For many analytics workloads, a column store can have dramatic performance benefits over a row store. Netezza stores each row of data onto disk in data blocks, whereas Amazon Redshift stores each column of data. row store, concurrency scaling, and data lake integration. There are three important differences that could have significant impact on your data and application architecture when migrating from Netezza to Amazon Redshift: column store vs. Differencesįor all the similarities that Amazon Redshift and Netezza share, they also have differences. Applications designed to employ these FPGA features on Netezza (for example, queries that rely heavily on certain aggregate functions and data filtering) translate well to Amazon Redshift clusters using AQUA. Netezza uses FPGAs to perform simple compute tasks before data reaches the CPU. When it’s released, AQUA will accelerate queries up to 10 times faster.
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Compression, encryption, filtering, aggregation-and other tasks-can now happen in this intermediary layer in between the storage and compute, which leaves the CPU free to handle more complex tasks. AQUA uses AWS-designed processors to bring certain compute tasks closer to the storage layer. Amazon Redshift AQUA and Netezza’s FPGAĪWS recently announced the new Amazon Redshift feature AQUA, which is in preview as of this writing.
For more information, see Amazon Redshift Engineering’s Advanced Table Design Playbook: Preamble, Prerequisites, and Prioritization. This means that you can apply similar architectural strategies from Netezza, such as zone maps and distribution styles, to Amazon Redshift. Each worker node performs its task in parallel and returns the results to the leader node, where the results are aggregated and returned to the user. This means that a query is sent to a leader node, which then compiles a set of commands that it sends to multiple compute nodes. MPP architectureīoth Netezza and Amazon Redshift are MPP databases. For example, both Netezza and Amazon Redshift don’t enforce primary keys to improve performance, though you can still define primary keys on your tables to help the optimizer create better query plans. And because both databases are built for analytics and not transactional workloads, there are similar characteristics between the two databases. You can also use the AWS Schema Conversion Tool, which can automatically migrate a large percentage of Netezza storage procedures to Amazon Redshift syntax with zero user effort. Your Netezza stored procedures can translate to Amazon Redshift with little-to-no rewriting of code. In particular, both support many features of PL/pgSQL, Postgres’s procedural language. This means that Netezza SQL and Amazon Redshift SQL have a similar syntax. Postgres compatibilityīoth Netezza and Amazon Redshift share some compatibility with Postgres, an open-source database. Three significant similarities between Netezza and Amazon Redshift are their compatibility with Postgres, their massively parallel processing (MPP) architecture, and the Amazon Redshift feature Advanced Query Accelerator (AQUA) compared to Netezza’s use of FPGAs. This post discusses important similarities and differences between Netezza and Amazon Redshift, and how they could impact your migration timeline.
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For more information, see How to migrate a large data warehouse from IBM Netezza to Amazon Redshift with no downtime.
For stakeholders, it means a lower cost of, and time to, migration. For developers, this means less time spent retraining on the new database.
You can migrate your data and applications to Amazon Redshift in less time and with fewer changes than migrating to other analytics platforms. For some, this presents an opportunity to transition to the cloud.Īmazon Redshift is a cloud-native data warehouse platform built to handle workloads at scale, and it shares key similarities with Netezza that make it an excellent candidate to replace your on-premises appliance. With IBM announcing Netezza reaching end-of-life, you’re faced with the prospect of having to migrate your data and workloads off your analytics appliance.
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