Data Science Platforms News Roundup, August 2018
On a monthly basis, I will be rounding up key news associated with the Data Science Platforms space for Amalgam Insights. Companies covered will include: Alteryx, Anaconda, Cloudera, Databricks, Dataiku, DataRobot, Datawatch, Domino, H2O.ai, IBM, Immuta, Informatica, KNIME, MathWorks, Microsoft, Oracle, Paxata, RapidMiner, SAP, SAS, Tableau, Talend, Teradata, TIBCO, Trifacta.
Alteryx announced the availability of Alteryx 2018.3, including the new Visualytics capability, hinted at during Alteryx Inspire. Visualytics provides inline visual data cues throughout the analytics workflow, helping end users to identify outliers and better understand their data within the modeling process. This also extends to the new Interactive Chart Tool, which creates Visualytics outputs such as charts and graphs for sharing. With the debut of Visualytics, Alteryx builds on its reputation for easy-to-use interfaces that emphasize intuitive understanding of the data being analyzed.
Anaconda announced that it has secured an undisclosed amount of funding from Citi Ventures. Crunchbase lists a convertible note for $5M for Anaconda by Citi Ventures as the latest round of funding. Anaconda has not confirmed the type or amount of funding, nor provided significant details on their intentions to use said funding.
DataRobot Announces Automated Time Series Solution that Allows Frontline Business People to Predict the Future
DataRobot announced the general availability of DataRobot Time Series, an add-on to its DataRobot Cloud and Enterprise products that automates time-series modeling. DataRobot Time Series has its origins in the May 2017 acquisition of Nutonian; integrating Nutonian’s Eureqa modeling engine into the DataRobot product line resulted in DataRobot Time Series. Time-series modeling has historically required significant expertise to model correctly; DataRobot TimeSeries allows line of business users to create predictive models using time-series data without needing significant technical knowledge in areas typically required such as data science, coding, or manual forecasting.
Domino Data Lab announced that it has secured $40M in a funding round led by Sequoia Capital and Coatue Management. Domino had been anticipating seeking another funding round in early 2019, but the early funding will permit Domino to focus on direct sales and product enhancements, as well as strengthen and expand upon strategic partnerships more rapidly like the partnership Domino has with SAS Analytics.
Oracle introduced GraphPipe, a high-performance protocol to transmit tensor data across a network. This will permit easier deployment and querying of machine learning models. I explain GraphPipe in more depth in Oracle GraphPipe: Expediting and Standardizing Model Deployment and Querying.
RapidMiner announced the availability of RapidMiner 9 with TurboPrep. The new TurboPrep product, which is included with paid and academic RapidMiner Studio licenses, accelerates the task of data preparation in a simple point-and-click interface, and allows users to create and save specific data prep processes as macros that can be easily reused in the future. Combined with RapidMiner’s AutoModel, this expedites the overall data science process, letting analysts without significant statistics or coding skills swiftly prepare data and then create machine learning models based on that data.