Analytics Application

Create  Analytics Apps for Enterprise

How the Analytic Application of  Sparkflows becomes a Performance Management

Fire Insights enables you to quickly develop an Analytics Application for analysis of a buisness process like Sales Pipeline Analysis, Accounts Payable Analytics or risk adjusted profitable analysis in support of decision making.

With Application Analytics, build a user interface screen with drag & drop and add various stages to execute the whole application.

Build UI with Drag and Drop
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Adding Stages
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Various stages can be added and they can also be rearranged using the drag and drop.

Upload Stage

Add column component and divide in two columns.

Parameters Stage


Add select, text-field, select boxes, buttons etc. components.


Run Stage

Execute the notebook with all parameters added in the App.

Integrate with Databricks Notebook
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Example of Housing Analytics

The Web App in Fire Insights can trigger a Notebook in Databricks.

Fire Insights passes 2 parameters to the Notebook:

  • postback-url

  • job-id

Add wheel file to your Databricks Notebook

Add the wheel file to your Databricks Notebook. This is to enable using the Fire Insights API’s for sending data to it.


Outputing details to Fire Insights

The Databricks Notebook can output text, tables and charts to be dispalyed in Fire Insights.

Running Analytics App
run analytics app.png

Once the Analytics App has been created, they can be executed

with the below steps for executing an Analytics App.

  • Click on Analytics App Name

  • Go through the various stages




Enterprise Scalability

Easily scale horizontally to petabytes of data. Sparkflows also allows you to control the persistence level of DataFrames, execution parameters etc. to ensure you are not limited in any way.

Sparkflows processors are written to run at extreme scale. Save millions of dollars by running faster with efficient algorithms.

Deploy and Run
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Run your workflows with one click, schedule them or trigger them by event. Easily view the results of past executions.


Or run them with the scheduler of your choice as Sparkflows is an open system.

Save, Load and Deploy your ML models.


Sparkflows is a collaborative data science and analytics platform. Teams can work together to build Applications. Data Analysts, Data Scientists and Data Engineers can iterate, build and deliver data products seamlessly.

 Multiple groups with different permissions can work together on an Application in Sparkflows. From preparing data to analytics to building predictive models to visualization and dashboards, users can seamlessly accomplish them in an Application.


Integrate with your other systems using the powerful REST API's. Create workflows, run them, view models and execution results using the REST API's.

Perform Predictive Analytics

Define Dataset

Prepare Data

Perform Analytics

Build Models

Deploy & Run

Get Started

Contact us for a demo

Download Fire Insights

Get started with our tutorials

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Random Forest, GLM, GBM, XGBoost, GLRM, Word2Vec and many more.

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SVM, Nearest Neighbors, Random Forest, SVR, Ridge Regression, Lasso, K-Means, Spectral Clustering, Mean-Shift and many more.

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Provides fully managed Machine Learning System.

Apache MXNet, TensorFlow, PyTorch, and Chainer.Scikit-learn and SparkML by providing pre-built Docker images.

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