple Notification...

Building a data lake catalog using AWS Lake Formation

The data lake design pattern has been widely adopted in the industry. Data lakes help to break data silos by allowing you to store all of your data in a single, unified place. You can collect the data from different sources and data can arrive at different frequencies—for example, clickstream data. The data format can be structured, unstructured, or semi-structured. Analyzing a unified view of the data allows you to derive more value and helps to derive more insight from the data to drive business value.

Your data lake should be secure and should meet your compliance requirements, with a centralized catalog that allows you to search and easily find data that is stored in the lake. One of the advantages of data lakes is that you can run a variety of analytical tools against them. You may also want to do new types of analysis on your data. For example, you may want to move from answering questions on what happened in the...

Exporting a data lake from Amazon Redshift

Amazon Redshift empowers a lake house architecture, allowing you to query data within the data warehouse and data lake using Amazon Redshift Spectrum and also to export your data back to the data lake on Amazon S3, to be used by other analytical and ML services. You can store data in open file formats in your Amazon S3 data lake when performing the data lake export to integrate with your existing data lake formats.

Getting ready

To complete this recipe, you will need the following to be set up:

  • An IAM user with access to Amazon Redshift
  • An Amazon Redshift cluster deployed in the eu-west-1 AWS Region with the retail dataset created from Chapter 3, Loading and Unloading Data, using the Loading data from Amazon S3 using COPY recipe
  • Amazon Redshift cluster masteruser credentials
  • Access to any SQL interface such as a SQL client or the Amazon Redshift Query Editor
  • An AWS account number—we will refer to this in...

Extending a data warehouse using Amazon Redshift Spectrum

Amazon Redshift Spectrum allows Amazon Redshift customers to query data directly from an Amazon S3 data lake. This allows us to combine data warehouse data with data lake data, which makes use of open source file formats such as Parquet, comma-separated values (CSV), Sequence, Avro, and so on. Amazon Redshift Spectrum is a serverless solution, so customers don't have to provision or manage it. It allows customers to perform unified analytics on data in an Amazon Redshift cluster and data in an Amazon S3 data lake, and easily create insights from disparate datasets.

Getting ready

To complete this recipe, you will need the following to be set up:

  • An IAM user with access to Amazon Redshift
  • An Amazon Redshift cluster deployed in the eu-west-1 AWS Region with the retail dataset created from Chapter 3, Loading and Unloading Data, using the Loading data from Amazon S3 using COPY recipe
  • Amazon Redshift cluster...

Data sharing across multiple Amazon Redshift clusters

Amazon Redshift RA3 clusters decouple storage and compute, and provide the ability to scale either of them independently. The decoupled storage allows for data to be read by different consumer clusters that allow workload isolation. The data producer cluster controls access to the data that is shared. This feature opens up the possibility to set up a flexible multi-tenant system—for example, within an organization, data produced by a business unit can be shared with any of the different teams such as marketing, finance, data science, and so on that can be independently consumed using their own Amazon Redshift clusters.

Getting ready

To complete this recipe, you will need the following:

  • An IAM user with access to Amazon Redshift
  • Two separate two-node Amazon Redshift ra3.xlplus clusters deployed in the eu-west-1 AWS Region:

    a. The first cluster should be deployed with the retail sample dataset from Chapter...

Querying operational sources using Federated Query

Amazon Redshift Federated Query enables unified analytics across databases, data warehouses, and data lakes. With the Federated Query feature in Amazon Redshift, you can query live data across from Amazon RDS and Aurora PostgreSQL databases. For example, you might have an up-to-date customer address data that you might want to join with historical order data to enrich your reports—this can be easily joined up using the Federated Query feature.

Getting ready

To complete this recipe, you will need the following:

  • An IAM user with access to Amazon Redshift, AWS Secrets Manager, and Amazon RDS.
  • An Amazon Redshift cluster deployed in the eu-west-1 AWS Region with the retail sample dataset from Chapter 3, Loading and Unloading Data.
  • An Amazon Aurora serverless PostgreSQL database. Create an RDS PostgreSQL cluster (see https://aws.amazon.com/getting-started/hands-on/building-serverless-applications-with-amazon...
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Published in: Jul 2021Publisher: PacktISBN-13: 9781800569683
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Authors (3)

author image
Shruti Worlikar

Shruti Worlikar is a cloud professional with technical expertise in data lakes and analytics across cloud platforms. Her background has led her to become an expert in on-premises-to-cloud migrations and building cloud-based scalable analytics applications. Shruti earned her bachelor's degree in electronics and telecommunications from Mumbai University in 2009 and later earned her masters' degree in telecommunications and network management from Syracuse University in 2011. Her work history includes work at J.P. Morgan Chase, MicroStrategy, and Amazon Web Services (AWS). She is currently working in the role of Manager, Analytics Specialist SA at AWS, helping customers to solve real-world analytics business challenges with cloud solutions and working with service teams to deliver real value. Shruti is the DC Chapter Director for the non-profit Women in Big Data (WiBD) and engages with chapter members to build technical and business skills to support their career advancements. Originally from Mumbai, India, Shruti currently resides in Aldie, VA, with her husband and two kids.
Read more about Shruti Worlikar

author image
Thiyagarajan Arumugam

Thiyagarajan Arumugam (Thiyagu) is a principal big data solution architect at AWS, architecting and building solutions at scale using big data to enable data-driven decisions. Prior to AWS, Thiyagu as a data engineer built big data solutions at Amazon, operating some of the largest data warehouses and migrating to and managing them. He has worked on automated data pipelines and built data lake-based platforms to manage data at scale for the customers of his data science and business analyst teams. Thiyagu is a certified AWS Solution Architect (Professional), earned his master's degree in mechanical engineering at the Indian Institute of Technology, Delhi, and is the author of several blog posts at AWS on big data. Thiyagu enjoys everything outdoors – running, cycling, ultimate frisbee – and is currently learning to play the Indian classical drum the mrudangam. Thiyagu currently resides in Austin, TX, with his wife and two kids.
Read more about Thiyagarajan Arumugam

author image
Harshida Patel

Harshida Patel is a senior analytics specialist solution architect at AWS, enabling customers to build scalable data lake and data warehousing applications using AWS analytical services. She has presented Amazon Redshift deep-dive sessions at re:Invent. Harshida has a bachelor's degree in electronics engineering and a master's in electrical and telecommunication engineering. She has over 15 years of experience architecting and building end-to-end data pipelines in the data management space. In the past, Harshida has worked in the insurance and telecommunication industries. She enjoys traveling and spending quality time with friends and family, and she lives in Virginia with her husband and son.
Read more about Harshida Patel

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