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.
Data pipeline performance hinges on several important techniques, including algorithmic optimization and parallelization.
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.
