Today, we are introducing BigQuery Omni, a flexible, multi-cloud analytics solution that lets you cost-effectively access and securely analyze data across Google Cloud, Amazon Web Services (AWS), and Azure (coming soon), without leaving the familiar BigQuery user interface (UI). Using standard SQL and the same BigQuery APIs our customers love, you will be able to break down data silos and gain critical business insights from a single pane of glass.

Today, we’re announcing Data QnA, a natural language interface for analytics on BigQuery data, now in private alpha. Data QnA helps enable your business users to get answers to their analytical queries through natural language questions, without burdening business intelligence (BI) teams. This means that a business user like a sales manager can simply ask a question on their company’s dataset, and get results back that same way.

At Google Cloud, we work with organizations performing large-scale research projects. There are a few solutions we recommend to do this type of work, so that researchers can focus on what they do best—power novel treatments, personalized medicine, and advancements in pharmaceuticals.

We hear from our users in the scientific community that having the right technology foundation is essential. The ability to very quickly create entire clusters of genomics processing, where billing can be stopped once you have the results you need, is a powerful tool. It empowers the scientific community to spend more time doing their research and less time fighting for on-prem cluster time and configuring software.

Editor’s note: We’re hearing today from Unity Technologies, which offers a development platform for gaming, architecture, film and other industries. Here, Director of Engineering and Data Sampsa Jaatinen shares valuable insights for modern technology decision makers, whatever industry they’re in.

We’re announcing a key capability to help organizations govern their data in Google Cloud. Our new BigQuery table-level access controls (table ACLs) are an important step that enables you to control your data and share it at an even finer granularity. Table ACLs also bring closer compatibility with other data warehouse systems where the base security primitives include tables—allowing migration of security policies more easily.

Google Cloud’s enterprise data warehouse BigQuery offers some flexible pricing options so you can get the most out of your resources. Our recently added Flex Slots can save you money by switching your billing to flat-rate pricing for defined time windows to add maximum efficiency. Flex Slots lets you take advantage of flat-rate pricing when it’s most advantageous, rather than only using on-demand pricing.

BigQuery has several built-in features and capabilities to help you save on costs, manage spend, and get the most out of your data warehouse resources. In this blog, we’ll dive into Reservations, BigQuery’s platform for cost and workload management. In short, BigQuery Reservations enables you to: Quickly purchase and deploy BigQuery slots Assign slots to various parts of your organization

When you use data to guide your business decision-making process, you need to continually optimize your data analytics usage to get more out of that data. Here, we’ll share some ways to be more efficient with your BigQuery usage through ups and downs and changing demands.

Editor’s note: Today we’re hearing from some of the team members involved in building BigQuery over the past decade, and even before. Our thanks go to Jeremy Condit, Dan Delorey, Sudhir Hasbe, Felipe Hoffa, Chad Jennings, Jing Jing Long, Mosha Pasumansky, Tino Tereshko, and William Vambenepe, and Alicia Williams. This month, Google’s cloud data warehouse BigQuery turns 10.