Bluetab
an IBM Company
SPARK TUNE
The solution that enhances the observability and optimization of Spark data platforms for superior performance and cost efficiency.
Unlocking advanced analysis through knowledge databases
Exploiting a robust knowledge base with simple algorithms, we can find numerous valuable insights for decision-making. Use cases include:
- Process variability analysis.
- Resource consumption analysis.
- Process distribution analysis.
- Estimated cost calculation in Databricks for different cluster strategies.
- Identification of processes that can benefit from Databricks Photon.
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.
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