Faster Self-Service Advanced Analytics for Hadoop in RapidMiner
Faster Self-Service Advanced Analytics for Hadoop in RapidMiner : RapidMiner, the easiest-to-use Modern Analytics platform, provided significant updates to the comprehensive advanced analytics offering. Most analytics vendors extract data from Hadoop to build and score analytic models. Moving Big Data out of Hadoop reintroduces bottlenecks and increases complexity. Only a few analytics vendors push down analytics computation to Big Data in Hadoop. RapidMiner pushes the computation of more than 250 machine learning models directly to the data in the cluster, making it easy to deploy powerful predictive analytics into production inside Hadoop. RapidMiner is an easy to use Modern Analytics platform that significantly accelerates productivity from data prep to predictive action with prebuilt models and one-click deployments. RapidMiner is code-free advanced analytics platform available commercially that can execute analytical processes in-memory, in-Hadoop, in-Cloud, in-Stream and in-database.
“With our pushdown Hadoop processing in RapidMiner Radoop, combined with our recent announcement of RapidMiner Streams, it’s easy to see that we are quickly turning dormant data lakes into money-making machines where enterprises can maximize the business value from their data,” said RapidMiner CEO and Co-founder Ingo Mierswa. “Predictive analytics is no longer a nice-to-have competitive advantage. It’s an absolute business necessity. Nobody else offers what RapidMiner does, and our latest release establishes us as the de facto modern analytics platform.”
RapidMiner’s new in-Hadoop Model Scoring Delivers Up to 20x in Performance Compared to Legacy Hadoop Model Scoring. RapidMiner Radoop, which automatically creates an optimized analytic execution plan based on the unique Hadoop cluster configuration, now integrates machine learning algorithms from MLlib, Apache Spark’s machine learning library. This RapidMiner Radoop release includes push down processing for logistic regression and decision tree algorithms that can be trained natively in Hadoop, making use of the full distributed computation power of Spark in a Hadoop cluster.
Data security is top of mind for enterprises worldwide. This crucial business requirement typically delays analysis, but not with RapidMiner. RapidMiner Radoop easily integrates with Kerberos authentication securing Hadoop clusters, making it easy to perform large-scale data exploration, model building and model scoring while complying with well-adopted security standards.
RapidMiner continues to differentiate itself from other advanced analytics providers by offering a guided approach to building predictive analytics based on the wisdom gleaned from the 250,000 member strong RapidMiner community. The analytic best practice, or wisdom from the crowd, is mined via RapidMiner machine learning to recommend how to best build a predictive model. As users are always experimenting and learning, the latest innovations happening in the community are offered up as recommendations. This unique feature, which leverages the power of the RapidMiner community to create recommendations, makes it easier to develop more accurate predictive models, no matter how sophisticated the end user may be.
The new release now includes context-aware recommendations, resulting in more relevant and focused guidance. Context awareness gives RapidMiners better understanding of how other users are solving similar problems, and by tapping into that wisdom, offers up better recommendations that accelerate their time-to-value. The new platform also now includes recommendations for parameter settings. Tuning parameters is critical when developing analytic work flows and is a notoriously tedious and difficult task, especially for beginners. The RapidMiner parameter recommender uses the knowledge and experience of the community to recommend and fine tune parameters which improves the model accuracy and results.
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More Information on Predictive Analysis Process
For more information of predictive analytics process, please review the overview of each components in the predictive analytics process: data collection (data mining), data analysis, statistical analysis, predictive modeling and predictive model deployment.