Self-Service Advanced
Analytics and ML Studio on GCP
Sparkflows is a highly advanced Self-Service No/Low-code Decision Intelligence Studio that operates securely in GCP.
Sparkflows integrates deeply with serverless Dataproc, BigQuery, Gemini, Vertex-ai, and Biglake and comes packed with 200+ powerful workflows, and 450+ processors for connecting, transforming, exploring data, and building AI models at scale.
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Sparkflows is deeply integrated with and certified on GCP. It can be installed on an GCP machine, run in standalone mode, or submit the jobs to Google Cloud Dataproc.
It can process data from Google Cloud Storage, BigQuery, etc.
Build and Run Analytics and ML jobs on Dataproc or standalone machines
Read and Write data to Google BigQuery
Seamlessly read files from Google Cloud Storage and process them
Read and process streaming
data from Apache Kafka and Google Cloud Dataflow
Send data to and build ML
models on Vertex AI Workbench
Results include data in
Charts, Tables, Text, etc.
Integration with Google Cloud Dataproc
Sparkflows can be easily installed on GCP. It can connect to any Google Dataproc cluster, submit jobs to it and display results.
Integration with Google Cloud Dataprep
Sparkflows can submit the Analytical Jobs to be run onto Google Dataprep. The results and visualizations are displayed back in Sparkflows.
Integration with BigQuery
Sparkflows is fully integrated with BigQuery. Sparkflows has processors for reading from and writing to BigQuery. Seamlessly browse the BigQuery catalog, run queries and view results.
Integration with Google Cloud Storage
Sparkflows allows you to access your files on Cloud Storage. The jobs run by Sparkflows can read from and write to files on Cloud Storage. The files can be in various file formats including CSV, JSON, Parquet, Avro, etc. Sparkflows also allows you to browse your files on Cloud Storage.
Integration with Vertex AI Workbench
Sparkflows is fully integrated with Vertex AI Workbench. Sparkflows provides a number of processors for doing model building with Vertex AI Workbench. These include :
LinearLearnerBinaryClassifier
LinearLearnerRegressor
XGBoost Model
AutoML
Scikit-learn library Models
Benefits of Sparkflows on Google Cloud
Enables Business Analysts
Self Serve Advanced Analytics
Return on Investment (ROI)
Enables Business Analysts to find quick value with GCP clusters.
Enables users to do Analytics and Machine Learning in minutes.
Solves your Data Science use cases 10x faster.
10x More Users
Enables 10x more users to build
Data Science use cases.
No code and low code platform
Makes it easy to build, maintain
and execute.