Easy Big Data Analytics:
Actian RushAnalytics

Whether your data resides in flat files, SQL, NoSQL, HBase or the Hadoop filesystem (HDFS), we’ve got you covered.  Actian RushAnalytics for KNIME can read and write any combination of sources simultaneously. 

Before analysis, do you need to check aspects of your data against business rules? Check mins, maxes, averages and such?  RushAnalytics takes the limits off the number of metrics you can check without bogging down your hardware. 

Need to find matches in data with similar names, addresses, ID numbers?  The DataMatcher operators let you build a high speed solution for matching inaccurate, inconsistent and duplicate data.  

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RushAnalytics also lets you fill in missing values, sort, aggregate, and any other transformations needed to improve data quality and prepare the data you need, whether its in a data store or streaming by on the fly. Whether you're dealing with big data or a normal analytics data set that might laughably be called "small," you can crunch through it fast, so you can move on to the real work. Analysis.

Details on Data Preparation

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The analytics library in Actian RushAnalytics for KNIME supports data mining for marketing, surveillance, fraud detection, cybersecurity and scientific discovery. Build a recommender system, market basket analysis, risk analysis, customer churn analysis and more using the RushAnalytics extensive library of analytics operators.

• Association Rule Mining, Affinity Analysis
• Classifiers: Decision tree, K-nearest-neighbors, Naïve Bayes, Support Vector Machine (SVM)
• Clustering: Recommender learner and predictors, k-means
• Feature Selection: Principal Component Analysis (PCA)
• Regression Analysis

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Actian RushAnalytics for KNIME integrates directly with existing toolsets to solve problems requiring greater analysis performance speed. RushAnalytics uses the industry standard Predictive Model Markup Language (PMML) as either an input or output format. Take models developed in SAS, SPSS and other tools that use PMML and run them using RushAnalytics highly parallelized operators to get answers faster, without buying more hardware. Or use the intuitive design interface and rapid test and iterate cycle of Actian RushAnalytics for KNIME to generate the models, then visualize them in another tool that uses PMML. 

Working with R for statistical computing? RushAnalytics can do the heavy lifting on big data preparation and flow the output straight to your R code, vastly reducing overall execution time. You can also run snippets of R code as just another step of the workflow, open R views or even learn models within R. Be sure to compare R analytics operators with similar RushAnalytics operators. In many cases, the dataflow optimized operators will give you an equally accurate answer in far less processing time.

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Display data graphically in interactive charts that can be modified on-the-fly.  Scatter plots, line graphs, bar graphs, dashboards and more allow visualizing data to instantly spot trends. BIRT and R provide a wide variety of open source options.

Output in PMML or through JDBC to visualization tools like Tableau, Actuate, etc. to expand your visualization options without limits.

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Actian RushAnalytics for KNIME is built on the patented Actian DataRush data processing engine to ensure scalability and future-proof your analytics workflows.  Actian DataRush automatically detects and utilizes all cores and nodes available at runtime up to a settable limit. Execution moves seamlessly from desktop to server to cluster, without the need to modify code, re-design models, or recompile.  

For example, an application written on a 4-core desktop will automatically scale to take full advantage of the additional resources when installed on a 16-core server or a cluster of 100 8-core machines. Every bit of current hardware will be used to it's fullest capacity, nothing wasted. Organizations can simply add more compute resources to keep up with growing data volumes over time.

Read more about Actian DataRush

If you need to run on Hadoop, Actian supports all major Hadoop distributions

• Apache
• Cloudera
• Hortonworks
• MapR
• IBM BIgInsights

For more information, see our Hadoop Solutions page. 

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Do you need an analytics or data preparation operator that Actian didn't build?

Actian RushAnalytics includes a full set of data preparation and analytics operators that, because they are built directly with the DataRush engine API in a dataflow paradigm, are fully optimized for running on multi-core and distributed systems, providing automatic scaling and extreme levels of performance.

If you need a parallel optimized dataflow operator that is not included in RushAnalytics, you can use the DataRush API to develop custom operators using the very simple DataRush Javascript interface, or any other JVM language, including Java, JRuby, Groovy, Jython, Scala, etc. Your custom operators will have the same automatic scaling and extreme performance, thanks to the DataRush dataflow framework.

Also, keep in mind that standard KNIME operators can be mixed with the Actian DataRush parallel optimized operators in RushAnalytics to give a tremendous breadth of functionality. Most of these KNIME created, or KNIME partner created, operators are not optimized for multi-core or parallel execution. However, KNIME and Actian partnered to help improve the KNIME API to allow KNIME operators to be made "flowable" so that they can take advantage of some of the speed of the DataRush dataflow processing framework.

If you need just Actian created operators, or a mix of KNIME and Actian created operators, or a mix of both plus some custom operators that you build yourself,  then you need Actian RushAnalytics. It includes everything.

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If you are already using KNIME, have all the operators you need, do not need to run in a distributed environment, and you just want to add some of that DataRush processing speed increase, try Actian RushAccelerator for KNIME.  It includes only the DataRush engine that plugs into KNIME. RushAccelerator will only increase performance on flowable KNIME nodes.