Master Data Management challenges, Detect Anomalies and Transform Event Streams

Sep 29, 2021 · Auckland, New Zealand

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Agenda:
16:30pm-16:35pm: Introduction & Online Networking
16:35pm-17:05pm: Coran Stow, Solutions Architect, Confluent
17:05pm-17:35pm: Johnny Mirza, Senior Solutions Engineer, Confluent
17:35pm: Q&A
Have follow-up questions afterward? Log in to Slack or Join our Forum (https://www.confluent.io/community/ask-the-community) to ask any follow-up questions!

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Talk 1-

Speaker:
Coran Stow, Solutions Architect, Confluent

Bio:
Coran is a technically-minded solutions architect who likes making technology work to solve customer problems. He's spent the last decade delivering Master Data Management and other Information Management solutions to clients in the Government, FInancial and Healthcare industries in Australia and the region.

Talk:
Applying Kafka to Master Data Management challenges

Abstract:
As soon as organisations have more than one system they are challenged with keeping their data consistent between systems. What if they have dozens of systems? What if they have a mix of bespoke, COTS, distributed, centralised, on-prem and cloud-based systems to keep their data consistent between? How can Kafka help solve this challenge?

Talk 2-

Speaker:
Johnny Mirza, Senior Solutions Engineer, Confluent

Bio:
Johnny has a strong technical background supporting the Telco, Media, Insurance and Transport industry verticals. Johnny comes from a pre-sales solutions engineering background and has a passion for architectures of public cloud and on-premise infrastructure, service assurance and monitoring platforms, and working with open source technologies.

Talk:
Detecting Anomalies and Transforming Event Streams with Data in Motion

Abstract:
In this webinar we’ll look at how stream-processing can be used to perform real-time anomaly detection in streaming event data. Organisations can use stream-processing to detect potentially fraudulent transactions and activities, and SIEM teams can curate and detect patterns in security and event-log data.

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