When it comes to anomaly detection, one of the key challenges that many organizations face is that it can be difficult to know how to define what an anomaly is. How do you define and anticipate unusual network intrusions, manufacturing defects, or insurance fraud? If you have labeled data with known anomalies, then you can choose from a variety of supervised machine learning model types that are already supported in BigQuery ML.

9.6 is focused on Yellowfin features that enhance the way our customers build, design and embed stunning analytical content, which include data storytelling, augmented analytics, actionable dashboards — and provide a high ease-of-use experience. As always, you can read the full list of updates in our release notes page, and view our release highlights video below to see the new enhancements demonstrated.

During the product keynote at our recent QlikWorld online event, we unpacked the power of the analytics data pipeline to transform raw data into informed action. Imagine a data pipeline where information flows continuously into everyday processes, allowing your organization to seize every business moment, as it happens...

Mercury, the Roman god of commerce, is often depicted carrying a purse, symbolic of business transactions, wearing winged sandals, illustrating his abilities to move at great speeds. Transactions power the world’s business systems today, ranging from millions of packages moving worldwide tracked in real time by logistics companies to global payments from personal loans to securities trading to intergovernmental transactions, keeping goods and services flowing worldwide.

In financial services, data is essential for storing product information, capturing customer details, processing transactions and keeping records of accounts; the relationship between products and their underlying data has always been symbiotic. A significant amount of data infrastructure is static, fragmented across data silos or based on legacy platforms. This has created an impedance mismatch between products and the underlying data.

June is the month which holds the summer solstice, and (at least in the northern hemisphere) we enjoy the longest days of sunshine out of the entire year. Just as the sun is making its longest trips across the sky, the BigQuery team is delighted to announce our next set of user-friendly SQL features.

When ATB Financial decided to migrate its vast SAP landscape to the cloud, the primary goal was to focus on things that matter to customers as opposed to IT infrastructure. Based in Alberta, Canada, ATB Financial serves over 800,000 customers through hundreds of branches as well as digital banking options. To keep pace with competition from large banks and FinTech startups and to meet the increasing 24/7 demands of customers, digital transformation was a must.

Today we’re announcing preview availability of a new public dataset for Google Trends. For the first time we’re bringing Google-owned Search data into Google Cloud Datasets for convenient analysis in BigQuery, or through your favorite business intelligence tools.

Google’s cloud data warehouse, BigQuery, has enabled organizations around the world to accelerate their digital transformation and empower their data analysts to unlock actionable insights from their data. Using BigQuery ML, data analysts are able to create sophisticated machine learning models with just SQL and uncover predictive insights from their data much faster.

Starting this week, we’re adding new content to the BigQuery Spotlight Youtube series. Throughout the summer we’ll be adding new videos and blog posts focused on helping new BigQuery architects and administrators master the fundamentals. You can find complimentary material for the topics discussed in the official BigQuery documentation.