Dataiku (www.dataiku.com), a software developer firm, has released the latest version of Data Science Studio (DSS), a software platform that combines all of the steps and big data tools necessary to build highly specific services that turn raw data into impactful business solutions. DSS 2.1 has an array of brand new features and with so much cool new stuff, there is no need to be modest.
Better Graphs and Charts
One of the first improvements is DSS’s Charts module. The entire visual interface has been redesigned to make it easier for users to get the precise data visualization they want. Users will find a greater number of new charts, therefore enabling them to visualize data and analysis results in the most comprehensive ways.
Writing code can be difficult and time consuming, especially for less experienced developers. Well, DSS makes writing code a lot easier. Developers can use DSS notebooks (iPython, SparkR, R, Hive, Impala) with code samples. Simply put: the newest additions will help you use, create, and share your own snippets. Also, Dataiku has added pre-written code-snippets in the Studio so that users can get started coding immediately with common functions.
The latest version of DSS is without a debt more connected. How so? One answer: plugins. Certified plugins will enable users to connect to all of their data sources, add custom code snippets, and enrich their data with APIs. Anyone can now develop their own plugins easily and add them to the community platform. Data teams and entire companies can now easily collaborate and share plugins and custom code to speed up the development of projects.
The freedom to do exactly what you want with your data
What you will do with your data is entirely up to you. In DSS 2.1, create your own projects, folders, transform your data sets into editable datasets, add notes about your data, and write comments about a client contact or transaction. And that’s just a brief glimpse.
Apache Spark Integration
Finally, DSS 2.1 is integrated with the power of Apache Spark. Paring the capabilities of Spark with the advanced analytics features of DSS creates significant opportunities for those looking to leverage very large Hadoop data sets, often ranging into the terabytes, and it also allows users to process that information much more quickly.