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

Authors (3):

Shruti Worlikar

Thiyagarajan Arumugam

Harshida Patel

View More author details

Chapter 4: Data Pipelines

Companies build modern cloud-based data warehouses to either migrate from their on-premises data warehouses or to build new workloads. To hydrate data in these modern data warehouses, users can build data pipelines based on the source data. In this chapter, we will cover the different types of data pipelines that we can design on Amazon Web Services (AWS) with Amazon Redshift as a destination data warehouse.

The following recipes are discussed in this chapter:

Ingesting data from transactional sources using AWS Database Migration Service (AWS DMS)
Streaming data to Amazon Redshift via Amazon Kinesis Firehose
Cataloging and ingesting data using AWS Glue

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 section. 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/Chapter04/chapter_4_CFN.yaml) to create two IAM policies:
a. An IAM policy attached to the IAM user that will give the user access to Amazon Redshift, Amazon Relational Database Service (Amazon RDS), Amazon Kinesis, Amazon Kinesis Data Firehose...

Ingesting data from transactional sources using AWS DMS

When you have transactional data sources—either on-premises or on AWS RDS—and you want to replicate or migrate that data to your data warehouse in Amazon Redshift for consolidation or reporting, you can use AWS DMS. AWS DMS is a fully managed service that helps you to do full loading from your transactional source to the target data warehouse as well as near-real-time change data capture (CDC) from source to target.

In this recipe, we will do full replication of the parts table from Amazon RDS MySQL, serving as a transactional source to the Amazon Redshift database warehouse.

Getting ready

To complete this recipe, you will need the following:

An Amazon Redshift cluster deployed in the eu-west-1 AWS region.
Amazon Redshift cluster master user credentials.
An IAM user with access to Amazon Redshift, Amazon RDS, and AWS DMS.
An Amazon RDS MySQL cluster deployed in the eu-west-1 AWS region...

Streaming data to Amazon Redshift via Amazon Kinesis Firehose

Streaming datasets are continuous datasets that can originate from sources such as internet of things (IoT) devices, log files, gaming systems, and so on. Ingesting streamed data into Amazon Redshift allows the running of near-real-time analytics that can be combined with the historical/operational data to produce actionable reporting—for example, in a manufacturing shop, analyzing the data from several IoT sensors can help predict the failure of machinery and enable you to take preventive action.

In this recipe, we will simulate a streaming dataset using the www.amazon.com product review data to be ingested into Amazon Redshift using Amazon Kinesis Firehose. Amazon Kinesis Firehose provides out-of-the-box integration to capture the streaming dataset and land it into an Amazon Redshift table.

Getting ready

To complete this recipe, you will need the following:

An Amazon Redshift cluster deployed in...

Cataloging and ingesting data using AWS Glue

Data that is staged in Amazon S3 can be cataloged using the AWS Glue service. Cataloging the data allows metadata to be attached and the AWS Glue Data Catalog to be populated. This process enriches the raw data, which can be queried as tables using many of the AWS analytical services—such as Amazon Redshift, Amazon Elastic MapReduce (Amazon EMR), and so on—for analytical processing. It is easy to perform this data discovery using the AWS Glue crawlers that can create and update the metadata automatically.

In this recipe, we will enrich the data to catalog and enable ingestion into Amazon Redshift.Getting ready

To complete this recipe, you will need the following:

An Amazon Redshift cluster deployed in the eu-west-1 AWS region
Amazon Redshift cluster master user credentials
An IAM user with access to Amazon Redshift, Amazon S3, and AWS Glue
An IAM role attached to an Amazon Redshift cluster that can...

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Authors (3)

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 (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 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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