80% of data leaders see the line between data and AI blurring. Leveraging large language models (LLM) for business data can provide a competitive edge, but effective data building, preparation, management, modeling, and scaling are crucial.
Thousands of organizations already use BigQuery with integrated AI capabilities to support their data cloud. In the AI-driven era, managing all data workloads simply is essential. BigQuery combines key features of multiple Google Cloud analytics services into a single, AI-ready platform, offering simplicity and scale for managing structured, unstructured, and streaming data with the best cost-performance ratio.
Table of Contents
- BigQuery advantages
- Support for all data types and open formats
- Using multiple languages and serverless engines on a single data copy
- Making real-time decisions and deploying ML models
- Enhancing analytics and AI through governance and enterprise features
- Unified experience for all data users
- Gemini in BigQuery: AI-Powered Collaboration
Table of Contents
- BigQuery advantages
- Support for all data types and open formats
- Using multiple languages and serverless engines on a single data copy
- Making real-time decisions and deploying ML models
- Enhancing analytics and AI through governance and enterprise features
- Unified experience for all data users
- Gemini in BigQuery: AI-Powered Collaboration
BigQuery advantages
- Expand your data and AI foundation by supporting all data types and open formats.
- Utilize your data at any scale without pre-sizing, thanks to a fully managed serverless workload management model and universal metastore.
- Enhance data team collaboration flexibility and agility by integrating multiple languages and engines (SQL, Spark, Python) into a single data copy.
- Support the end-to-end data to AI lifecycle with built-in high availability, data governance, and enterprise security features.
- Simplify analytics with a unified product experience designed for all data users, featuring AI-driven assistance and collaboration capabilities.
With BigQuery, you can efficiently integrate generative AI and large language models (LLM) into your data. BigQuery offers Gemini models via BigQuery ML and DataFrames, simplifying multimodal generative AI for enterprises. It unlocks value from unstructured data through extended integrations with Vertex AI’s document processing and speech-to-text API, and vector capabilities for AI-driven data searches. Insights from combining structured and unstructured data can further refine your LLM.
Support for all data types and open formats
Customers use BigQuery for managing all data types (structured and unstructured) with precise access control and integrated governance. BigLake serves as BigQuery’s unified storage engine, supporting open data tables for accessing structured and unstructured data using existing open source and legacy tools, within an integrated data platform. It supports major open data formats including Apache Iceberg, Apache Hudi, and Delta Lake, now seamlessly integrated with BigQuery, offering fully managed capabilities like DDL, DML, and streaming support for Iceberg.
For seamless access to structured, unstructured, and open format data, Google Cloud introduces BigQuery Metastore, a managed and scalable metadata service enabling detailed access control strategies for analytics and AI implementations. It supports Google Cloud, open-source engines (via connectors), and third-party partner engines.
Using multiple languages and serverless engines on a single data copy
Customers increasingly want to run multiple languages and engines on a single data copy, but the distributed nature of analytics and AI systems poses challenges. Now, you can apply Python and PySpark directly to your data within BigQuery!
BigQuery DataFrames combine Python’s power with BigQuery’s scalability and ease of use, supporting over 400 Pandas and scikit-learn APIs. This enables data scientists to explore, transform, and train on terabyte-scale data seamlessly.
Apache Spark is popular for data processing tasks, and serverless usage in Google Cloud has surged over 500% in the past year. Like other parts of BigQuery, Spark is fully serverless, allowing you to create and call PySpark stored procedures from SQL-based pipelines.
Making real-time decisions and deploying ML models
Data teams are increasingly tasked with providing real-time analytics and AI solutions to shorten the gap between signals, insights, and actions. BigQuery now simplifies real-time streaming data processing with its new support for continuous SQL queries, allowing data to be processed as it arrives via SQL statements.
BigQuery’s continuous queries enhance real-time connectivity across data and AI platforms, benefiting downstream SaaS applications like Salesforce. Additionally, Google Cloud has introduced a preview of Apache Kafka for BigQuery to support open-source streaming workloads. This allows customers to manage streaming data and deploy ML models without concerns about version upgrades, rebalancing, monitoring, and other operational complexities.
Enhancing analytics and AI through governance and enterprise features
Last year, Google Cloud integrated advanced data governance capabilities like data quality, lineage, and analysis from Dataplex directly into BigQuery. This integration aims to streamline data management, discovery, and governance. BigQuery now offers enhanced search capabilities supported by a unified metadata catalog from Dataplex, helping users discover data and AI assets, including models and datasets from Vertex AI. Column-level lineage tracking is in preview, with Vertex AI pipeline lineage to follow. Additionally, fine-grained access control governance rules are also in preview, allowing enterprises to define policies based on metadata.
Google Cloud introduces BigQuery Managed Disaster Recovery for customers needing geographic redundancy. Now in preview, this feature automates compute and storage failover and offers a tailored cross-region SLA for critical business workloads. It includes standby compute capacity in secondary regions and is part of the BigQuery Enterprise Plus pricing.
Unified experience for all data users
BigQuery, Google Cloud’s integrated data analytics platform, revolutionizes collaboration with BigQuery Studio. Launched at Next ’23, BigQuery Studio provides a collaborative workspace where data teams can seamlessly accelerate workflows from data to AI. It supports SQL, Python, PySpark, and natural language within a single interface, accommodating diverse data scales, formats, and locations. With comprehensive lifecycle features like team collaboration and version control, BigQuery Studio has quickly gained adoption among hundreds of thousands of users.
Gemini in BigQuery: AI-Powered Collaboration
Google Cloud introduces new features in BigQuery’s Gemini to enhance AI-driven data preparation, analysis, and engineering for data teams. It includes intelligent recommendations to boost user productivity and optimize costs. BigQuery’s Data Canvas enables fast and intuitive data discovery and exploration through AI-driven natural language input. AI-enhanced data preparation helps users clean, organize, and visualize data with low-code pipelines or rebuild existing ones. Gemini in BigQuery also simplifies SQL and Python coding with natural language prompts, leveraging relevant frameworks and metadata.
This article is adapted from Google Cloud’s official blog, showcasing the latest updates through Microfusion Technology. We’re committed to bringing you the most relevant and cutting-edge insights.
