Migrate to Amazon Redshift

Data is an invaluable resource in today’s world and it is growing in volume and complexity faster than ever before. Traditional data warehousing systems cannot keep up. With rigid architectures that require significant investment to maintain, update, and secure, they do not give organizations the opportunity to make the most of their data.

Moving to a cloud data warehouse like Amazon Redshift liberates your analytics from these limitations. You can run queries across petabytes of data in your data warehouse and extend into your data lake, what we call a “lake house” approach.

With Amazon Redshift, you can set up a cloud data warehouse in minutes, start small for just $0.25 per hour, and scale to over a petabyte of compressed data for under $1,000/TB/Year - less than one-tenth the cost of other solutions.

Introduction to Data Warehousing on AWS with Amazon Redshift (1:35)

Why migrate?

Set up, deploy, and manage with ease

Amazon Redshift is fully managed and simple to use, enabling you to deploy a new data warehouse in minutes and load virtually any type of data from a range of cloud or on-premises data sources. It automates most of the common administrative tasks to manage, monitor, and scale your data warehouse, so you can spend your time on more productive, needle-moving endeavors. It delivers fast query performance, improves I/O efficiency, and scales up or down as your performance and capacity needs change.

Get scale and flexibility

With your data warehouse on AWS, you can go beyond data loaded on to local disks and query vast amounts of unstructured data in your Amazon S3 data lake - without having to load or transform any data. You can query data in open formats, including CSV, TSV, Parquet, Sequence, and RCFile. Having your data available in open formats in your data lake ultimately gives you the flexibility to use the right engine for different analytical needs.

Operate at the lowest cost

Start small at $0.25 per hour and scale up to petabytes for under $1,000 per terabyte per year. Pay for compute and storage separately and only pay for the storage you use. Use pause and resume for clusters not in use and know how much you'll spend with predictable monthly costs. Amazon Redshift is at least 50% less expensive than all other cloud data warehouses and less than one-tenth the cost of most on-premises systems.

Perform the fastest queries

Redshift is the world’s fastest cloud data warehouse and gets faster every year, so you can derive insights from your data even more quickly. For performance intensive workloads, you can use the new RA3 instances to get up to 3x the performance of any cloud data warehouse.

Customer Success: OLX Group (2:27)

Common migrations

Netezza to Redshift

Nielsen

Nielsen was running a legacy on-premise data warehouse based on Netezza until the company built a new, data-lake-based architecture with Amazon Redshift.

Learn how Nielsen migrated to Redshift »

Oracle to Redshift

Amazon.com

Amazon.com’s on-premises Oracle infrastructure was hard to maintain and operate, so the company decided to switch to Amazon Redshift.

Learn how Amazon.com migrated to Redshift »

Teradata to Redshift

Equinox

Equinox Fitness migrated to Redshift from its Teradata on-premises data warehouse to reduce time-to-insight and control infrastructure costs.

Learn how Equinox Fitness migrated to Redshift »

SQL Server to Redshift

Intuit

Intuit moved its mission-critical workloads to Amazon Redshift from its on-premises environment to speed development, innovate faster, and better solve customers’ needs.

Learn how Intuit migrated to Redshift »

Migration tools

AWS Database Migration Service (DMS)

AWS Database Migration Service (DMS) is a self-service tool you can use to migrate your data from the most widely used commercial data warehouses to Amazon Redshift. The source database remains fully operational during the migration, minimizing downtime to applications that rely on the database. When migrating databases to Amazon Redshift, you can use DMS free for six months.

Learn more »

Schema Conversion Tool (SCT)

Automatically convert the source data warehouse schema and a majority of the data warehouse code objects.

Learn more »
 

AWS Professional Services

AWS Professional Services is a global team of experts that can help you realize your desired business outcomes when using the AWS Cloud. They work together with your team and your chosen member of the AWS Partner Network (APN) to execute your enterprise cloud computing initiatives.

Learn more »

AWS Data Modernization Partners

Many Amazon Redshift Partners have specific offerings, skills, tools, processes, and references to help you de-risk your migration from legacy on-premises data warehouses to AWS.

Agilisium

Agilisium offers Amazon Redshift competency and cloud-based analytics for the media and entertainment industries.

Capgemini

Capgemini’s end-to-end migration approach helps clients migrate existing applications and benefit from the agility, speed, and pay-as-you-go model of the AWS Cloud.

SoftServe

SoftServe's specialized team of big data engineers, architects, and data scientists provides services across the entire spectrum of big data and analytics projects.

TEKsystems

Amazon Redshift thought leadership, solution design, and implementation are delivered via TEKsystems data (EAA) practice.

Virtusa

Virtusa's Data and Analytics practice provides end-to-end capabilities for modernizing data platforms whilst driving AI/ML outcomes.

Resources

Recent AWS re:Invent and Summit Sessions on Redshift Migrations

AWS re:Invent 2019: Migrate your data warehouse to the cloud, featuring Nielsen (50:39)
AWS re:Invent 2019: Migrate your data warehouse to the cloud, ft. Fannie Mae (59:08)
AWS re:Invent 2019: Migrating your data warehouses to Amazon Redshift (1:06:21)
How Equinox Fitness Clubs Migrated to a Modern Data Warehouse (57:25)

Recent AWS Online Tech Talks on Redshift Migrations

Practical Tips for Migrating Your IBM Netezza Data Warehouse to the Cloud (23:23)
Accelerate Your Analytics by Migrating to a Cloud Data Warehouse (35:03)
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