Sparkflows and AWS: Your Path to Self-Serve Advanced Analytics & AI

As Data Science becomes an integral part of enterprise strategy, the ability to enable data specialists like data analysts and data scientists to drive advanced analytics remains a top priority. Organizations are on a mission to be data-centric to extract most value from their data. However, the complexity of the data solutions significantly slow them down.

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Sparkflows is a low-code drag and drop analytics studio providing powerful self-service advanced analytics to data personas across the analytics value chain. Sparkflows enables data scientists, data analysts and data engineers with end-to-end analytics capabilities via workflows and over 350+ nodes to connect, read, prepare, transform, profile/visualize data, build ML models, and create reports and dashboards dynamically. Sparkflows is deeply integrated with AWS Services like AWS Glue, Amazon EMR, Amazon S3, Amazon Redshift, Amazon MSK, Amazon Kinesis, AWS Lake Formation and Amazon Sagemaker. Users can easily browse, upload, download data from S3 and view the data in the Hive metastore. Sparkflows supports multiple ML engines including Scikit-learn, H2O, Apache Spark ML, Prophet, ARIMA, Statsmodel, Keras etc., and provides an enterprise-wide secure collaboration platform for users.

​In this webinar, Sparkflows and Amazon Web Services (AWS) will address how organizations can solve the complexities of the data solutions by implementing Sparkflows on AWS.