#bigqueryengine

The goal of today’s companies is to become “by-the-second” enterprises that can quickly adjust to shifts in their inventory, supply chain, consumer behavior, and other areas. Additionally, they aim to deliver outstanding customer experiences, whether it is via online checkout or support interactions. All businesses, regardless of size or budget, should have access to real-time intelligence, in opinion, and it should be linked into a single data platform so that everything functions as a whole. With the release of BigQuery Engine for Apache Flink in preview today, we’re making significant progress in assisting companies in achieving these goals.

BigQuery Engine for Apache Flink

Construct and operate applications that are capable of real-time streaming by utilizing a fully managed Flink service that is linked with BigQuery.

Features

Update your unified data and AI platform with real-time data

Using a scalable and well-integrated streaming platform built on the well-known Apache Flink and Apache Kafka technologies, make business decisions based on real-time insights. You can fully utilize your data when paired with Google’s unique AI/ML capabilities in BigQuery. With built-in security and governance, you can scale efficiently and iterate quickly without being constrained by infrastructure management.

Use a serverless Flink engine to save time and money

Businesses use Google Cloud to develop streaming apps in order to benefit from real-time data. The operational strain of administering self-managed Flink, optimizing innumerable configurations, satisfying the demands of various workloads while controlling expenses, and staying up to date with updates, however, frequently weighs them down. The serverless nature of BigQuery Engine for Apache Flink eases this operational load and frees its clients to concentrate on their core competencies, which include business innovation.

Compatible with Apache Flink, an open source project

Without rewriting code or depending on outside services, BigQuery Engine for Apache Flink facilitates the lifting and migration of current streaming applications that use the free source Apache Flink framework to Google Cloud. Modernizing and migrating your streaming analytics on Google Cloud is simple when you combine it with Google Managed Service for Apache Kafka (now GA).

Streamling ETL

ETL streaming for your data platform that is AI-ready

An open and adaptable framework for real-time ETL is offered by Apache Flink, which enables you to ingest data streams from sources like as Kafka, carry out transformations, and then immediately load them into BigQuery for analysis and storage. With the advantages of open source extensibility and adaptation to various data sources, this facilitates quick data analysis and quicker decision-making.

Create applications that are event-driven

Event-driven apps assist businesses with marketing personalization, recommendation engines, fraud detection models, and other issues. The managed Apache Kafka service from Google Cloud can be used to record real-time event streams from several sources, such as user activity or payments. These streams are subsequently processed by the Apache Flink engine with minimal latency, allowing for sophisticated tasks like real-time processing.

Build a real-time data and AI platform

Apache’s BigQuery Engine You may use Flink for stream analytics without having to worry about infrastructure management. Use the SQL or DataStream APIs in Flink to analyze data in real time. Stream your data to BigQuery and link it to visualization tools to create dashboards. Use Flink’s libraries for streaming machine learning and keep an eye on work performance.

The cutting-edge real-time intelligence platform offered by BigQuery Engine for Apache Flink enables users to:

Utilize Google Cloud‘s well-known streaming technology. Without rewriting code or depending on outside services, BigQuery Engine for Apache Flink facilitates the lifting and migration of current streaming applications that use the open-source Apache Flink framework to Google Cloud. Modernizing and migrating your streaming analytics on Google Cloud is simple when you combine it with Google Managed Service for Apache Kafka (now GA).

Lessen the strain on operations. Because BigQuery Engine for Apache Flink is completely serverless, it lessens operational load and frees up clients to concentrate on their core competencies innovating their businesses.

Give AI real-time data. A scalable and well-integrated streaming platform built on the well-known Apache Flink and Apache Kafka technologies that can be combined with Google’s unique AI/ML capabilities in BigQuery is what enterprise developers experimenting with gen AI are searching for.

With the arrival of BigQuery Engine for Apache Flink, Google Cloud customers are taking advantage of numerous real-time analytics innovations, such as BigQuery continuous queries, which allow users to use SQL to analyze incoming data in BigQuery in real-time, and Dataflow Job Builder, which assists users in defining and implementing a streaming pipeline through a visual user interface.

Google cloud streaming offering now includes popular open-source Flink and Kafka systems, SQL-based easy streaming with BigQuery continuous queries, and sophisticated multimodal data streaming with Dataflow, including support for Iceberg, thanks to BigQuery Engine for Apache Flink. These features are combined with BigQuery, which links your data to top AI tools in the market, such as Gemma, Gemini, and open models.

New AI capabilities unlocked when your data is real-time

It is evident that generative AI has rekindled curiosity about the possibilities of data-driven experiences and insights as a turn to the future. When AI, particularly generative AI, has access to the most recent context, it performs best. Retailers can customize their consumers’ purchasing experiences by fusing real-time interactions with historical purchase data. If your business provides financial services, you can improve your fraud detection model by using real-time transactions. Fresh data for model training, real-time user support through Retrieval Augmented Generation (RAG), and real-time predictions and inferences for your business applications including incorporating tiny models like Gemma into your streaming pipelines are all made possible by real-time data coupled to AI.

In order to enable real-time data for your future AI use cases, it is adopting a platform approach to introduce capabilities across the board, regardless of the particular streaming architecture you want or the streaming engine you select. Building real-time AI applications is now easier than ever with to features like distributed counting in Bigtable, the RunInference transform, support for Vertex AI text-embeddings, Dataflow enrichment transforms, and many more.

When it comes to enabling your unified data and AI platform to function in real-time data, Google cloud are thrilled to put these capabilities in your hands and keep providing you with additional options and flexibility. Get started utilizing BigQuery Engine for Apache Flink right now in the Google Cloud console by learning more about it.