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Google BigQuery

Shine on with user-friendly SQL capabilities in BigQuery

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.

ATB Financial boosts SAP data insights and business outcomes with BigQuery

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.

AutoML Tables is now generally available in BigQuery ML

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.

The BigQuery admin reference guide: Resource Hierarchy

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.

How to structure your BigQuery resources

What are folders, projects, and datasets and how do they come together to support warehousing fundamentals like security and cost management? In this episode of BigQuery Spotlight, we’ll review the BigQuery resource model and how this resource hierarchy is reflected in the Cloud Console, where you’ll interact with and analyze your BigQuery data. Moreover, we’ll give you some helpful tips when it comes to structuring your own BigQuery resources. Watch to learn the best way to structure your BigQuery deployments!

BigQuery row-level security enables more granular access to data

Data security is an ongoing concern for anyone managing a data warehouse. Organizations need to control access to data, down to the granular level, for secure access to data both internally and externally. With the complexity of data platforms increasing day by day, it's become even more critical to identify and monitor access to sensitive data.

Monitoring BigQuery reservations and slot utilization with INFORMATION_SCHEMA

BigQuery Reservations help manage your BigQuery workloads. With flat-rate pricing, you can purchase BigQuery slot commitments in 100-slot increments in either flex, monthly, or yearly plans instead of paying for queries on demand. You can then create/manage buckets of slots called reservations and assign projects, folders, or organizations to use the slots in these reservations. By default, queries running in a reservation automatically use idle slots from other reservations.

Realtime data replication into BigQuery with Datastream and Dataflow

How can you replicate data from a relational database in real time? In this video, we’ll show you you can combine Datastream with Dataflow templates to replicate data from a relational database. Watch to learn how you can use this streaming analytics service in unison with Datastream to easily replicate data from Oracle to BigQuery in real time!

Real-time Change Data Capture for data replication into BigQuery

Businesses hoping to make timely, data-driven decisions know that the value of their data may degrade over time and can be perishable. This has created a growing demand to analyze and build insights from data the moment it becomes available, in real-time.