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Databricks Unified Analytics Platform Takes the Stage at Gartner Data & Analytics Summit 2019

Co-Founder and Chief Architect Reynold Xin, One of the Original Creators of Apache Spark, Leads Discussion on Unified Analytics; Company Competes in Data Science and Machine Learning Bake-Off

Databricks, the leader in Unified Analytics and founded by the original creators of Apache Spark™, will exhibit and participate in several sessions at the Gartner Data & Analytics Summit 2019 taking place March 18 – 21 in Orlando, Florida. On the expo floor, Databricks will showcase its Unified Analytics Platform that has been adopted by over 2,000 organizations to unify data and machine learning initiatives. Databricks will also participate in the first-ever Data Science and Machine Learning Bake-Off, and deliver a session featuring Reynold Xin, a Databricks co-founder and one of the original creators of Apache Spark; it will highlight data science use cases across a range of industries including Financial Services, Media & Entertainment, Healthcare & Life Sciences, Technology, Energy and Retail.

“Our session will cover practical approaches that have been leveraged by thousands of organizations to overcome the bottlenecks associated with data silos and disparate technologies.”

The Gartner Data & Analytics Summit will offer a holistic view of current trends and topics around data management, business intelligence (BI), and analytics, including innovative technologies such as AI, blockchain and IoT. Enterprises attend the Summit to learn how to overcome data and analytics complexities to make game-changing business decisions. The Summit is expected to draw over 3,000 attendees consisting of chief data officers and chief analytics officers, senior IT and business leaders, as well as practitioners.

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Conference-goers can visit Databricks at booth #230 within the expo hall or attend the following sessions:

Monday, March 18

Data Science and Machine Learning (DSML) Bake-Off
Sean Owen, Data Scientist

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Databricks will participate in the first Data Science and Machine Learning (DSML) Bake-Off taking place on Monday, March 18. This fast-paced session will feature multiple vendors who will demo their platforms, compare capabilities and contrast offerings with each other. The bake-off focuses on showing the diverse nature of DSML platforms but all providing an end-to-end approach to building and managing models.

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Tuesday, March 19

Session Talk – Unified Data Teams across The End-to-End Data and Machine Learning Lifecycle
Reynold Xin, Original Creator of Apache Spark and Chief Architect at Databricks

Most organizations today are challenged with data silos, using separate technologies for data processing and machine learning, with limited collaboration between data scientists and engineers. The session will cover a new unified approach that leverages open source technologies (Apache Spark, MLflow, TensorFlow and others) to accelerate your data science initiatives. Unified Analytics combines data processing and machine learning in a single collaborative platform for data science and engineering teams. In this talk, Databricks will also share how customers like Nielsen, Bechtel, Shell, and are building unified teams across the end-to-end data and machine learning lifecycle to accelerate innovation.

“We hear from enterprises across the globe that the end-to-end data and machine learning life cycle continues to be a challenge for data teams looking to achieve AI,” said Xin. “Our session will cover practical approaches that have been leveraged by thousands of organizations to overcome the bottlenecks associated with data silos and disparate technologies.”

Databricks was recently named a Visionary in Gartner’s January 2019 Magic Quadrant for Data Science and Machine Learning Platforms for the second consecutive year. Databricks’ Unified Analytics Platform is a cloud-based platform powered by Apache Spark that provides auto-scaling for big data clusters, performs up to 50x faster than Apache Spark, integrates seamlessly with machine learning frameworks and simplifies productioning data pipelines.

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