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The Visual Big Data Analytics Platform for Stream Processing and Machine Learning

Visually build and deploy streaming and batch processing use cases rapidly, with best-of-breed open-source technologies, both on-premise and in the cloud.

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The Visual Big Data Analytics Platform for Stream Processing and Machine Learning

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  1. DATA SHEET The Visual Big Data Analytics Platformfor Stream Processing and MachineLearning DATA 360 Visually build and deploy streaming and batch processing use cases rapidly, with best-of-breed open-source technologies, both on-premise and in thecloud. • Dataingestion • Datapreparation StreamAnalytix is a multi-engine, enterprise-grade, visual platform for unifiedstreamingandbatch dataprocessing,and machinelearning. • Analytics • Machinelearning Use big data compute engines like Apache Spark (and more) as the underlying technology to ingest, blend, and processhigh-velocity big data streams as they arrive, run machine learning models, train and refreshmodels inreal-time orinbatch mode,visualizeresultson real-time dashboards, and raise corresponding real-time alerts and action triggers. • Actiontriggers • Storage andvisualization MULTI- ENGINE SUPPORT WhyStreamAnalytix? Build and operationalize Apache Spark (and engines like Storm,Flink, and TensorFlow) based applications five to ten times faster using an intuitive drag-and-drop interface, an exhaustive set of pre-built operators, full application lifecycle support, and one-click options for on-premise and clouddeployments Build bigdata applications 5x to 10xfaster Boost performanceby 4x using the same infrastructure Process 1Mn+ events per second–both on-premise and inthecloud Have yourexisting team work at 3x efficiency © 2018 Streamanalytix • www.streamanalytix.com

  2. USE CASES StreamAnalytixFeatures Unified batch and streaming dataprocessing • Real-timecustomer-360 • Call centeranalytics Ingestand blenddataatscale fromanydatasource–batchorstreaming. • Real-time churnanalytics With built-in support for Spark structured streaming, StreamAnalytix enables continuous applications by exposing a single API to write streaming as well as batchqueries. • Next bestoffer/action • Insider threatdetection • Credit card transactionprocessing • Fraud and riskanalytics Usemodelstrainedand refreshed inbatchworkflowstomake predictions on real-time datapipelines. • Cyber securityanalytics • IoT, sensor data, loganalytics • Anomalydetection Click +code Useanexhaustivesetofpre-integrateddrag-and-dropoperatorsinan intuitive visual interface. Or introduce custom logic in the language of your choice (Java, Scala, orPython). • Predictivemaintenance • Telecom networkanalytics Figure 1: Build big data applicationsvisually End-to-end dataprocessing An end-to-end big data processing platform, StreamAnalytix supports data ingestion, data preparation and processing, analytics, machine learning, action triggers, data visualization, and datastorage. Data ingestion: Connect withanydatasourceorstoragesystemforboth streaming and batch use cases on-demand. Use pre-built connectors or create your own using custom API. Ingest and output popular data formats like JSON, CSV, AVRO, and Parquet, or easily build your parsers for custom dataformats. 2 The Visual Big Data Analytics Platform for Stream Processing and Machine Learning • © 2018 Streamanalytix • www.streamanalytix.com

