Elasticsearch vs. Yext

Elasticsearch is really well suited for searching text (Natural Language Processing) and you can fine tune the searches and scoring very well. I like the ability to find Significant Terms in the Index, where you can find aggregations that are really relevant to a specific search. It also allows for queries to lead to new queries via aggregations which is great for navigating your data. It is less suited to doing more complex aggregations where slices of data are required to be processing using guassian normalizations. And doing searches which join different documents is very very hard, and requires serious thought on how to denormalize data.

Incentivized

Yext is great for bigger clients. If your client has a franchise of any kind, or even multiple locations, that's an ideal use case. For smaller clients, with one location that doesn't change a lot, Yext isn't as viable, and the effort to update maybe ten or fifteen listings would be worth it.

Incentivized

• Super-fast search on millions of documents. We've got over 2 billion documents in our index and the retrieve speeds are still in the < 1-second range.
• Analytics on top of your search. If you organize your data appropriately, Elasticsearch can serve as a distributed OLAP system
• Elasticsearch is great for geographic data as well, including searching and filtering with geojson, and a variety of geospatial algorithms.

Incentivized

• Making sure our location data is correctly being understood by Google.
• Taking into account best design principles for our pages layout.
• Staying on top of Google best practices so we don't have to.
• Building a very robust place for us to store all our location information.
• Solid integrations with some of our other key partners, for example Olo, Punchh.
• Being a very good partner for us, our Client Success Manager is exceptional.

Incentivized

• Setting Java memory thresholds can be a pain for those not accustomed to things like Eden Space & Old Generation which can lead to over allocation, or more likely, under allocation. Apache Solr had a similar issue. It would be nice if the program would take an extra step and dogfood it's own advice by analyzing the system & processes to return a solid recommendation for that configuration. The proper configuration information is outlined in the documentation, it would be nice if that was automated.
• The only health check that ElasticSearch reports back is a "red" status without any real solid information about what is going on, though its usually memory thresholds or disk I/O. I am currently on ElasticSearch 1.5 so that may have changed for newer versions. When the status goes "red", I as the administrator of the software, feel like I lose control of whats going on which should rarely happen. Something more verbose would eliminate that.
• This is more of a critique of the ElasticStack in general. The whole top to bottom stack is starting to get feature creep with things that are better suited in other software and increasing the barrier for entry for people to get started with setting up a robust logging infrastructure. ElasticSearch as a storage search engine, is pretty streamlined, but I can see that the tools that comprise the ELK Stack are going to require a certification with constant study at some point. During major release for Logstash a while back, it literally took a month to learn a new language because Elastic completely changed the syntax. For a medium sized organization of only a couple of admins, that is a pretty high bar where time is money. They really should work on refining/automating the tools & search engine they have, instead of shoehorning/changing things on to an already rock solid foundation.

Incentivized

• Cost. This platform offers a lot of powerful features, but it has an expensive barrier to entry.
• Annual contracts limit flexibility for smaller companies
• The biggest drawback is that you need to keep your subscription active or listings/citation data will revert back to what it was pre-Yext implementation

Incentivized

We're pretty heavily invested in ElasticSearch at this point, and there aren't any obvious negatives that would make us reconsider this decision.

Incentivized

To get started with Elasticsearch, you don't have to get very involved in configuring what really is an incredibly complex system under the hood. You simply install the package, run the service, and you're immediately able to begin using it. You don't need to learn any sort of query language to add data to Elasticsearch or perform some basic searching. If you're used to any sort of RESTful API, getting started with Elasticsearch is a breeze. If you've never interacted with a RESTful API directly, the journey may be a little more bumpy. Overall, though, it's incredibly simple to use for what it's doing under the covers.

Incentivized

Yext is pretty easy and straightforward to utilize out of the box. While I work for a digital marketing agency, and this is second nature to me at this point, I do believe the average SMB owner with zero digital marketing experience could be a proficient Yext user within 1 hour or so of usage. It's easy

Incentivized

We've only used it as an opensource tooling. We did not purchase any additional support to roll out the elasticsearch software. When rolling out the application on our platform we've used the documentation which was available online. During our test phases we did not experience any bugs or issues so we did not rely on support at all.

Incentivized

We are a larger company with 26 locations, so we may receive additional attention. But they are always quick to answer our calls or emails. I rarely need support as the software delivers as promised. I will say their staff thus far has been very knowledgable about Yext and some lesser-known features. For us, they really understand our needs and why I'd be asking for certain solutions.

Incentivized

Do not mix data and master roles. Dedicate at least 3 nodes just for Master

Incentivized

Elasticsearch is the most well-known and supported free data platform that we identified. We are taking advantage of community knowledge and practices. In terms of flexibility and breadth of use cases no other competitor came close to Elasticsearch. We've tried Solr in the past be we encountered issues which were deal-breaking for us. MongoDB - it just did not pass our evaluation parameters as a main data platform. We still use it for smaller purposes, though.

Incentivized

Before Yext, I used Moz Local to manage our business listings. Moz Local had fewer listing platforms included with their service. For example, Moz Local did not include Yahoo, and we had issues with inconsistent Yahoo listings. We ultimately switched to Yext and it's been a good fit for us.

Incentivized

• I am not in finance and I suspect even if I was this would be hard to measure. But for sure, Elasticsearch has enabled us to have the most flexible data model in the industry for our customer's data, and in doing so we have attracted many many technical customers and got much of their $$$.
• One problem with Elasticsearch is that because it runs on the JVM, there can be some stop-the-world JVM garbage collections happening that can take down nodes and reduce indexing speed. The solution for that tends to be "let's just upgrade the CPU on that machine". And before you know it you are paying $$$ because this'll happen with 40+ machines.
• On the other hand, I do think that ES is more efficient than other systems and so it requires fewer nodes to keep it highly tolerant and available, so we probably saved some money that way.

Incentivized

• Yext has absolutely strengthened our SEO performance and has driven leads that perform extremely well.
• Yext has improved our consultation show rate because all of the clinic information for each of our locations is accurate no matter which website our clients are using to search for the clinic info.
• Yext has allowed our organization to ensure the correct phone numbers are listed across the web, which increased the number of inbound leads that come into our Call Center.

Incentivized