Let History Halve Your Next Data Analytics Purchase
Oct 13, 2017
Author: Vincent Morello
Market News, Snowflake for Industry, Snowflake Technology
If you’re lucky, you’ll spend just six to 12 months considering and buying your next enterprise software solution. If it’s a seven-figure purchase, plan for an additional six to 12 months to confirm your organization has made the best investment possible.
During that process, dozens of your IT and business leaders will engage your shortlist of on-premises and software-as-a-service (SaaS) vendors to compare these competing technologies based on architecture, features, performance, business benefits and cost of ownership. Buying a data warehouse is no different.
But what if you could shorten that process? What if you had fact-based information arranged in a non-traditional but highly effective method to help you confidently narrow your search and quicken your time to market (TTM) with your chosen solution? What would that be worth to your organization and your peace of mind?
All enterprise software decisions include alternatives that span the decades. Meaning, your organization is likely to consider upgrading an existing technology you already own, and consider solutions that represent today’s latest SaaS offerings.
How so? One of your alternatives might be to upgrade your existing, on-premises data warehouse your organization purchased 10 to 15 years ago. A solution that wasn’t much different when it first emerged in the 1990s, and hasn’t advanced much since first deployed in your data center.
You may also own or are considering an on-premises NoSQL solution such as Hadoop. This technology emerged just over a decade ago, challenging the very existence of the legacy data warehouse in order to accommodate the exponential increase in the volume, variety and velocity of existing and new data types.
Since the advent of Hadoop, many traditional data warehouse vendors now offer a cloud version of their on-premises solution. With all this said, there are now many more solution categories, and many new vendors, that you must consider for your next data warehouse purchase.
Herein lies the rub. Nearly all data warehouse purchase decisions, and all enterprise software decisions for that matter, take the form of a side-by-side-by-side, laundry-list comparison. That’s a significant amount of ground to cover, especially for data warehousing, which is four decades old. Your review of competing alternatives becomes even more protracted when the architectures and features of traditional and more modern products don’t align, which they never do.
Instead, think linear. Think like a historian. Many of the solutions you’ll consider are a response to the drawbacks, and benefits, of preceding, competing technologies. For example, at Snowflake, many of our customers had previously used a legacy data warehouse for years and then added a Hadoop solution to expand their data analytics platform. Unfortunately, that combination of technologies did not meet their ever-increasing requirements.
So, before you kick off a necessary, side-by-side comparison, consider your initial group of alternative technologies as building blocks, from bottom to top, from the oldest to the most recent. Then eliminate from contention those that do not add anything new or innovative. This approach enables you to focus on more recent technologies that truly deliver better value, and will enable you to continue to innovate well into the future. In the end, you’ll get to a shorter shortlist, and quickly, thus speeding your decision-making process and TTM by up 50 percent.
At Snowflake, we’ve done the hard work for you. We invite you to read this short but revealing ebook that details the benefits and drawbacks of each succeeding data analytics technology – from the birth of the legacy data warehouse, all the way to today’s modern, built-for-the-cloud data warehouse. We’re confident it will provide the insight you need to quicken your next data warehouse purchase by rapidly reviewing every technology that got us to where we are today.