Data and AI pioneer Databricks has plans to roll out its ground-breaking data lakehouse vision to more enterprise customers following a $1.6bn funding injection.
The Series H funding saw investors like Amazon Web Services (AWS), CapitalG and Microsoft put fresh capital into the San Francisco-based company, giving it a valuation of $38 billion.
Databricks said its data lakehouse architecture is aimed at enterprises that struggle with maintaining both data lakes and data warehouses. The data silos that can result often prevent a single source of truth, and maintaining them comes both at a high cost and a reduced speed of decision-making. A lakehouse is a new type of platform that implements similar data structures and data management features to those in a data warehouse, but directly on the low-cost, flexible type of storage used for cloud data lakes. CIOs get the reliability, governance and performance of a data warehouse at the same time as leveraging the data lakes that most organizations already store their data in. This allows them to build analytics platforms on AWS, Microsoft Azure, and Google Cloud to support every data and analytics workload in a single, unified location. This new AI-powered architecture allows traditional analytics and data science to co-exist in the same system.
“We have a vision for an open and unified approach to data and AI on any cloud,” said Ali Ghodsi, Co-Founder and CEO of Databricks. “Now we want to make more organizations successful in their move to the cloud and accelerate adoption of the lakehouse architecture.”
Here are two examples from different verticals of enterprises that have already deployed this new model:
- Drug development giant AstraZeneca was experiencing difficulty with tapping into all of the scientific information available to it faster than the pace of new data coming in. It needed a platform that enabled them to build scalable, performant data pipelines that feed machine learning models designed to help their scientists make targeted decisions. With Databricks, it is now able to leverage data and machine learning to build a recommendation engine that empowers scientists to more easily uncover new novel drugs quicker, cheaper and more effectively
- Oil giant Shell also wanted a way to make better use of data and AI to improve operational efficiencies, drive customer engagement, and tap into innovations like renewable energy. Held back by large volumes of data, Shell chose Databricks to be one of the foundational components of its Shell.ai platform. Today, Databricks empowers hundreds of Shell’s engineers, scientists and analysts to innovate together as part of their ambition to deliver cleaner energy solutions more rapidly and efficiently. “As an industry, we are going through a massive transition,” explained Dan Jeavons, GM of data science at Shell. “Digital technology is absolutely core to making our existing business more effective and efficient. As the industry continues to expand into new areas of energy that are more sustainable and reduce environmental impact, data and digital technology are now table stakes.”
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