Explorium, headquartered in San Mateo, provides an External Data Platform that automatically discovers thousands of relevant data signals and uses them to improve analytics and machine learning. The automated Explorium Platform enables organizations to discover and use third party data to improve predictions and ML model performance. With faster, better insights, organizations can increase revenue, streamline operations and reduce risks.
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Posit
Score 10.0 out of 10
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Posit, formerly RStudio, is a modular data science platform, combining open source and commercial products.
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Pricing
Explorium
Posit
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Explorium
Posit
Free Trial
No
Yes
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
Optional
Additional Details
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More Pricing Information
Community Pulse
Explorium
Posit
Features
Explorium
Posit
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Explorium
7.8
1 Ratings
7% below category average
Posit
9.3
27 Ratings
11% above category average
Connect to Multiple Data Sources
8.01 Ratings
8.026 Ratings
Extend Existing Data Sources
8.01 Ratings
9.927 Ratings
Automatic Data Format Detection
7.01 Ratings
9.926 Ratings
MDM Integration
8.01 Ratings
00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Explorium
6.5
1 Ratings
26% below category average
Posit
9.0
27 Ratings
7% above category average
Visualization
6.01 Ratings
8.027 Ratings
Interactive Data Analysis
7.01 Ratings
10.024 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Explorium
6.5
1 Ratings
23% below category average
Posit
10.0
26 Ratings
20% above category average
Interactive Data Cleaning and Enrichment
6.01 Ratings
10.024 Ratings
Data Transformations
6.01 Ratings
10.026 Ratings
Data Encryption
7.01 Ratings
00 Ratings
Built-in Processors
7.01 Ratings
00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Explorium
7.3
1 Ratings
14% below category average
Posit
10.0
22 Ratings
18% above category average
Multiple Model Development Languages and Tools
7.01 Ratings
10.022 Ratings
Automated Machine Learning
8.01 Ratings
00 Ratings
Single platform for multiple model development
8.01 Ratings
10.022 Ratings
Self-Service Model Delivery
6.01 Ratings
10.019 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
We need to constantly measures costs in our health business and we forecast pricing acoording to several values and conditions. Explorium works quite good analysing simple datasets, but when hierahies start to increase, meaning 6-10 olap variables, the system start to slow down quite a bit until was no longer to retrieve the info we required. This is why we test several tools, because even world-class solutions we purchase, don´t do the job we need. Explorium is a good tool, but complexity will be a minus in some scenarios.
In my humble opinion, if you are working on something related to Statistics, RStudio is your go-to tool. But if you are looking for something in Machine Learning, look out for Python. The beauty is that there are packages now by which you can write Python/SQL in R. Cross-platform functionality like such makes RStudio way ahead of its competition. A couple of chinks in RStudio armor are very small and can be considered as nagging just for the sake of argument. Other than completely based on programming language, I couldn't find significant drawbacks to using RStudio. It is one of the best free software available in the market at present.
The support is incredibly professional and helpful, and they often go out of their way to help me when something doesn't work.
The one-click publishing from RStudio Connect is absolutely amazing, and I really like the way that it deploys your exact package versions, because otherwise, you can get in a terrible mess.
Python doesn't feel quite as native as R at the moment but I have definitely deployed stuff in R and Python that works beautifully which is really nice indeed.
Python integration is newer and still can be rough, especially with when using virtual environments.
RStudio Connect pricing feels very department focused, not quite an enterprise perspective.
Some of the RStudio packages don't follow conventional development guidelines (API breaking changes with minor version numbers) which can make supporting larger projects over longer timeframes difficult.
There is no viable alternative right now. The toolset is good and the functionality is increasing with every release. It is backed by regular releases and ongoing development by the RStudio team. There is good engagement with RStudio directly when support is required. Also there's a strong and growing community of developers who provide additional support and sample code.
For someone who learns how to use the software and picks up on the "language" of R, it's very easy to use. For beginners, it can be hard and might require a course, as well as the appropriate statistical training to understand what packages to use and when
RStudio is very available and cheap to use. It needs to be updated every once in a while, but the updates tend to be quick and they do not hinder my ability to make progress. I have not experienced any RStudio outages, and I have used the application quite a bit for a variety of statistical analyses
Since R is trendy among statisticians, you can find lots of help from the data science/ stats communities. If you need help with anything related to RStudio or R, google it or search on StackOverflow, you might easily find the solution that you are looking for.
The simplicity of the tool is an advantage. The integrations as well work quite well. All these solutions have worked well until some point and what we have discovered over the years is that we need to combine various solutions. There is no such thing as one tool ruling them all. Explorium works quite well until we start testing more advanced relations, and here, the tool is promising but requires a little work.
RStudio was provided as the most customizable. It was also strictly the most feature-rich as far as enabling our organization to script, run, and make use of R open-source packages in our data analysis workstreams. It also provided some support for python, which was useful when we had R heavy code with some python threaded in. Overall we picked Rstudio for the features it provided for our data analysis needs and the ability to interface with our existing resources.
RStudio is very scalable as a product. The issue I have is that it doesn't necessarily fit in nicely with the mainly Microsoft environment that everybody else is using. Having RStudio for us means dedicated servers and recruiting staff who know how to manage the environment. This isn't a fault of the product at all, it's just part of the data science landscape that we all have to put up with. Having said that RStudio is absolutely great for running on low spec servers and there are loads of options to handle concurrency, memory use, etc.
Using it for data science in a very big and old company, the most positive impact, from my point of view, has been the ability of spreading data culture across the group. Shortening the path from data to value.
Still it's hard to quantify economic benefits, we are struggling and it's a great point of attention, since splitting out the contribution of the single aspects of a project (and getting the RStudio pie) is complicated.
What is sure is that, in the long run, RStudio is boosting productivity and making the process in which is embedded more efficient (cost reduction).
