Google BigQuery offers continuous queries for real-time data processing and ML inference, outputting to BigQuery, Pub/Sub, or Bigtable. It supports multiple ingestion methods, monitoring, and autoscaling. Use cases include personalized messaging, anomaly detection, event-driven pipelines, data enrichment, and reverse ETL. Spanner supports cross-regional federated queries from BigQuery, enabling cross-region queries without egress fees in preview.
BigQuery now allows scheduling automated data transfers from Snowflake using the Data Transfer Service, currently in preview, staging data in the same cloud or Amazon S3 for AWS accounts. It supports cross-region batch loading and exporting data across any regions with simple commands, now generally available. Vector indexes support the TreeAH type with Google's ScaNN algorithm for efficient batch processing of many embeddings, also generally available.
Google Cloud has introduced a Generative AI Leader certification for non-technical professionals, reflecting the rising need for AI skills across various roles. This certification enables managers to leverage generative AI effectively. The $99 exam evaluates knowledge in AI fundamentals, Google Cloud's offerings, techniques for enhancing AI output, and business strategies. A free learning path is also provided to help candidates prepare.
Google has announced new SQL features in BigQuery that enhance data grouping and query flexibility. The GROUP BY STRUCT feature allows grouping of STRUCT types if all fields are groupable, applicable only in GROUP BY or SELECT DISTINCT clauses. The GROUP BY ARRAY feature supports ARRAY types under similar conditions. Additionally, the GROUP BY ALL clause infers grouping keys from SELECT items while excluding expressions with aggregate or window functions.
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Google has introduced enhanced grid line customization for cartesian charts in Looker Studio, allowing users to set specific colors and line styles for individual axis grid lines. This update improves clarity in multi-axis charts and is available for reports with modern charts enabled. This feature enhances visualization flexibility, giving data analysts more control over their visualizations and supporting clearer communication of information.
'Google has enhanced BigQuery with improved resource monitoring tools, including a six-hour event timeline and a new reservation slot usage view for better resource management. Users can visualize query execution flows more effectively with the new Query text section. Additionally, BigQuery now supports multimodal analysis and transformation workflows in SQL and Python, allowing integration of unstructured data and generative AI for analysis.
Google has introduced an autosave feature for BigQuery saved queries that automatically saves changes to query text two seconds after typing stops. These changes are visible in the Version history as "Your changes" but do not create a new version until saved. Users can save a new version through the Google Cloud console. This feature enhances productivity while allowing users to control when changes are visible to collaborators.
You can now use a Gemini powered assistant in BigQuery data canvas, which can construct and modify canvases to answer analytics questions. This feature is in Preview. The chat assistant can create nodes, run queries, and make visualizations based on user prompts. You can add data and instructions for the assistant to follow. To use it, click Open Data Canvas Assistant, enter prompts, and manage data and settings through the assistant interface.
Google has rolled out updates to BigQuery, enhancing data preparation, machine learning, and performance. Data preparation is now generally available, featuring AI suggestions for data cleansing and visual pipelines for workflow management. BigQuery ML supports remote models from Llama and Mistral AI for natural language tasks. Smart-tuning for materialized views improves performance and resource utilization for analytics workloads.
