Elastic {ON} has always been a great place to witness the sheer excitement shared by the developers and their management teams with Enterprise Search technologies. As a leading partner and sponsor of such events, we get a first-hand look at the adaptability of this technology and how it fits well with a diverse set of use cases.

One such use case is to identify an alternative to the Google Search Appliance (GSA). Over the past few months, Google has discontinued the sale of this product with an end-of-life support till 2018. While an appliance going out of support or obsolete is not very uncommon, it does put pressure on the teams supporting or managing such platforms.

There are close to a dozen such options in the market with one being a possible cloud-based solution offered by Google to its existing GSA customers. Most of the others being another appliance or a subscription based software.

With that said:

How do you decide what’s best?

What is the criteria of evaluation?

Did the existing appliance fit well in the original TCO plan?

and most importantly,

Will you be in a similar situation repeating the whole process again in next few years?

Let us take a closer look at this use case.

If it were a cloud replacement, wouldn’t you rather start monitoring your GSA usage to see if a cloud-based solution is acceptable? And while at it, wouldn’t you also start investigating on what the gaps were in the current environment and what are the areas of improvement. Of course, all of this will happen on top of how the new solution maps to existing features.

The most efficient approach must be tailored specifically to an Enterprise Search domain mapped to your organization. Start with putting together an inventory of current data sources, content security requirements, legal, regulatory compliance, and identifying what is important to your end users. Each line item needs to convert into an evaluation use case that would map to success criteria’s and a broader product feature list. Prioritize the features applicable to you like: incremental directory and website crawl, auto spell check, relevancy boosting, support of languages, caching, and most recently expert search. Combine all these requirements and map them to a broader set of proof-of-concept (PoC) use cases.

While you start reaching out to the leaders in the Gartner’s Magic Quadrant, I propose you to specifically a look at Elasticsearch as one such option. My previous blog “Beyond Enterprise Search” gives an overview of few use cases. However, the main differentiator is its strong open source community of committers and a huge acceptance by most of the top enterprises across the world.

I do agree that comparing a one-size-fits-all black box to a continuously improving open-source based platform is challenging, but when done in the right way is as much rewarding as well.

In addition to the GSA capabilities I mentioned earlier, the elastic platform can be further extended to: scale-out architecture, Big Data Integration, Role-Based Access Control, Encryption, LDAP/AD Integration, ACL Authorization, Auditing, Machine Learning (contextually relevant search results), Monitoring and Alert, Recommender Engine, and Advanced Analytics (time series, graph relationships, and more). As with most of the open source technologies, do account for customization as part of your overall migration plan. Also, include few notable third-party partner solutions that are proven migration accelerators.

From a TCO perspective, the technology is very reasonable when bought under a support license. Third-party plugins and API’s practically would pay for themselves within the first year when compared to building these API’s from scratch. On the question of when does this become obsolete, the answer is that being part of an open-source software project, there will be continued improvements constantly added with no vendor lock-in at your end. When the time does come to move to a more futuristic technology alternative, such a platform will not become a critical cost and give enough time for your teams to evaluate newer solutions.