Vishwa Belur - Databricks

Vishwa Belur

Manager, Informatica

Vishwa manages Informatica’s Streaming and IoT products and solutions at Informatica. Prior to Informatica, Vishwa worked at EdgeVerve Systems, an Infosys Company as the Product Manager for the Finacle Core Banking solution. He also worked at Informatica as a product manager of Metadata and Governance solutions. Vishwa has 10 years Product Management experience managing a variety of enterprise software products. He has worked at SAP and Hewlett Packard in various roles. Prior to becoming a Product Manager, Vishwa was a Software Engineer. He holds an Engineering Degree in Computer Science and Master’s Degree from Indian Institute of Management, Bangalore.

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AI-Powered Streaming Analytics for Real-Time Customer ExperienceSummit Europe 2019

Interacting with customers in the moment and in a relevant, meaningful way can be challenging to organizations faced with hundreds of various data sources at the edge, on-premises, and in multiple clouds.

To capitalize on real-time customer data, you need a data management infrastructure that allows you to do three things:
1) Sense-Capture event data and stream data from a source, e.g. social media, web logs, machine logs, IoT sensors.
2) Reason-Automatically combine and process this data with existing data for context.
3) Act-Respond appropriately in a reliable, timely, consistent way. In this session we'll describe and demo an AI powered streaming solution that can tackle the entire end-to-end sense-reason-act process at any latency (real-time, streaming, and batch) using Spark Structured Streaming.

The solution uses AI (e.g. A* and NLP for data structure inference and machine learning algorithms for ETL transform recommendations) and metadata to automate data management processes (e.g. parse, ingest, integrate, and cleanse dynamic and complex structured and unstructured data) and guide user behavior for real-time streaming analytics. It's built on Spark Structured Streaming to take advantage of unified API's, multi-latency and event time-based processing, out-of-order data delivery, and other capabilities.

You will gain a clear understanding of how to use Spark Structured Streaming for data engineering using an intelligent data streaming solution that unifies fast-lane data streaming and batch lane data processing to deliver in-the-moment next best actions that improve customer experience.