Unified data and analytics with Databricks Lakehouse Platform The Mercury Energy story

Details
Today, every business wants to leverage data to drive strategic initiatives with machine learning, data science and analytics — but siloed teams, proprietary technologies and unreliable data challenge the data-driven plans.

That’s why enterprises are turning to the lakehouse for a single platform that unifies all their data, analytics and AI workloads.

We’ll explore how the Databricks Lakehouse Platform can help compete in the world of big data and artificial intelligence.

We will provide a high-level overview of big data concepts and the Databricks Lakehouse Platform, including:
- Foundational concepts in big data, the key roles and abilities to look for when building data teams, and an overview of the complete data landscape
- How the Databricks Lakehouse Platform can help your organization streamline workflows, break down silos and make the most of your data

We will be sharing a customer use case – to demonstrate how Mercury NZ has built its Data Platform using the Lakehouse Architecture

Speakers BIO:

Zivile is a Solutions Architect at Databricks where she is helping teams across New Zealand to democratise AI and build their data platforms using Lakehouse architecture. Before joining Databricks Zivile has spent years working with different kiwi startups and has taken part in building and upgrading analytics and data platforms across companies of various industries and sizes. Zivile’s technical background is in computer science and she holds a Bachelor of Computing and Information Science as well as Solutions Architect certification for Azure and AWS clouds.

Srinivasan is a Technical Lead for Data & Analytics in Mercury where he is building a Modern Data Platform using the Lakehouse architecture and has enabled the delivery of advanced analytics use cases on top of it. Srinivasan has experience working with customers and helping them design and implements Data Platform to deliver self-service analytics, Data Science and Machine Learning use cases. When he is not architecting Data Platforms, Srinivasan enjoys playing/watching cricket.