Nimble Storage deployed HPE 好色先生TV Vertica Analytics Platform to replace its legacy open-source database management (DBM) system
As Nimble Storage expanded, they sought to move off their previous database solution to a product that was also more scalable and more robust. They had been using the open-source object-relational database system PostgreSQL. While PostgreSQL is scalable to the multiple terabyte level, Nimble Storage found that it would be a complicated endeavor and preferred a solution that had more flexibility and back-end support.
The company also sought to better collect and manage data coming from their many storage arrays and use that data to run analytics queries. PostgreSQL had only limited analytics capabilities. With half a trillion pieces of information coming in every day, their daily query jobs were taking longer than 24 hours on their previous system. Additionally, a 600% growth in their customer base over the past four years has brought an ever-growing amount of data.
The company evaluated a few options other than HPE 好色先生TV? Vertica? Analytics Platform such as Hadoop, another open-source system like PostgreSQL, but only seriously investigated Analytics because they saw that it filled in all the gaps from their previous solution while greatly exceeding their prior analytics capabilities. One additional benefit of Vertica Analytics Platform over other solutions was the flexibility of node scaling, allowing for extra nodes to be added without downtime.
Nimble Storage started their deployment in 2014 and gradually expanded their number of licenses with Vertica Analytics Platform as they and their customer base grew. They doubled their number of subscriptions in 2015 and increased by a similar amount the year after that.
The company was able to mostly reuse hardware that they already had from their previous solution, leading to low hardware costs up front. The company now has an integrated flow of data from their many flash arrays into the Analytics engine, allowing for remote monitoring of array status and storage use.
Like many data management and analytics solutions, the majority of the benefits came from user productivity gains. However, Nimble Storage also found benefits to their sales cycle and reduced hardware.
Costs of the project included hardware, software, and personnel time.
Because Nimble Storage is a newer company, they initially began with an opensource database. For companies using open-source software, it is important to recognize when potential limitations outstrip the cost saving benefits. For Nimble Storage, the benefits of Analytics exceeded just their initial list of limitations, having a wider impact on the company and achieving a positive ROI across multiple departments.
The biggest portion of the ROI for Nimble Storage came from avoided technical engineer hires. Avoided hires are a common benefit to data management deployments for companies that are scaling out of smaller legacy solutions. Companies should perform cost projections to understand how their current solution scales in terms of price versus functionality, and how their prospective growth will affect their employee headcount. By looking at the productivity benefits in terms of avoided hires, companies may find that getting the right database or analytics engine in place in the early stages of company growth will simplify concurrent personnel growth.
Nucleus quantified the initial and ongoing costs of software subscription fees, hardware, and personnel time to implement and support the application.
Direct benefits quantified included the avoided hiring of addition engineers and avoided hardware costs due to the greater compression capabilities of the new solution. The indirect benefits quantified included the increase in data scientist and data engineer productivity driven by the deployment, calculated based on the average annual fully loaded cost of the employees. These productivity savings were quantified based on the average annual fully loaded cost of an employee using a correction factor to account for the inefficient transfer between time saved and additional time worked. Also quantified was increased revenue stemming from a shortened sales cycle.
Nimble Storage was founded in 2008 and is based in San Jose, California. They released their first product in 2010 and have since grown to over 10,000 customers globally with an annual revenue of $322 million. Their main product is their flash storage arrays, but they also offer converged infrastructure and predictive analytics for storage.
The company spawned out of the shift from data storage exclusively on hard disks to flash storage, seeing that consumer-sized flash drives could be scaled for enterprise grade storage. They offer flexible storage methods for different types of applications, and tout predictive analytics as a primary method for maintaining high levels of uptime and consistent customer support.