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Dremio vs IBM Watson Explorer comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Dremio
Average Rating
8.6
Reviews Sentiment
7.1
Number of Reviews
8
Ranking in other categories
Cloud Data Warehouse (9th), Data Science Platforms (9th)
IBM Watson Explorer
Average Rating
8.4
Number of Reviews
10
Ranking in other categories
Data Mining (11th)
 

Mindshare comparison

While both are Business Intelligence solutions, they serve different purposes. Dremio is designed for Data Science Platforms and holds a mindshare of 4.2%, up 3.0% compared to last year.
IBM Watson Explorer, on the other hand, focuses on Data Mining, holds 1.0% mindshare, up 0.9% since last year.
Data Science Platforms
Data Mining
 

Featured Reviews

KamleshPant - PeerSpot reviewer
Solution offers quick data connection with an edge in computation
It's almost similar, yet it's better than Starburst in spinning up or connecting to the new source since it's on SaaS. It is a similar experience between the based application and cloud-based application. You just get the source, connect the data, get visualization, get connected, and do whatever you want. They say data reflection is one way where they do the caching and all that. Starburst also does the caching. In Starburst, you have a data product. Here, the data product comes from a reflection perspective. The y are working on a columnar memory map, columnar computation. That will have some edge in computation.
it_user840897 - PeerSpot reviewer
Ingests, retrieves information from a range of sources; enables dissecting questions in context and answering them
WEX is more a platform, I believe, than it is the application. I could talk about what I'm looking for in the application. We've done visualizations and we can do basic analysis with the system as it stands. Where we're looking to take it is implementing it into workflows, so the workers on the line can actually understand the risks that they're exposing themselves to and then address them on the fly. So that's fantastic. And then the final one is, it's not prediction, but maybe anticipation. So when people are put at risk, we'll be implementing solutions shortly that will help people anticipate the risks and the dangers they're exposing themselves to so they can control them.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"Dremio gives you the ability to create services which do not require additional resources and sterilization."
"Dremio enables you to manage changes more effectively than any other data warehouse platform. There are two things that come into play. One is data lineage. If you are looking at data in Dremio, you may want to know the source and what happened to it along the way or how it may have been transformed in the data pipeline to get to the point where you're consuming it."
"We primarily use Dremio to create a data framework and a data queue."
"It's almost similar, yet it's better than Starburst in spinning up or connecting to the new source since it's on SaaS."
"Overall, you can rate it as eight out of ten."
"Dremio is very easy to use for building queries."
"Everyone uses Dremio in my company; some use it only for the analytics function."
"Dremio allows querying the files I have on my block storage or object storage."
"The valuable feature of Watson Explorer for us is data entities, and to see the hidden insights from within unstructured data."
"For me, as a user, the most valuable feature is the ability to ingest and then retrieve information from a range of separate sources; the ability to dissect questions in context and actually answer them."
"Ease of use is pretty good as is the standardization of not actually having to have my own natural learning algorithms, just to use the Watson APIs."
"We take natural language that was happening in our repositories and our application and then feed it to the Watson APIs. We receive JSON payloads as an API response to get cognitive feedback from the repository data."
"The ability to easily pull together lots of different pieces of information and drill down in a smarter way than has been possible with other analytics tools is key. Watson is all based on a set of AI and deep learning, machine-learning capabilities, and it is looking behind the scenes at some relationships that you likely would not have spotted on your own. It's pulling things together, categorizing some things, that are not something that you might have seen on your own."
"I have found the auto-generated document very useful as well as the main keywords that are highlighted, which are used for the search functionality within IBM Watson Explorer."
 

Cons

"Dremio doesn't support the Delta connector. Dremio writes the IT support for Delta, but the support isn't great. There is definitely room for improvement."
"I cannot use the recursive common table expression (CTE) in Dremio because the support page says it's currently unsupported."
"They need to have multiple connectors."
"There are performance issues at times due to our limited experience with Dremio, and the fact that we are running it on single nodes using a community version."
"It shows errors sometimes."
"We've faced a challenge with integrating Dremio and Databricks, specifically regarding authentication. It is not shaking hands very easily."
"They have an automated tool for building SQL queries, so you don't need to know SQL. That interface works, but it could be more efficient in terms of the SQL generated from those things. It's going through some growing pains. There is so much value in tools like these for people with no SQL experience. Over time, Dermio will make these capabilities more accessible to users who aren't database people."
"Dremio takes a long time to execute large queries or the executing of correlated queries or nested queries. Additionally, the solution could improve if we could read data from the streaming pipelines or if it allowed us to create the ETL pipeline directly on top of it, similar to Snowflake."
"I would say, give some kind of a community edition, a free edition. A lot of companies do, even Amazon gives you some kind of trial and error opportunities. If they could provide something like that, it would be good."
"Small businesses will probably have a little harder time getting into it, just because of the amount of resources that they have available, both financial and time, but it really is a solution that should work for them."
"The solution is expensive."
"More cognitive feedback would be good. The natural language analysis is great, the sentiment analyzers are great. But I would just like to see more... innovation done with the Watson platform."
"Much of IBM operates this way, where they have sets of tools that are in the middleware space, and it becomes the customer's responsibility or the business partner's responsibility to develop full solutions that take advantage of that middleware. I think IBM's finding itself in that spot with Watson-related technologies as well, where the capabilities to do really interesting and useful things for customers is there, but somebody still has to build it. Is that going to be the customer? Are they going to be willing to take on that responsibility themselves"
"It is a little bit tricky to get used to the workflow of knowing how to train Watson, what can be provided, what can't be, how to provide it, how to import, export, and what it means every time you have to add a new dictionary"
"It needs better language support, to include some other languages. Also, they should improve the user interface."
"Stability is actually one of the areas that could use improvement. Setting it up is always tough. Setting Explorer requires experts, but also the underlying platform is not that stable. So it really needs a good expert to keep it running."
 

Pricing and Cost Advice

"Right now the cluster costs approximately $200,000 per month and is based on the volume of data we have."
"Dremio is less costly competitively to Snowflake or any other tool."
"The solution is expensive."
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Top Industries

By visitors reading reviews
Financial Services Firm
31%
Computer Software Company
10%
Manufacturing Company
6%
Healthcare Company
4%
Computer Software Company
15%
Educational Organization
15%
University
11%
Financial Services Firm
11%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Dremio?
Dremio allows querying the files I have on my block storage or object storage.
What is your experience regarding pricing and costs for Dremio?
The licensing is very expensive. We need a license to scale as we are currently using the community version.
What needs improvement with Dremio?
They need to have multiple connectors. Starburst is rich in connectors, however, they are lacking Salesforce connectivity as of today. They don't have Salesforce connectivity. However, Starburst do...
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Comparisons

 

Also Known As

No data available
IBM WEX
 

Overview

 

Sample Customers

UBS, TransUnion, Quantium, Daimler, OVH
RIMAC, Westpac New Zealand, Toyota Financial Services, Swiss Re, Akershus University Hospital, Korean Air Lines, Mizuho Bank, Honda
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