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Today, Google kicked off its Data Cloud Summit with a variety of new product and enhancement announcements designed to help data scientists harness the power of Google Cloud Platform for data science. The company has invested heavily in artificial intelligence over the years, and its new products can help businesses and users make sense of the deluge of data using both traditional analytics and machine learning.
“Data is probably high on the agenda of every C-suite on the planet,” said Gerrit Kazmaier, General Manager and VP of Databases, Data Analytics and Google Cloud View. “Every company is a big data company. It is multi-format capable. It’s streaming and it’s everywhere.”
Google wants to compete for this demand with its cloud platform by offering sophisticated tools for applying artificial intelligence and machine learning. At the same time, it promotes an open ecosystem so that companies can use and share data wherever it is collected. The new releases emphasize breaking barriers between clouds from different merchants and also self-hosting options by customers.
This open strategy can help Google compete with big competitors like Amazon or Microsoft. Amazon’s web services offer nearly a dozen different options for data storage, and these are all tightly integrated with many platforms for data analysis using traditional reporting or machine learning. Microsoft’s Azure also offers a wide range of options that leverage its long history with enterprise computing.
Google’s BigLake platform is designed to work with data across different clouds, stored both on-premises and in commercial clouds, including competitors. The service offers companies the opportunity to combine their data warehouses and lakes in a multi-cloud platform.
Historically, many companies have built data warehouses, a well-managed model that combines good reporting with solid access control. Recently, some are using the term “data lake” to describe systems optimized for big data rather than sophisticated tools. Google wants to compensate for these different approaches with its BigLake model.
“By bringing these worlds together, we take the good from one side and apply it to the other side, and that’s how you make your storage space infinite,” explained Sudhir Hasbe, a director at Google’s Cloud. “You can enter as much data as you like. You get the richness of governance and management you want in your environment in a rapidly changing regulatory environment. You can store and manage all the data and have really good control.”
Part of Google’s strategy is to create the Data Cloud Alliance, a collaboration between Google and Confluent, Databricks, Dataiku, Deloitte, Elastic, Fivetran, MongoDB, Neo4j, Redis and Starburst. The group wants to help standardize open formats for data so that information can flow as easily as possible between the different clouds across political and business barriers.
“We’re excited to partner with Google Cloud and the members of this Data Cloud Alliance to unify access to data across clouds and application environments and remove barriers to digital transformation efforts,” said Mark Porter, CTO at MongoDB . “Legacy frameworks have made working with data difficult for too many companies. There couldn’t be a more timely and important data initiative to build faster and smarter data-driven applications for customers.”
At the same time, Google also has to watch a growing number of smaller cloud providers like Vultr or DigitalOcean, which often offer dramatically lower prices. Google’s greater commitment to artificial intelligence research allows them to offer much more sophisticated options than any of these commodity cloud providers.
“What really sets Google apart is that we believe in creating unique technical products,” said Kazmaier. “Our mindset for innovation is ingrained, and understanding data is a vast and limitless resource when used properly. Most importantly, you need to have an open ecosystem around you for it to be successful.”
The Vertex AI Workbench is a tool that integrates Jupyter notebooks with the main components of Google’s cloud, from data processing instances to serverless to event-driven tools like Spark. The tool can pull information from any of these sources and feed it into analysis routines, allowing data scientists to look for signals in the data. It will tentatively be available in some regions on April 6th and everywhere in June.
“At Google Cloud, we are pushing the boundaries of data clouds to further bridge the data-to-AI value gap,” said June Yang, VP of Cloud AI and Innovation at Google. “This capability enables teams to build, train, and deploy models five times faster than traditional notebooks.”
The company also wants to encourage teams and companies to share some of the AI models they create. The Vertex AI Model Registry, now in preview, provides data scientists and application developers with a way to store and reuse AI models.
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