Collaborative Self-Serve Advanced
Analytics with Sparkflows + AWS
Perform Data Analytics, Data Exploration, build ML models
and Data Engineering in minutes using the 330+ Processors in Fire Insights
Sparkflows is deeply integrated with and certified on AWS. It can be installed on an EC2 machine and can run in standalone more or submit the jobs to EMR or AWS Glue. It can process data from S3, Redshift, Kinesis etc. Detailed documentation of the integration is available at:
Build and Run Analytics and ML jobs on EMR or standalone machines.
Seamlessly read files from
S3 and process them.
Send data to and build ML
models on Sagemaker
Read and Write data to Redshift.
Read and process streaming
data from Apache Kafka and Kinesis.
Results include data in
Charts, Tables, Text etc.
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.
Fire can submit the Analytical Jobs to be run onto AWS Glue. The results and visualizations are displayed back in Fire Insights.
Integration with Glue
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
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
Amazon Sagemaker
Fire is fully integrated with AWS SageMaker. Fire provides a number of processors for doing model building with SageMaker. These include :
LinearLearnerBinaryClassifier
LinearLearnerRegressor
PCASageMakerEstimator
SaveSageMaker
KMeansSageMakerEstimator
XGBoostSageMakerEstimator
LDASageMakerEstimator
Benefits of Sparkflows on AWS
Find quick value with Sparkflows and AWS