So Many Choices…. Oh My! Choosing the Right Cloud Data Warehouse for My Modernization Project

So, you have made the business case to modernize your data warehouse. Good choice! A modernization project, done correctly can deliver compelling and predictable results to your organization including millions in cost savings, new analytics capabilities and greater agility. But how do you effectively go about choosing the right data warehouse to migrate to?

Want all the details? Check out the full case study here.

The Business Benefits of Data Migration Can be Compelling

At a global bank that recently migrated from Netezza to the Actian analytics platform was able to save $7.9M over 5 years demonstrating a nearly 45% reduction in TCO costs. Through advanced utilities they were able to convert over 95% of their queries and migrate to a system that demonstrated a nearly 20 X performance improvement. But the benefits went beyond cost savings and performance gains, the new system augmented their capabilities with increased workload concurrency, improved failover protection and advanced workload management.

What is the Right Choice?
Should you stay with your existing traditional data warehouse provider as they try to convince you to stay on-premises with their latest appliance? Or, perhaps move to their “lock you in for life” cloud platform? Or you could go to one of those new-fangled next gen cloud-only offerings? But does either path enable you to effectively avoid yet another perpetual lock-in (you can check-in but you can never leave)?

Well there is another path. The hybrid path; one based on understanding your unique needs and priorities. It starts with understanding that there are five important aspects of your future data warehouse that need to be understood in order to ultimately make a successful choice:

MEET THE BIG FIVE  >>>>

1. Data Diversity: How well does your future warehouse support disparate data types and does it provide users with a current and holistic view of data from a variety of internal and external sources?
2. Hybrid Infrastructure Support: How well does your future warehouse need to support the various current and future operational requirements of your organization by enabling secure access from anywhere, ingesting data in real time, and providing elasticity to increase or decrease compute and storage resources when you need to?
3. Analytics Performance: How easy is it to adopt sophisticated analytical techniques at scale, such as machine learning, predictive analytics, deep learning, and IoT analytics? How important is having your data be kept “fresh” so you can be certain that real-time decisions are based on accurate, up to date data?
4. Concurrency: Can multiple user teams obtain the throughput performance they require even when dozens of other users are on the system? Do you have the goal of developing composable analytics apps that could issue multiple complex queries in parallel? Is your future data warehouse architecture smart enough to optimize available resources across a collection of users or will it force you to allocate resources in a “captive fashion” to ensure performance SLAs are met, usually at additional cost?
5. Security and Governance: Are there tools and techniques in place to control data access, support data governance, and ensure compliance with data security standards?

As Alex Woodie eloquently describes in his recent article, Big Data Is Still Hard. Here’s Why, with so much investment in cloud platforms, one might assume that data management is destined to improve, to get simpler and easier over time. In actuality, most established enterprises will continue to use on-prem systems to store the most critical data and run the most critical workloads, while using cloud options for data and workloads that are less critical and also newer.

This emerging hybrid world, which encompasses on-premises and cloud workloads, will introduce more complexity to data management tasks, and open more opportunities for failure, than if companies had been running everything on-premises or everything in the cloud.

Where to Find This Mythical Hybrid Data Warehouse of the Future Today?

One option worth considering is Actian’s cloud data warehouse. This next-generation cloud data warehouse features industry leading price performance. It excels at larger, more demanding workloads delivering up to 20X performance advantage of other alternatives at close to 1/10 the cost. Click here for the Actian Data Platform vs. Redshift benchmark report.  With the Actian Data Platform (formerly known as Avalanche), you’re able to seamlessly run on-premises and in the cloud (AWS, Azure and soon GCP support) this is the perfect hybrid database that creates options for you rather than take them away!

For more information go to Actian cloud data warehouse.

This is part two of a three part series. Check out Part Three: It’s not the Destination but the Journey – How Smart IT Managers Are Forging Their Data Warehouse Path to the Cloud here.

Also, learn more migration best practices by watching our on-demand webinars featuring leading industry analysts and former executives from Teradata and Netezza.

Rethinking data warehouse modernization, featuring James Curtis, Senior Analyst, 451 Research

Rethinking Teradata Migration: 7 real-world secrets to success, featuring Raghu Chakravarthi, SVP of R&D at Actian (former Head of Big Data at Teradata)

Top 7 tips for a successful migration from Netezza, featuring Paul Wolmering, VP of Sales Engineering at Actian (former Director of Tech Services at Netezza)

About Actian Corporation

Actian is helping businesses build a bridge to a data-defined future. We’re doing this by delivering scalable cloud technologies while protecting customers’ investments in existing platforms. Our patented technology has enabled us to maintain a 10-20X performance edge against competitors large and small in the mission-critical data management market. The most data-intensive enterprises in financial services, retail, telecommunications, media, healthcare and manufacturing trust Actian to solve their toughest data challenges.