Data and AI technology for manufacturing is having a moment. As AI has expanded its lengthy reach into manufacturing, there have been several purpose-built releases lately from companies like Nvidia and Databricks that are helping companies make sense of the deluge of data collected on everything from physical operations to the supply chain. Snowflake is now part of this action with the debut of its Manufacturing Data Cloud. The company says this new offering will enable companies in the automotive, technology, energy, and industrial sectors to tap into the value of siloed industrial data by leveraging Snowflake's data platform, partner solutions, and industry-specific datasets. The Snowflake Data Cloud provides a platform for data warehousing, SQL analytics, machine learning, data engineering, and monetization of third-party data.

Even in today's changing business climate, our customers' needs have never been more clear: They want to reduce operating costs, boost revenue, and transform customer experiences. Today, at our third annual Google Data Cloud & AI Summit, we are announcing new product innovations and partner offerings that can optimize price-performance, help you take advantage of open ecosystems, securely set data standards, and bring the magic of AI and ML to existing data, while embracing a vibrant partner ecosystem. In the face of fast-changing market conditions, organizations need smarter systems that provide the required efficiency and flexibility to adapt. That is why today, we're excited to introduce new BigQuery pricing editions along with innovations for autoscaling and a new compressed storage billing model. BigQuery editions provide more choice and flexibility for you to select the right feature set for various workload requirements.

We're at the forefront of the data revolution, committed to building the world's greatest data and applications platform. Our'get it done' culture allows everyone at Snowflake to have an equal opportunity to innovate on new ideas, create work with a lasting impact, and excel in a culture of collaboration. There is only one Data Cloud. Snowflake's founders started from scratch and designed a data platform built for the cloud that is effective, affordable, and accessible to all data users. They engineered Snowflake to power the Data Cloud, where thousands of organizations unlock the value of their data with near-unlimited scale, concurrency, and performance.

UiPath, a leading enterprise automation software company, announced it has strengthened its partnership with Snowflake, the Data Cloud company, by launching a new bi-directional integration that will extend the value of automation across the enterprise. UiPath and Snowflake are enabling joint customers to design and build workflows based on 360-degree views of trusted and accessible data on Snowflake's platform. By leveraging the Snowflake Data Cloud, UiPath robots can quickly connect data directly to business processes in the Data Cloud without using complex code, speeding up time to value. Automation is helping organizations around the world become faster and more agile in the face of increased demand and rapidly changing environments. The UiPath end-to-end platform provides robotic process automation (RPA) at its core, removing manual work so users can focus on what matters most.

According to a recent PwC U.S. CEO survey, 84% of CEOs plan to increase their investment in digital transformation.1 This investment is especially important in the financial services (FinServ) industry, where legacy systems often impede the smooth and speedy flow of data that is necessary for transaction processing. According to a recent Economist Intelligence Unit report sponsored by Snowflake, "The persistence of data silos puts a unified view of data out of reach for many FinServ firms. And that, in turn, makes it hard for them to achieve strategic goals such as offering individual and institutional customers a personalized experience across departments, channels, and touchpoints; meeting global regulatory standards; detecting and protecting against risk and fraud; and increasing overall operational efficiency." Snowflake's Data Cloud equips banks, brokerages, insurers, and financial technology startups with the power of unified data that is easy to share securely.

There is only one Data Cloud. Snowflake's founders started from scratch and designed a data platform built for the cloud that is effective, affordable, and accessible to all data users. They engineered Snowflake to power the Data Cloud, where thousands of organizations unlock the value of their data with near-unlimited scale, concurrency, and performance. This is our vision: a world with endless insights to tackle the challenges and opportunities of today and reveal the possibilities of tomorrow. Amazon Web Services (AWS) and Microsoft Azure are our strategic Partners with whom we have thousands of joint customers using Snowflake.

There is only one Data Cloud. Snowflake's founders started from scratch and designed a data platform built for the cloud that is effective, affordable, and accessible to all data users. They engineered Snowflake to power the Data Cloud, where thousands of organizations unlock the value of their data with near-unlimited scale, concurrency, and performance. This is our vision: a world with endless insights to tackle the challenges and opportunities of today and reveal the possibilities of tomorrow. We're hiring talented Senior Engineering Managers to help us expand the Snowflake Data Cloud by building upon our Continuous Data Pipelines technologies to achieve zero latency with infinite throughput data processing.

Snowflake is getting new feature stores, as an increasing number of enterprise teams look at the data company to build and deploy machine learning applications. In a statement on Wednesday, Tecton announced a partnership with the data giant under which the former's feature store, known for managing the complete lifecycle of machine learning (ML) features, as well as the open-source one from Feast will be integrated with the Snowflake Data Cloud. The move, as the companies explained, will give enterprise data scientists a fast yet simple way to build production-grade features for a broad range of operational ML use cases, starting from fraud detection and product recommendation to real-time price tracking. Enterprises using cloud data platforms (such as Snowflake) for ML projects can run into issues such as distinct pipelines during implementation or training data leakages/inaccuracies. This can slow the development time, affecting the delivery of the project.

There is only one Data Cloud. Snowflake's founders started from scratch and designed a data platform built for the cloud that is effective, affordable, and accessible to all data users. They engineered Snowflake to power the Data Cloud, where thousands of organizations unlock the value of their data with near-unlimited scale, concurrency, and performance. This is our vision: a world with endless insights to tackle the challenges and opportunities of today and reveal the possibilities of tomorrow. Snowfake's future success depends upon making our users: data scientists, app developers, and data engineers successful.

At OpenWorld, Oracle jumped into the artificial intelligence and machine learning space for its customer experience products (aka customer relationship management) and other applications (like human capital management) with an interesting difference -- a huge data store to help educate the algorithms that work for you. We're waiting for products to be delivered this year. Machine learning depends on data about prior situations that the learning algorithms can use to get smart about a situation. Ten examples are good, 100 are better. Generally, the more samples there are the more refined a recommendation can be.