sparkflows/fire repository overview

What is Fire Insights:

Fire Insights is a platform that provides comprehensive tools for data visualization, advanced analytics, machine learning, and Generative AI. It streamlines the process of exploring, analyzing, and gaining actionable insights from large datasets, helping users make data-driven decisions more efficiently. With its intuitive, visual interface, Fire Insights is accessible to both Data Scientists and Business Analysts, enabling self-service analytics without the need for extensive coding.

Fire makes it incredibly fast and easy to do Self-Serve Data Preparation and Advanced Analytics. With the power of Fire at your hands, seamlessly find value from your data and scale to Petabytes of data.

Install on the cloud, on-premise or even on your laptop. Fire seamlessly integrates with the most complex of Enterprise Environments.

Fire provides the following features:

• Connect to various data source
• Perform ETL/Data Preparation
• Profile and Clean Data
• Measure Data Quality
• Build ML Models using various ML engines
• Deploy and execute the ML models
• Build Reports and Dashboards
• Build Analytical Applications

More info at https://www.sparkflows.io⁠

What is this image:

It supports Spark and Pyspark engines with self-serve capability.

How to use this image:

To run -

There are two images that gets pushed out:

• One with just the Java engine - This image is a smaller image with core functionalities embedded in it. These images are named as 3.2.1_3.1.0_XXX
• Another with Java and Python engine - This image has all the features that Fire offers and is bigger in size. These images are named as py_3.2.1_3.2.XX

To run the smaller image, the steps would be:

• docker pull sparkflows/fire:3.2.1_3.1.0_XXX
• docker run -p 8080:8080 -p 8443:8443 -e KEYSTORE_PASSWORD=12345678 -e FIRE_HTTP_PORT=8080 -e FIRE_HTTPS_PORT=8443 sparkflows/fire:3.2.9_3.2.1

To run the bigger image with all the functionalities, the steps would be:

• docker pull sparkflows/fire:py_3.2.1_3.1.0
• export FIRE_ROOT=/home/{username}/fire
• docker run -p 8080:8080 -p 8443:8443 -v $FIRE_ROOT:/usr/local/fire-3.1.0_spark_3.2.1 -e KEYSTORE_PASSWORD=12345678 -e FIRE_HTTP_PORT=8080 -e FIRE_HTTPS_PORT=8443 sparkflows/fire:py_3.2.1_3.1.0

For the h2db to be accessible on the mounted directory, please edit the path in conf/db.properties to working directory and restart docker image::

• spring.datasource.url = jdbc:h2:file:./firedb (By default it would be ~/firedb)

Fire would be accessible at: http://localhost:8080⁠

One can login using the following default users:

• Username : admin
• Password : admin

OR

• Username : test
• Password : test

Other helpful docker container commands:

• docker ps -a (to view all of your containers)
• docker start containerid (to restart a container)
• docker attach containerid (to attach to a running container)

Resources:

For General Info: https://www.sparkflows.io/⁠

For Technical Info: https://docs.sparkflows.io/en/latest/⁠

For Q&A: https://www.sparkflows.io/forum⁠