Qubole reached out to us to run a targeted Roundtable in Singapore for 15 of potential customers.
The below content was extremely well received.
Today, organizations are making significant investments in big data projects, yet a staggering 85% fail to meet expectations according to McKinsey. Why do such a large percentage fail? The hardest transition to make is from a successful analytics or machine learning pilot to a full-scale deployment. As service breaches occur, SLAs slip, and a backlog of requests mount, companies often respond by deploying more admins, more data engineers or maxing out infrastructure spend.
This networking lunch has been designed to stir up a lively discussion that sweeps across a broad range of topics revolving around how to make your big data platform run more efficiently at scale, across use cases ranging from machine learning, artificial intelligence, advanced customer analytics, IoT, supply chain optimization, fraud detection and more.
Key Takeaways:
· The benefits of a cloud-native platform for “self-serve” analytics and ML, done at enterprise scale
· Real-world success stories from companies such as Disney, Expedia, Grab, Lyft, Oracle, Comcast, Autodesk, Samsung, ESPN and Malaysia Airlines
· How these data-driven companies scale out performance for ETL pipelines, ad hoc queries or ML models
· How to support more users and data sources (while meeting SLAs within your targeted budget) without additional administrators
· Best practices for migrating from an on-premises environment to the cloud
· How to reduce your infrastructure spend/TCO by 50 percent or more
