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This section provides a short overview of

D s product
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, its key features, and how they interact with each other.

...

Data preparation (or data wrangling) has been a constant challenge for decades, and that challenge has only amplified as data volumes have exploded.

Note

Did you know:

D s item
itemproducts
are used in 173 countries by over 75,000 users in 20,000 organizations.



Why use 
D s product
?


Tip

Company value: Be a multiplier.


Estimates vary, but something like 60% of an analyst's time is consumed with preparing data for use, leaving two days per week to actually analyze it. That's expensive and inefficient.

Note

Did you know: This new category of software, called data wrangling or data prep, was invented by the founders of

D s company
, who created the first self-service data preparation tool. This joint Stanford/UC Berkeley release was called Data Wrangler.

Some organizations have pushed these cleansing efforts onto IT, which may take weeks to come up with a custom, scripted solution that requires inevitable back-and-forth between coder and analyst. As formats, feeds, and requirements change, these rigid solutions require frequent updating, which cannot be done by the people who really know the data. Instead of producing insights, analysts are filing requests and waiting for weeks for solutions.


The 

D s item
itemsolution
 delivers the tools to wrangle data to the people who understand the meaning of the data. With
D s brand
, analysts have the means to apply their expertise to the preparation of the data, in a way that is faster and more productive.

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Featuring a leading-edge interface, powerful machine intelligence, and advanced distributed processing,

D s product
 renders the time-consuming, complex, and error-prone process of preparing datasets of any volume into a point-and-click exercise. What took six weeks in the IT lab can be done in less than two hours at the analyst's desk. 

Note

Did you know:

D s company
has been ranked the #1 vendor in Dresner Advisory Service's report on the data prep space in each of the last four years.



Predictive Interaction


Tip

Company value: It starts with the user.

Humans are pretty good at identifying singular problems; software is better at solving them at scale. The platform leverages this concept through predictive interaction.

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For more information, see Overview of Predictive Transformation.

Machine Learning


Tip

Company value: Always be learning.


As you make selections, the platform's predictions become smarter and better. What you select today with this dataset informs the platform recommendations for transforming tomorrow's dataset.

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The scale and complexity of these transformations can quickly overwhelm even the most powerful of machines.

D s product
 utilizes a number of techniques to deliver high performance at scale.


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Platform interactions and data movements

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Datasets, recipes, and outputs can be grouped together into objects called flows. A flow is a unit of organization in the platform. Depending on your product, flows can be shared between users, scheduled for automated execution, and exported and imported into the platform. In this manner, you can build and test your recipes, chain together sets of datasets and recipes in a flow, share your work with others, and operationalize your production datasets for automated execution.

What else can you do in 
D s product
?

In addition to the above, the following key features simplify the data prep process and bring enterprise-grade tools for managing your production wrangling efforts.

Visual Profiling


Tip

Company value: Iterate to excellence.


For individual columns in your dataset, data histograms and data quality information immediately identify potential issues with the column. Select from these color-coded bars, and specific suggestions for transformations are surfaced for you. When you make a selection, you can optionally choose to display only the rows or columns affected by the change.

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The

D s platform
supports automation through externally available REST APIs.

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See API Reference.


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Getting Started

Overviews: Predictive Transformation | Sampling | Visual Profiling

Basics: Object Model | Import | Profiling | Transform | Running Job | Export