Ajit Bhave has over 25 years’ experience in designing and implementing scalable enterprise systems. He co-founded Cumulus Systems (acquired by Hitachi) whose IT performance monitoring software is deployed in many large enterprise data centers. He holds several patents in the area of time series data storage and analysis. Ajit obtained his Bachelor's degree in Computer Science from IIT Delhi, India.
Ajit Bhave is speaking at the following session/s
Enhancing Fusion with Multi-Model, Real Time Streaming Analytics
Real-time analytics and prediction transform an ocean of data into actionable information and organizational wisdom. Bolt Analytics is a special-purpose, distributed data-flow execution framework that enables real-time analytics from multiple data sources. Creating inherently scalable and customizable data transformation pipelines by converting streaming data into a time-series representation is the core of Bolt’s framework. Bolt’s parametrized AI-driven models automatically detect and forecast anomaly occurrences in real time, and store them in Fusion, displaying results using faceted search. Bolt Analytics’ time-series models uniquely combine statistical and variable autoencoder neural models eliminating typical limitations of statistical techniques (time-series drift). The underlying distributed processing pipeline architecture can process up to 10 TB of data per day using a clustered Fusion system in production environments.
Techniques to improve ingestion performance and using neural models with Fusion.
The Lucidworks Fusion customers and partners responsible for submitting Support Tickets.