  3. BUILT- IN OPERATORS AND CONNECTORS Data preparation and processing: Perform data cleansing, data blending, and data enrichment at scale–on data as it arrives. Experience significantly faster processing with Spark-based structured streaming as the underlyingtechnology. Connect with any data sourceand sink−batch orstreaming Analytics: Apply built-in analytical operators for complex event processing, time window aggregation, geospatial analytics, correlation, andmore. • Messagequeues o Kafka, RabbitMQ, MapRStreams, MQTT, and more Data science and machine learning: Use advanced analytics and machine learning operators at scale like Spark MLlib and ML, model-porting standards like PMML, H2O, and TensorFlow oreasily prototype your custom algorithm. Train and refresh modelsin both batch mode and on real-time data. Apply A/B testing and use ‘Champion Challenger, Hot Swap’ paradigms to swap the best performingmodel. • Distributedfilesystems o HDFS, Hive, andmore • Cloudsources o Amazon S3, Redshift, Amazon Kinesis, AWS-IOT, Azure Event Hub, andmore Actionsand alerts:Set thresholdsforkeymetricsand corresponding real-time alerts and actiontriggers. • No SQL o HBase, MongoDB,Cassandra, Couchbase, andmore Data visualization: Use inbuilt or custom real-time dashboards to display the status of metrics and key performance indicators for a pipeline. Blend real-time and historicaldatawithofflineanalytics andintegrate everything you must keep track of,however disparate, onto a single screen. • RDBMS o Oracle, Hive, SQLServer, and more • Indexingstores o Elasticsearch,Solr • Custom channel Dataprocessors • Datatransformation o Masking, MapToPair, FlatMap, StreamCorrection,Deduplication, TransformByKey, andmore • Data cleansing o Filter, Imputation,Take • Datablending Figure 2: Built-in real-timedashboards o Join, Union, Intersection,Group, Intersection Data storage: Sinkdatainvariousstreamingandbatch datastorage systems • Data enrichment o Enricher, LookUps, Web-services, Expressions, SQL 3 The Visual Big Data Analytics Platform for Stream Processing and Machine Learning • © 2018 Streamanalytix • www.streamanalytix.com

  4. • Statistical andtemporal analytics Application lifecycle management Build applications end-to-end with support for the entire application delivery lifecycle; design, build, test, debug, deploy, monitor, and manage. o Aggregation, Average, Count, CEP, Window, Standard Deviation, and more Rapid application development: Rapidly build and operationalize applicationsusingapowerfulvisualpipelinedesigner, anda drag-and- dropinterface. Predictive analytics, machinelearning, and deeplearning • SparkMLlib Interactive pipeline designing: Create schema automatically within pre-builtoperators.Datacan be accessed from a datastoragesystem, or configured from a source such as Kafka, JDBC and more and is automatically examined and assigned for each field andcolumn. • SparkML • H2O • TensorFlow • PMML Debug: Trace and debug messages at each step, from entry to exit, during both the development phase of your pipelines as well as during production. • Custom processing (using Pythonor R) Extensibility Retain full control and flexibility to add new functionality and interfaces as the technology ecosystemevolves. • Extensions API, custom data operators, custom processors (Spark and Storm operators), custom machine learning(Python and R) Figure 3: Data inspect feature ofStreamAnalytix One-click deployment –on-premise or in the cloud: Use one-click deployment options to deploy applications on-premise or on apublic cloud. Application diagnostic tools: Allows auto ‘Data Inspect’ before and after the use of every individual operator, for an end-to-end view of data transformation at every step. And use ‘Data Lineage’ for your production-deployedpipelines. 4 The Visual Big Data Analytics Platform for Stream Processing and Machine Learning • © 2018 Streamanalytix • www.streamanalytix.com

  5. PARTNERSHIPS Key Hadoopdistributions Cloud platforms Figure 4: Data lineage feature ofStreamAnalytix Versioning: Create and save new versions of your data pipelines. Roll backchangesconvenientlyby reverting toanolderversion. Key third partypartnerships Performance monitoring:Run streaming and batch pipelines regularly and consistently once they are in production. Get all performance metrics in real-time, through interactivegraphs. Workflow orchestration: Enable integration of multiple pipelines to workinsync,and supportparallelstitchingforlogicevaluationbeforeit is put inproduction. Open sourceflexibility Work with the power and flexibility of best-of-breed open source technologies integrated into a high-performance, scalable, and reliable enterprise-gradeplatform. Multi-engine support Leverage big data compute engine Apache Sparkand other engines including Apache Storm, Apache Flink, Tensorflow andOozie. Use multiple engines in a single pipeline; build and interconnect application sub-systems that each leverage the best suited engine with a consistent userexperience. 5 The Visual Big Data Analytics Platform for Stream Processing and Machine Learning • © 2018 Streamanalytix • www.streamanalytix.com

