COMPONENTS INCLUDED: - Native connectivity and bulk-loading to most common data sources, including Amazon Redshift and Snowflake - Data services to virtualize transformations without staging, making data sets immediately available to reports and applications - Process streaming data in real time. - Native Containerization to support multi-cloud deployments KEY FEATURES: - Code-free data transformation design that empowers 15x faster productivity versus hand-coding and executes in-cluster for high performance - Template-based approach to rapidly onboard data sources into Hadoop via metadata injection feature set - Ability to seamlessly switch between execution engines, such as Spark and the Pentaho native engine, to fit data volume and transformation complexity - Support for advanced analytics models from R, Python, Scala and Weka to operationalize predictive intelligence while reducing data prep time - Robust Dataflow Orchestration of pipeline - Support both structured and unstructured data TOP BENEFITS: - 50% Less Implementation Time - onboard multiple hundreds of varied on-premises, cloud, and edge data sources efficiently and quickly with modern data integration. - 3x Improve Pipeline Quality - No-code functionality versus hand-coding data pipelines using an easy drag and drop interface for on-premises and cloud Hadoop data lake integration. - 50% Faster Data Delivery - Produce stellar reports with intuitive processes and seamless integration that enhances the user experience, reduce data management costs, and create business value TOP USE CASES: - Onboard, prepare, filter data pipelines at scale combining data from both on-premises and cloud-based data sources - Cloud Data migration across hybrid cloud environments - Modernize and process large data sets in a data lake and data warehouse - Intelligent cloud automation for rapid value creation