Google Cloud | Swipe Insight
Google has introduced major updates to BigQuery, including renaming workflows to pipelines and adding version control capabilities. Workflows are now called pipelines, with documentation available. New version control features in preview allow users to manage file versions using Git and edit code in workspaces. Additionally, BigQuery ML supports remote models based on the Anthropic Claude model, enabling advanced AI text generation tasks.
Changes in Looker Studio
Google has made changes to Looker Studio, introducing quotas for scheduled emails that limit recipients for standard users, while Pro users face restrictions only for personal reports. Pro users can upgrade reports to avoid these limits. Exclusive features for Pro reports include immediate email delivery, custom subject and messages, and report previews in emails. A data preview feature is now available to all users, enhancing the data source editor.
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Updates on Looker Studio and BigQuery
Google has updated Looker Studio with enhanced permission management and event logging. The "gemini_in_looker" permission is now enforced for users accessing Conversational Analytics features, formalizing access controls for AI analytics. Additionally, the event logging system now includes connector types for charts in audit logs when users export data from reports using Looker data sources, improving security and oversight.
Announcements Regarding BigQuery
BigQuery has announced that Analytics Hub egress controls and data clean room subscriptions are now available in all editions and with on-demand pricing. This expansion broadens access to data sharing and protection features. Egress controls allow publishers to restrict how subscribers can export data from linked datasets. Data clean rooms create secure environments for sharing and analyzing sensitive data without revealing it, now accessible across all editions.
Data Transfer Service Updates
BigQuery Data Transfer Service now supports custom reports for Google Ads using Google Ads Query Language (GAQL) in transfer configurations. This feature allows ingestion of custom reports and fields beyond standard options and is generally available. The connector supports custom reports from all resources in the Google Ads API. Limitations include unsupported clauses like WHERE and ORDER BY, automatic appending of date segments, and restrictions on backfill runs.
Enhancements in Gemini within BigQuery
Google has enhanced Gemini in BigQuery with a new Python code completion feature that offers contextually relevant suggestions. This aids users in writing Python code more efficiently within BigQuery notebooks. To access it, users navigate to BigQuery Studio, select Python notebook, and type code to receive inline suggestions. Users can also customize their experience by managing Gemini features in the BigQuery toolbar, highlighting Google's focus on improving productivity.
Improved Sorting Feature in Looker Studio
Looker Studio has introduced a new feature for table charts that allows users to sort by multiple columns at once by clicking on column headers while holding the Shift key. Tables in Looker Studio present data in rows and columns, where each column represents a dimension or metric and each row is a data record. The tables automatically group data, displaying unique combinations of dimensions and aggregating metrics based on their defined types.
Resource Utilization Charts in BigQuery
BigQuery has introduced resource utilization charts in Preview, providing metrics views and enhanced configuration options. These charts help track past resource use for planning and troubleshooting. They gather data from INFORMATION_SCHEMA views and can be updated in real time for up to 30 days. Users can configure event timelines and select metrics like slot usage, with status chips indicating loading and errors.
Intelligent Join Operation Recommendations in BigQuery
Google has launched a Preview feature in BigQuery that uses Gemini AI for intelligent join operation recommendations. It provides context-aware suggestions and natural language processing for accurate joins, generating SQL expressions and descriptions. Users can access it in the data view interface, select join types, and preview results. The system defaults to a Cross join if no conditions are specified, allowing users to refine their options.
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