Bluetab
an IBM Company
SPARK TUNE
Optimize and enhance your Spark data processes and platform.
Unlocking advanced analysis through knowledge databases
Optimize and enhance your Spark data processes and platform.
There are several challenges related to Spark that cannot be addressed through descriptive analytics alone. When analyses involve dozens of dimensions, machine learning and deep learning algorithms must be employed. Use cases include:
- Process clustering for cluster definition in Databricks.
- Comparative analysis of Cloudera and Databricks post-migration
Generative models are capable of generating content, images, videos, but they also have analytical capabilities by leveraging specialized knowledge, in our use case, Spark. Our research focus is on exploiting these capabilities to solve very complex optimization problems. Use cases include:
- Analysis of knowledge database processes by converting natural language to SQL.
- Recommendation engine for optimizations of existing processes.
- Anomaly detection.
Footer
