RapidMiner Radoop

    Feature List

    RapidMiner Radoop extends common RapidMiner in-memory functionality by providing sophisticated operators that are implemented for in-Hadoop execution. Radoop includes more than 60 operators for data transformations as well as advanced and predictive modeling that run on a Hadoop cluster in a distributed fashion.

    • Easy to maintain and develop Visual Programming Environment

    • Integration of SparkR scripts running on your own environment within the visual processes

    • Integration of PySpark scripts running on your own environment within the visual processes

    • Automatic Execution of Analytic Workflows into Hadoop (run the process where the data is)

    • Purely functional operators for data access, data preparation and modeling. The technology becomes transparent.

    • Supports Cloudera, Hortonworks, Amazon EMR, Apache, Microsoft’s Azure HDInsight (Other Hadoop distributions may be integrated by specifying the proper libraries and dependencies)

    • Supports Kerberos authentication

    • Supports data access authorization employing Apache Sentry & Apache Ranger

    • Supports HDFS encryption to seamlessly integrate with data security policies

    • Supports Hadoop impersonation

    • Transparent data exchange between local memory and cluster

    • Push any RapidMiner operator or subprocess (including extensions) down to Hadoop and execute in a parallel way

    • Supports Hive on Spark and Hive-on-Tez

    • Smart optimization of processes by grouping requests and reusing Spark containers as much as possible

    • Visualization of sampled Hadoop data within Studio

    • Easier-than-ever configuration: All the Hadoop and Spark settings and variables can be automatically imported by Radoop

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    ETL Capabilities

    • Read, store and append from and to Hive tables

    • Read CSV (from HDFS, Azure Blob or Datalake, Amazon S3 or local filesystem)

    • TEXTFILE, ORC, SEQUENCEFILE, PARQUET and RCFILE formats supported

    • Select Attributes, Sample, Filter Examples and Ranges: select a subset of the data according to various criteria and drop non-matching records and attributes Sample

    • Generate Attributes, Generate ID, Generate Rank: define new attributes with more than a hundred functions including mathematical and string operations

    • Aggregate: calculate aggregate values like averages and counts

    • Join: combine multiple data sets based on simple or
      complex keys

    • Sort: order data sets according to different attributes

    • Normalize: transform numeric values to fix ranges or variances

    • Pivot Table: summarize data and change table representation Replace: replace specific values and fix wrong data formats
    • Replace: replace specific values and fix wrong data formats
    • Replace and Declare Missing Values: handle missing values in various ways
    • Remove Duplicates: remove duplicate records that got there by error
    • Split Data, multiply: branch the process or partition the data
    • Store, Materialize, Append, Union: store and combine data results in Hive or Impala
    • Drop, Rename, Copy Table: manage Hive or Impala tables
    • Loop and Loop Attributes: organize loops for fixed iterations or over the attributes
    • Hive Script and Pig Script: implement custom data transformations in HiveQL or Pig

    Modeling

    • K-Means clustering

    • Principal Component Analysis

    • Correlation and Covariance Matrix

    • Naive Bayes

    • Logistic Regression

    • Decision Tree

    • Split Validation: evaluate model performance