Apache Solr is an open-source enterprise search server.
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IBM Watson Explorer
Score 8.4 out of 10
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IBM Watson Explorer supports enterprise search with unstructured data analysis, machine learning, and content analysis to improve decision-making, support customer service or serve other business needs.
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Searchspring
Score 9.0 out of 10
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Searchspring headquartered in Denver offers intelligent site search for customer facing web pages and ecommerce, providing product discvoery tools, navigation viacategory page, and other features to improve site navigation.
In February 2020, Searchspring merged with Nextopia to expand its product capabilities, and customer base. Nextopia customers will continue to receive the same services, under the SearchSpring brand.
Solr spins up nicely and works effectively for small enterprise environments providing helpful mechanisms for fuzzy searches and facetted searching. For larger enterprises with complex business solutions you'll find the need to hire an expert Solr engineer to optimize the powerful platform to your needs. Internationalization is tricky with Solr and many hosting solutions may limit you to a latin character set.
The Watson Explorer is great because it potentially replaces a meriad of other low-level analytics products that we would need to use for data analytics and data mining. WEX isn't really suitable much beyond doing text and data analytics and performing machine learning, so if your team doesn't really have a use-case that fits all of these categories, it is worth looking at an alternative.
Search Spring offers strong options for search customizations: synonyms, redirects, query replacements, spell corrections, etc. We enjoy the ability to boost and unique product display options. We were 4Tell customers prior to the Search Spring acquisition and we're looking forward to both being part of one console. Search Spring is a really solid, stable search/merch platform that I would recommend for any mid-market business.
Easy to get started with Apache Solr. Whether it is tackling a setup issue or trying to learn some of the more advanced features, there are plenty of resources to help you out and get you going.
Performance. Apache Solr allows for a lot of custom tuning (if needed) and provides great out of the box performance for searching on large data sets.
Maintenance. After setting up Solr in a production environment there are plenty of tools provided to help you maintain and update your application. Apache Solr comes with great fault tolerance built in and has proven to be very reliable.
Free to try - It's possible to use most of the useful features of Watson Explore on their trial/demo accounts.
Super well-designed data analytics tool - Most of the tools and features of the explorer are really useful, and truly help you fully understand the depth of any format of textual data.
Extensive sources compatibility - WEX can retrieve data from a large range of sources, and the compatibility there is quite good as well.
These examples are due to the way we use Apache Solr. I think we have had the same problems with other NoSQL databases (but perhaps not the same solution). High data volumes of data and a lot of users were the causes.
We have lot of classifications and lot of data for each classification. This gave us several problems:
First: We couldn't keep all our data in Solr. Then we have all data in our MySQL DB and searching data in Solr. So we need to be sure to update and match the 2 databases in the same time.
Second: We needed several load balanced Solr databases.
Third: We needed to update all the databases and keep old data status.
If I don't speak about problems due to our lack of experience, the main Solr problem came from frequency of updates vs validation of several database. We encountered several locks due to this (our ops team didn't want to use real clustering, so all DB weren't updated). Problem messages were not always clear and we several days to understand the problems.
Support is just OK, like most of the other IBM Watson products. The setup/integration is really hands-on, but it's also problematic because support later may take a considerable amount of time.
UI could still use a little more improvement - part of the administration and sources dashboards are hard to navigate.
The Application Builder is a great part of the product, but hard to learn/understand - this is where we needed the most support from IBM and tutorials/documentation.
Developing 'cocktails' of different ranking criteria. At the moment we can only serve results based on either 'relevancy' or 'sales performance'. It would be great to not only have the ability to blend these two options (by search term), but also add additional facets into the mix, such as stock quantity, margin, sponsorship factor etc...
Provide financing reporting on results - so we know how much revenue/conversion has been driven from specific search terms. For example, "Baby Milk" drove 50 searches, 6 direct conversions (customers that searched went on to buy an item(s) that were recommended), 16 indirect conversions (customers that searched went on to buy other item(s) not severed).
It takes some time to deploy and currectly maintein it. And also, to learn how to use and integrate in the enviroment as well. Once you get theses steps done, it usability is very simple, and almost of the time it don't require no further attention on it. Even for maintence, if you deploy it on a cluster mode, it is very reliable and easy to take one host down.
We have a monthly phone call with our account manager, and she is available for calls in between as well. She has always been accessible. Working with her has been easy and she has provided training where needed. She is proactive in making sure we have everything we need and feel comfortable with the platform.
We tried to use both Elasticsearch and Swiftype with Drupal 8 but there are currently no good modules that integrate Drupal with those solutions. So Solr was really the only option for a Drupal 8 web site. It's not as easy to learn or use as Swiftype, but in the end I think it will be a little less expensive and offer more customization and flexibility.
Google Cloud offers a Natural Language product, but it is just an API. This API doesn't offer the useful visualizations of relations, analytics, and graphs that IBM Watson Explorer offers on their interface. For this reason, we chose to go with IBM WEX. For later stages of our production, we decided to use Google's NLP API because we found that it was quick to integrate into production after studying data and developing models using IBM WEX.
Nextopia’s features were on par or better than consideration set at a lower cost and with an easier implementation. Contract terms were also more favorable.
Positive - Trial/demo period. This was really useful for us to figure out what features of WEX we liked most and how difficult it would be to integrate WEX into our workflow.
Negative - On-boarding was long and almost always requires support from IBM support, unlike most other products this advanced.
Positive - WEX replaced a large selection of alternative products we would have to use for the same functionality, and having all of that function in one place was definitely helpful.