  6. STRONG INDUSTRY RECOGNITION Built-in extensibility With the extensions API exposed by the platform, you can writeyour functionality in the language of your choice (Java, Scala, SQL, and Python), and make it available for all users across theplatform. The Forrester Wave™:Streaming Analytics,Q3'17 One of the 13 Most Significant Streaming AnalyticsProviders Self-service Use pre-built templates for frequently used application patterns and customize them to your needs. Easily access support and tutorials at everystep. Market Guide for Event Stream Processing, 2017-GartnerResearch Strong ecosystemintegration Compatible and integrated with leading big data technologies and platforms such as all key Hadoop distributions (Map R, Hortonworks, Cloudera), cloud platforms (AWS, Microsoft Azure), and key third party partnerships. One of the Key EventStream ProcessingPlatforms StreamAnalytix Lite–A Visual IDEfor ApacheSpark HotVendorsinStreamingAnalytics, 2017-Aragon Research StreamAnalytix Lite is a free, compact version of the StreamAnalytix platform. A light–weight visual integrated development environment (IDE), StreamAnalytix Lite offers you a full range of data processing and analytics functionality to build, test, and run Apache Sparkapplications on your desktop or any singlenode. One of the 4 Hot Vendors in the Streaming Analytics Space in2017 • Build and run enterprise-grade Apache Spark applicationson yourdesktop Datanami Editors' ChoiceAward Best Big Data Product or Technology: Real-timeAnalytics • Useawide rangeofbuilt-in operators,and anintuitive drag-and-drop interface to build Apache Spark pipelines within minutes, without writing a single line ofcode • Use built-in advanced analytics andmachine learning capabilities • Use powerful multi-tenancyfeatures • Quick-start with a light-weight tool, downloadable onto your Windows, Mac, or Linux desktop, or a servernode Click here to download StreamAnalytix Lite forFree. 6 The Visual Big Data Analytics Platform for Stream Processing and Machine Learning • © 2018 Streamanalytix • www.streamanalytix.com

  7. StreamAnalytixArchitecture StreamAnalytixintegrates various key big data technologies, including support for multiple big data compute engines, a powerful array of pre-built connectors and operators to multiple systems, and functional extensibility for futurereadiness. ADMINISTRATION APP LIFE CYCLE APPLICATION UI DATA CAPTURE DOWNSTREAM APPS DATA SINKS DATA SOURCES VISUAL BIG DATA ANALYTICS PLATFORM JSON AVRO ANALYTICS AND MACHINE LEARNING DATA PROCESSING ANDPREPARATION Delimited XML Custom CHANNELS Classification | Recommendation Complex Event Processing EMITTERS Clustering | NLP |AnomalyDetection Temporal | Geo-Spatial | Correlation Filtering | Transformation | Enrichment Regression | Market BasketAnalysis Custom Java | Custom Scala | Spark SQL Spark MLLib Spar k ML H2O PMML TensorFlow OPEN SOURCE BIG DATA PROCESSINGENGINES HADOOPDISTRIBUTION NATIVE OS/ VM / CLOUD © 2018 Impetus Technologies, Inc. All rights reserved. Product and company names mentioned hereinmay betrademarks of their respective companies. Aug2018 StreamAnalytix is an enterprise grade, visual, big data analytics platform for unified streaming and batch data processing based on best-of-breed technologies. It supports the end-to-end functionality of data ingestion, enrichment, machine learning, actiontriggers, and visualization. StreamAnalytix offersan intuitive drag-and-drop visual interface to build and operationalize big data applications five toten times faster,across industries,dataformats, and use cases. open source Visit www.streamanalytix.com or write to us atinquiry@streamanalytix.com

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