BigQuery offers the ability to quickly import a CSV file, both from the web user interface and from the command line: Indeed, try to open this file up with BigQuery: and we get the errors like: This is because a row is spread across multiple lines, and so the starting quote on one line is never closed. This is not an easy problem to solve — lots of tools struggle with CSV files that have new lines inside cells. Google Sheets, on the other hand, has a much better CSV import mechanism.

From rice genomes to historical hurricane data, Google Cloud Public Datasets offer a world of exploration and insight. The more than 20 PB across 200+ datasets in our Public Dataset Program helps you explore big data and data analytics without a lot of cost, setup, or overhead. You can explore up to 1 TB per month at no cost, and you don’t even need a billing account to start using BigQuery sandbox.

We launched BigQuery ML, an integrated part of Google Cloud’s BigQuery data warehouse, in 2018 as a SQL interface for training and using linear models. Many customers with a large amount of data in BigQuery started using BigQuery ML to remove the need for data ETL, since it brought ML directly to their stored data. Due to ease of explainability, linear models worked quite well for many of our customers.

At Google Cloud, we’re invested in building data analytics products with a customer-first mindset. Our engineering team is thrilled to share recent feature enhancements and product updates that we’ve made to help you get even more value out of BigQuery, Google Cloud’s enterprise data warehouse.

Understanding the data we collect is essential—it allows us to identify trends and uncover answers about our world. However, stories in our data frequently go untold. Large datasets are hard to share between research communities due to their size, security restraints, and complexity. Even if these datasets are accessible to users, the tools needed to query them often require deep technical knowledge.

October happens to be the month to celebrate World Smile Day when Harvey Ball, the inventor of the smiley face declared this day as such to give people a reason to smile. This month, BigQuery users have a lot of new reasons to smile about with the release of new user-friendly SQL capabilities now generally available.

Companies from every industry vertical, including finance, retail, logistics, and others, all share a common horizontal analytics challenge: How do they best understand the market for their products? Solving this problem requires companies to conduct a detailed marketing, sales, and finance analysis to understand their place within the larger market. These analyses are designed to unlock insights in a company's data that can help businesses run more efficiently.

Google BigQuery was released to general availability in 2011 and has since been positioned as a unique analytics data warehousing service. Its serverless architecture allows it to operate at scale and speed to provide incredibly fast SQL analytics over large datasets. Since its inception, numerous features and improvements have been made to improve performance, security, reliability, and making it easier for users to discover insights.

More than ever, businesses are making real-time, data-driven decisions based on information stored in their data warehouses. Today’s data warehouse requires continuous uptime as analytics demands grow and organizations require rapid access to mission-critical insights. Business disruptions from unplanned downtime can severely impact company sales, reputation, and customer relations.

Genomic data is some of the most complex and vital data that our customers and strategic partners like Mayo Clinic work with. Many of them want to work with genomic variant data, which is the set of differences between a given sample and a reference genome, in order to diagnose patients and discover new treatments. Each sample’s variants are usually stored as a Variant Call Format file, or VCF, but files aren’t a great way to do analytics and machine learning on these data.