Simplicity is the ultimate sophistication. Netezza consistently proves this by setting the standard for simplicity of use in enterprise data warehousing and analytics.

For years, others have tried to emulate Netezza’s elegance, simplicity, and speed. None have matched.

Over time, our customers have fallen in love with Netezza’s ease of use, its ability to linearly scale on performance, its deep-rooted analytics stack, and its “things just work” mentality.

For all those Netezza fanatics—and the soon to be converted—we have a huge announcement for you today:

Netezza Performance Server on the Cloud will be available on IBM Cloud and Amazon Web Services starting June 19, 2020. This development marks a significant departure from our prior releases because customers will now have a choice of where they want to deploy their data warehouse—on cloud, on-premises with the hyperconverged IBM Cloud Pak for Data System, or both.

The same great Netezza features you’ve come to expect on-premises are there on the cloud—simplicity, elegant design, ease of use, and linear scale. It’s still running on top of the same asymmetric massively parallel processing architecture (AMPP) that’s been powering your analytics workloads for years. And, best of all, it’s the same Netezza database engine—running on the cloud—delivered through a highly modular and extensible cloud native Data and AI platform. We call this platform Cloud Pak for Data.

What can you expect from your Netezza on the cloud?

For our existing Netezza customers, it’s business as usual. Same great database engine, same simple lift and shift to the cloud (nz_migrate or backup/restore), and same support for third-party tools that you’ve grown to love using with Netezza.

We’re going to have a lot more to say in the coming weeks and months. For the time being, read our extensive blog on the IBM Big Data Hub. Or, if you can’t wait, talk to one of our Netezza experts today to learn more.

We’re extremely excited to have Netezza back, and we know you’re excited too. Let’s get to work.


More from Analytics

Data science vs data analytics: Unpacking the differences

5 min read - Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. It focuses on data collection and management of large-scale structured and unstructured data for various academic and business applications. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to…

Financial planning & budgeting: Navigating the Budgeting Paradox

5 min read - Budgeting, an essential pillar of financial planning for organizations, often presents a unique dilemma known as the “Budgeting Paradox.” Ideally, a budget should give the most accurate and timely idea of anticipated revenues and expenses. However, the traditional budgeting process, in its pursuit of precision and consensus, can take several months. By the time the budget is finalized and approved, it might already be outdated.In today's rapid pace of change and unpredictability, the conventional budgeting process is coming under scrutiny.It's…

How Macmillan Publishers authored success using IBM Cognos Analytics

5 min read - Macmillan Publishers is a global publishing company and one of the “Big Five” English language publishers. If you're a reader, chances are good you've read a book from Macmillan. They published many perennial favorites including Kristin Hannah’s The Nightingale, Bill Martin’s Brown Bear, Brown Bear, what do you see? and some of the more recent bestsellers such as The Silent Patient by Alex Michaelides, Identity by Nora Roberts and Razorblade Tears by S. A. Cosby. It’s no wonder then that Macmillan…

MLOps and the evolution of data science

7 min read - The advancement of computing power over recent decades has led to an explosion of digital data, from traffic cameras monitoring commuter habits to smart refrigerators revealing how and when the average family eats. Both computer scientists and business leaders have taken note of the potential of the data. The information can deepen our understanding of how our world works—and help create better and “smarter” products. Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven…