BigQuery 连续查询使数据分析实时化_AI阅读总结 — 包阅AI

The world operates in real time: Customers make purchases, financial transactions are made, goods are shipped, sensors generate data, and security threats emerge. The sheer volume of data generated by these real-time events is staggering. Yet, many businesses still rely on traditional, batch-oriented analysis that struggles to keep pace.

Among data analysts and engineers, BigQuery is a favorite for its ability to handle massive datasets and complex queries with ease. However, users are increasingly demanding expanded real-time capabilities to manage continuous data streams for both input and output. To address this challenge for customers, we have transformed BigQuery into a real-time, event-driven analytical platform. So today, we’re excited to launch BigQuery continuous queries, now available in preview.

BigQuery continuous queries is our answer to the challenge of the inherent cost and complexity of true real-time data analysis. Historically, “real-time” meant analyzing data that was minutes or even hours old. However, the landscape of data ingestion and analysis is rapidly evolving. The surge in data generation, customer engagement, decision-making, and AI-driven automation has drastically reduced the acceptable latency for decision-making. The demand for insights is no longer minutes or hours, it’s seconds.

Customer expectations have shifted dramatically, too. Today, they want real-time, personalized interactions across their online experiences. Businesses are under pressure to respond instantly and with all the relevant context, a feat that batch-oriented analysis simply cannot achieve.

Meeting these demands is hard. Even an enterprise data platform like BigQuery, while capable of high-throughput real-time data ingestion, was originally designed to perform analysis in a batch-oriented manner, where data is “pulled” from the system through ad-hoc or scheduled jobs, rather than “pushed” in an event-driven way. And while you could integrate additional technologies with BigQuery to enable streaming analysis, this often added architectural complexity, required diverse programming skills, and addressed a limited number of use cases.

Introducing BigQuery continuous queries

BigQuery continuous queries changes all that. With BigQuery continuous queries, you can execute continuously processing SQL statements that can process, analyze, and transform data as new events arrive in BigQuery, ensuring your insights are always up to date. The feature’s native integration with the Google Cloud ecosystem unlocks even more potential. You can harness the power of Vertex AI and Gemini to perform machine learning (ML) inference on incoming data in real time. Or perhaps you want to replicate the results of a continuous query to Pub/Sub topics, Bigtable instances, or even other BigQuery tables for further processing or analysis. It’s like having an always-on analyst at your disposal, constantly monitoring your data streams and triggering actions the moment something noteworthy occurs.

With BigQuery continuous queries, we’re dramatically expanding BigQuery’s abilities, empowering you with new dynamic and event-driven data processing capabilities, alongside its existing unified data platform strengths. This feature allows you to build applications that respond instantly to changes in your data, opening a new realm of possibilities. Craft personalized customer experiences on the fly, detect anomalies before they escalate, and automate decision-making processes, all with unprecedented agility.