Cloudera Buys DataPad's Python Chops

In a move to strengthen its enterprise data hub Cloudera announced it has acquired the technology assets of data visualization company DataPad , a startup with deep expertise in using Python for data analytics.

San Francisco startup DataPad launched last year with the vision from co-founders Chang She and Wes McKinney to build better data tools for an integrated, accessible data discovery environment. McKinnery created the open source Python Pandas project, a software library for Python-based data manipulation and analysis.

DataPad launched with a $1.7 million investment from Accel Partners, Google Ventures, Andreesen Horowitz and Ludlow Ventures. DataPad offers automated analytics that connect to a variety of data sources and present visualizations with options for collaboration and optimizations for touch-screen devices.

Big Data analytics and data visualization for Hadoop is an active market right now, and the Cloudera acquisition seems to have been primarily about bringing the data engineering talent from DataPad into the mix at Cloudera.

DataPad specializes in data analysis using the Python programming language. With Python-based tools added to Cloudera's Big Data management and analytics platform the company looks to reach more developers and data scientists and further expand its contributions to open source projects.

Apache Spark was added to Cloudera's enterprise data hub earlier this year, promoting the notion of fast data that is instantly actionable. Cloudera went on to write a Python client for the open source Hadoop query engine Impala.

“We are thrilled to have the DataPad team join Cloudera and look forward to their contributions to the Cloudera roadmap,” said Peter Cooper-Ellis, vice president of engineering at Cloudera. “We’ve long been supporters of the DataPad team and have been impressed with their engineering work. Together, we possess some of the best talent in the data engineering sphere. The deep Python expertise that DataPad brings to Cloudera will further accelerate our data engineering capability.”