Squirrel – Enabling Accessible Analytics for All
Submitted by sudipta mukherjee (@samthecoder) on Sunday, 31 May 2015
Simplify and widen the scope of the Software Developer to create smart tools that enable easy access and actionable insights for all.
Enable the Consumers and Business users to acquire, generate and visualize data from a variety of sources on personalized devices with little or no training
Provide the large .NET developer community ways to collaborate in creating much needed tools for Data Processing and Analytics by leveraging the full .NET stack, available in open-source. Today, Microsoft is committed to port .NET to all major platforms, like Linux and Mac OS.We bring to the Developer, Consumer and Business users the Small Data movement, enabling the creation of simpler, smarter and more responsive applications that can reach mobile users providing easier to consume and intuitive experiences.
Big Data and Analytics are buzz-words in the industry today. But what is Small Data? Well, it is the last mile for Big Data. After the sensors and sources have been wired to information flows and visuals, the “last mile” is where value is created and impressions are formed by our Consumer and Business users, with insights into the most relevant to their task at hand.
And, what do we mean by Accessible Analytics? It means enabling easy access to Big and/or Small Data via simple tools. This clearly implies the following:
Connect to all relevant enterprise and application data and content sources, including RDBMS, NoSQL, Hadoop, social media, and ultimately machine data.
Good information design, ability to apply third-party visualizations, incorporating unstructured content, and creating smart apps that work in any environment.
Deliver insights on personalized devices producing an intuitive experience.
Squirrel seeks to simplify the task of discovering insights by bringing to the software developer a templatized design style for answering most common questions involving data science. Templates of readymade functions, bring the agility in developing a solution or a storyline from any data. These templates are grouped into the following function blocks:
Data Acquisition: I/O Blocks provide support for standard input data formats and database connectors
Data Modeling: All data is transformed into a ubiquitous data structure representation. Smart defaults can pipeline the Data Generation process through (one or more of):
o Filtering o Searching o Sorting (for example: understand that “Monday” comes after “Sundaay”) o Slicing and Dicing
- Data Cleansing:
Removing or extracting duplicates, outliers, etc
- Data Visualization: Adaptors for common data visualization
- Statistics and Mathematics: Basic statistical and
Squirrel brings the application closer to the Consumers and Business users by delivering the ability to acquire and visualize data from a variety of sources on personalized devices.
We envision smart abilities in Squirrel that would bring agile data analytic solution development and delivery to near real time.
Squirrel is hosted on github: https://github.com/sudipto80/Squirrel
Experiment, Adopt, Collaborate
Please feel free to download and experiment with Squirrel. And when you do, we would appreciate if you share the link to your github project and your data.
The Squirrel framework development is active! We invite early adopters who can benefit by shaping up the design by requesting features.If you are eager to simplify the solution of your Data Analytics problem we will help you to port your query into Squirrel.
And if you are looking for enhanced or new features please do write to us. And, finally, signing up as a collaborator is easy – just drop an email to:
firstname.lastname@example.org or email@example.com.
Sudipta Mukherjee https://in.linkedin.com/pub/sudipta-mukherjee/11/7b0/239 is the lead author of Squirrel framework. He has keen interest in Data structure, Algorithms, Text processing, Natural Language Processing Programming Language, Tools Development and Game Development.