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.
Data accessibility and analysis is a crucial part of getting value from your data. While there are many methods to view data when it comes to BigQuery, one common way is to export query results as an email on a scheduled basis. This lets end users get an email with a link to the most recent query results, and is a good solution for anyone looking for daily statistics on business processes, monthly summaries of website metrics, or weekly business reviews.
BigQuery, Google Cloud’s petabyte-scale data warehouse, lets you ingest and analyze data quickly and with high availability, so you can find new insights, trends, and predictions to efficiently run your business. Our engineering team is continually making improvements to BigQuery so you can get even more out of it. Recently added BigQuery features include new materialized views, column-level security, and BigQuery ML additions.
In a market where streaming analytics is growing in popularity, it’s critical to optimize data processing so you can reduce costs and ensure data quality and integrity. One approach is to focus on working only with data that has changed instead of all available data. This is where change data capture (CDC) comes in handy. CDC is a technique that enables this optimized approach.
