Sparkflows Big Data Use Cases

Below are some of the top big data use cases easily enabled by Sparkflows

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Each of these use cases will take less than 30 mins with Sparkflows

Self-Serve Big Data Analytics

Accomplish Big Data Analytics with Drag & Drop with 100+ building blocks

Machine Learning

Perform Machine Learning on huge datasets with drag and drop

NLP  

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Perform NLP on Big Data with OpenNLP & StanfordNLP

Log Analytics

Build Log Analytics Platform with Kafka, Spark, Solr/Elastic Search, Hue

Entity Resolution

Perform large scale Entity Resolution on data from multiple channels

Dashboards

Create Rich Dashboards from the output of various workflows

ETL Pipelines

Build ETL pipelines with ease. Also incorporate SQL, Scala, Jython in it.

Streaming Analytics

Perform Streaming Analytics reading from Kafka,performing complex transformations, generate graphs and write out to Solr, Hbase etc.

OCR

Perform OCR on millions of images with Tesseract

Loading Data into ES, Solr and HBase

Easily load data into Elastic Search, Solr, HBase etc.

Format Conversion

Convert Big Data from one format to another

Hadoop Small Files Problem

        Easily merge small files and create large ones

Self-Serve Big Data Analytics:  Accomplish Big Data Analytics with Drag & Drop with 100+ building blocks

ETL Pipelines:  Build ETL pipelines with ease. Also incorporate SQL, Scala, Jython in it.

NLP:  Perform NLP on Big Data with OpenNLP

OCR:  Perform OCR on millions of images with Tesseract

Streaming Analytics:  Perform Streaming Analytics reading from Kafka, performing complex transformations, generate graphs and write out to Solr, Hbase etc.

Machine Learning:  Perform Machine Learning on huge datasets with drag and drop

Entity Resolution:  Perform large scale Entity Resolution on data from multiple channels

Log Analytics:  Build Log Analytics Platform with Kafka, Spark, Solr/Elastic Search, Hue

Format Conversion:  Convert Big Data from one format to another

Loading Data into Solr, ES and HBase:  Easily load data into Solr, Elastic Search, HBase etc.

Hadoop Small Files Problem:  Easily merge and save large number of small files

Custom Nodes: Create & Use Custom Nodes which add custom features

Dashboards: Combine output of various Workflows/Nodes into a Dashboard