Quantyl Discovery features

Guess what your machine is capable of?

Quantyl Discovery is a feature-rich application dedicated to data discovery and data visualization. It empowers you with a rich set of highly interactive tools to select, filter, aggregate, visualize, animate your data. It’s a self-service exploration tool where visualization-authoring and consumption are merged. It is designed to be fast, easy to use and serverless.

Its user experience and flexibility make it unique on the market, for a single purpose: exploring data in the most efficient possible ways. 

How it works?

There is a single application to install from the Microsoft store: Quantyl Discovery. 

A 14 days trial of the  U N L I M I T E D  Edition is enabled by default. After this period, the app switches to the Free Edition.

You can purchase one of the different available Editions from within app, or use the Free Edition of the product. 

Editions only differ in the number of rows of data you can import and save per table. Quantyl Discovery extracts dimensions and measures from tabulated data imported from large text files like .csv and database through an ODBC 64-bit connector. You can also use Python and R scripts to connect to a vast amount of data sources.

Exploration of your own content is free, without any limitation. It means that other users may install Discovery for free and explore your content if you decide to share projects(s) with them.

Core features

Secure and non intrusive

Quantyl is non-intrusive (non-destructive workflow), it extracts needed data in memory and saves in its own format (a mix of binaries and .xml). Quantyl will NEVER modify your data. The content (your study) created in Quantyl (projects and views) may be updated quickly when input data has changed.

Quantyl does NOT hold any data in the cloud. Your data extracts are saved on local projects, in your file system.

No data preparation

Data preparation / aggregation process is not required. Everything is performed on the fly, leveraging memory cache and multicore CPU architecture on commodity windows machine. Quantyl is designed to scale-up on multi-core CPUs. You open/import your data directly in the application and start to work.

Agile

Unlike BI tools, it is not focused on data Aggregation, it is not focused on dashboarding. You can still work on fine grain records without performances drop, visualizing several millions of data points sorted in different layers, on a standard 64bits OS-Window machine like a tablet, a laptop or a desktop PC. Of course, you still can filter and aggregate data in any dimension you need, like you would do in a BI tool.

Rich content creation

A Quantyl project captures all needed data and content, without external reference. You may archive your project in a compact zip-archive to share it or for later use. It is self contained.

Content exploration is free for anyone.

Big data

Big data means different things for different people (Volume, Velocity, Variety…).

Our current sweet spot is to empower you to interact with millions of rows in the scope of  your local device, at a reasonnable cost, with ease. 

We offer a trial period of Quantyl Discovery U N L I M I T E D  Edition to give you an opportunity to test on your own data, without limits.

Technology

We are technology makers, we own 100% of our core components, both analytics and visualization. We are proudly native.

From a technology standpoint, Quantyl has more to share with game engines than the great D3.js or any web technology which are mainstream in data visualization. 

Quantyl discovery is designed to support a large number of charts and graphics with no latency, all interactively connected with cross selection, dynamic filtering, animation. This emphasis on multiple charts and performances offers new ways to explore data.

Data discovery and visualization, like almost any computing challenge, is about data transformation. It’s quite obvious that you have to minimize those data transformations and data movement to get high level of speed. When you have non homogeneous components, they often don’t speak the same “language”, do not share the same structures, and obviously there is a price to pay for this data translation.

Visualization and analytics are generally 2 different layers. This is a good practice in software development to separate UI from other layers (logically and physically). But in performance driven dataviz scenarios, you may reach big gains by getting both of them closer. It is what we do.