Thousands of data professionals descended on San Jose, California from June 17 – 21 for DataWorks Summit 2018. This year’s event witnessed more than 150 breakout sessions and keynotes from the leaders in the industry.
Impetus was a Diamond sponsor at the event.
One of the highlights this year was the keynote by Praveen Kankariya − Founder and CEO, Impetus Technologies. Praveen’s session explored the challenges in creating a unified view of enterprise data, an essential building block for information-driven decision making and the advent of an AI-driven future. You can watch the session here.
Experts from Impetus and our sister organization, Kyvos Insights, also hosted three breakout sessions featuring insights from Fortune 500 client case studies covering modern data management and advanced analytics:
- How a major bank leveraged Apache Spark and StreamAnalytix to rapidly re-build their insider threat detection application: Anand Venugopal, Product Head – StreamAnalytix and Sr. Solutions Architect, Punit Shah spoke about a StreamAnalytix use case to transform an aging insider threat detection application for a major bank.
- Migrating analytics to the cloud at Fannie Mae: Praveen Kankariya hosted the session presented by Kevin Bates, Vice President of Enterprise Data Strategy Execution at Fannie Mae. The presentation described the modernization of Fannie Mae’s analytics platform and corresponding full migration of its Netezza warehouse to the cloud.
- BI on big data with instant response times at Verizon: Ajay Anand, Kyvos Insights Vice President of Product Management and Marketing hosted Arun Jinde, Sr. Technical Consultant for data warehousing at Verizon. He shared the success that Verizon has enjoyed using Kyvos for low latency OLAP processing on massive big data scale.
Meetup – Anomaly Detection Techniques and Implementation Using Apache Spark
Feb 27, 2019 - Feb 27, 2019 Atlanta, GA
An Easy Approach to ETL with Apache Spark – Visually prepare, integrate, and transform data as it arrives
2019-03-08 23:30:00 - 10:00 am PT / 1:00 pm ET