IBM Releases Apache Spark Powered Data Science Collaborative Platform
|Richard Harris in Analytics Thursday, June 9, 2016|
IBM has announced a new cloud-based development environment for near real-time, high performance analytics. The new IBM Data Science Experience is an interactive, collaborative, cloud-based environment where data scientists can use multiple tools to activate insights. It is in limited preview and IBM has created a waiting list for individuals interested in accessing the platform.
Available on the IBM Cloud Bluemix platform, the Data Science Experience provides 250 curated data sets, open source tools and a collaborative workspace to help data scientists uncover and share insights with developers, making it easier to rapidly develop applications that are infused with intelligence.
The new offering is part of IBM’s investment in developing Apache Spark as a type of “analytics operating system.” IBM created the Data Science Experience to extend the speed and agility of the analytical process through new contributions to SparkR, SparkSQL and Apache SparkML. As a result, data scientists who work in R will have faster access to more data.
The Data Science Experience’s environment allows data scientists to accelerate and simplify data ingestion, curation and analysis by bringing together content, data, models, and open source resources from IBM and others including H2O, RStudio, Jupyter Notebooks on Apache Spark in a single security-rich managed environment.
IBM is collaborating with data science organizations including Galvanize, H2O.ai, LightBend and RStudio to promote an integrated and unified data science ecosystem. IBM is also joining the R Consortium to help accelerate data science’s readiness for the enterprise.
On top of the development of open source capabilities, IBM is adding new features and APIs which include:
- Sparkling.Data: Cleaning and preparing data for analysis are the tasks that data scientists typically spend the majority of their time on. IBM created a library that helps users discover the different file types and returns a data frame loaded with data (by default) from the file type that occurs the most. It can be used to infer the schema, discover data types, profile data sets, view range and distribution, reveal and fix bad data, and more.
- Prescriptive Analytics: The Decision Optimization CPLEX Modeling library (DOcplex) contains modeling packages such as Mathematical Programming and Constraint Programming.
- Data Connections: From the Notebook interface, users can set up data connections to Bluemix data services like Cloudant or dashDB or to on-premises or external services.
- Schedule Jobs: The Notebook interface provides the ability to schedule jobs to run periodically.
IBM has contributed to related projects including Apache Toree, EclairJS, Apache Quarks, Apache Mesos, Apache Tachyon now called Alluxio, and major contributions to Apache Spark sub-projects SparkSQL, SparkR, MLLib, and PySpark with over 3,000 total contributions in the last year.
In addition, IBM has built Spark into the core of its platforms including Watson, Commerce, Analytics, Systems, Cloud as well as more than 30 offerings including IBM BigInsights for Apache Hadoop, IBM Analytics on Apache Spark, Spark with Power Systems, Watson Analytics, SPSS Modeler and IBM Stream Computing. IBM also open-sourced its SystemML machine learning technology to advance Spark’s machine learning capabilities in 2015.
Read more: http://datascience.ibm.com/