![]() ![]() For disk storage, we use 1TB of 500 IOPS Provisioned SSD because intermediate results are stored on disk. The size of each instance is 8 vCPU, 32 GB memory, and up to 10 Gb network capacity. You can install the data extraction agents on on-premises VM instances running Linux with root administration privileges. You should configure several AWS SCT data extraction agents to match the amount of data to be concurrently transferred and the number of Netezza connections available. Choosing Microsoft Windows as the operating system allows your users to graphically control the creation of projects, modify profiles, start and view the progress of the conversions, and view the output of the migration assessment reports.īecause you don’t perform the data migration directly on the AWS SCT console, a general purpose EC2 instance with 4 vCPU, 16 GB memory, 100 GB storage, and moderate network bandwidth is sufficient. The AWS SCT is installed on an EC2 instance running Microsoft Windows 10 with administrator privileges. Configuring AWS SCT for the Netezza source environment Using Direct Connect also adds flexibility in case extract jobs need to be re-run. You can establish private connectivity between your AWS account and your data center, office, or co-location environment by using Direct Connect, which in many cases can reduce your network costs, increase bandwidth throughput, and provide a more consistent network experience than internet-based connections. AWS Direct Connect is a cloud service solution that makes it easy to establish a dedicated network connection from your premises to an AWS account. ![]() AWS Snowball is a petabyte-scale offline solution for moving large amounts of data into the AWS account where sufficient bandwidth of a direct connection isn’t available. AWS strongly recommends installing them on premises within the same subnet as the Netezza data warehouse.ĭuring the transfer of data from the on-premises data center to the AWS account, you can use either a direct connection or offline storage. The AWS SCT data extraction agents are installed and run as close to the Netezza data warehouse as possible.The AWS SCT is installed within the AWS account onto an Amazon Elastic Compute Cloud (Amazon EC2) instance to facilitate migration operations, orchestrate the AWS SCT data extraction agents, and provide access via a user-friendly console.The migration should ensure the following: The following diagram illustrates this architecture. The migration strategy uses the AWS SCT to accelerate schema object conversion and migrate the data from the Netezza database to the Amazon Redshift cluster. Make sure it’s clear which data warehouses are in scope for the migration. Some Netezza source systems contain two Netezza data warehouses, for example one for ETL loading throughout the day and one for end-user reporting users. For each table identified, record the number of rows and size in GB. This information forms a migration runbook that is updated during the migration to document the progress of data migration from Netezza to Amazon Redshift. To plan and keep track of the migration tasks, you should produce a tracker of all the Netezza databases, tables, and views in scope. Business validation (including optional dual-running).Migrate to other pre-production environments.Configure AWS SCT for Netezza source environments.Record objects to be migrated into a migration runbook.It details the different environments migrated to and the tasks, tools, and scripts used to complete the work: The following plan is a real-world use case from a large European Enterprise customer. It’s important to build a migration plan unique to your organization’s processes and non-functional requirements. We also walk you through validating that the schema and data content were migrated as expected and followed Amazon Redshift best practices. In this post, we explain how a large European Enterprise customer implemented a Netezza migration strategy spanning multiple environments, using the AWS Schema Conversion Tool (AWS SCT) to accelerate schema and data migration. The post How to migrate a large data warehouse from IBM Netezza to Amazon Redshift with no downtime described a high-level strategy to move from an on-premises Netezza data warehouse to Amazon Redshift. ![]()
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