Using Fire on AWS

Fire integrated and certified on AWS

Fire Insights is deeply integrated and certified on AWS. It can be installed on an EMR cluster or on a standalone machine on AWS.

 

It can process data from S3, Redshift, Kinesis etc. 

Detailed documentation of the integration is available at :

 

http://docs.sparkflows.io/en/latest/aws/index.html

Integration with EMR

 

 

Fire can be easily installed on an AWS EMR Cluster. Fire can be installed on the master node of an EMR cluster. It would then submit the jobs to the EMR cluster.

Integration with S3

Fire Insights allows you to access your files on S3. The jobs run by Fire can read from and write to files on S3. The files can be in various file formats including CSV, JSON, Parquet, Avro etc.

Fire also allows you to browse your files on S3.

Integration with Sagemaker

Fire is fully integrated with AWS SageMaker. Fire provides a number of processors for doing model building with SageMaker.

These include:

  • KMeansSageMakerEstimator

  • XGBoostSageMakerEstimator

  • LDASageMakerEstimator

  • LinearLearnerBinaryClassifier

  • LinearLearnerRegressor

  • PCASageMakerEstimator

  • SaveSageMaker

Integration with Redshift

Fire is fully integrated with Redshift. Fire has processors for reading from and writing to Redshift. They include:

  • Read Redshift AWS

  • Write Redshift AWS

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