Query materialized views with Java, Spring, and streaming database

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RDBMS Database Stream Processing cloud-native SQL

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  • risingwave-java-spring

    • Order delivery performance is a crucial metric for any business that sells products online. It reflects the efficiency and effectiveness of the order fulfillment process and directly impacts customer satisfaction. To analyze order delivery performance, businesses need to track key performance indicators (KPIs) such as delivery time, order accuracy, and delivery cost. In the scope of this article, we will create a sample order-delivery application (You can find the source code on GitHub) built using Java and Spring Boot and learn how to use a streaming database and materialized views to analyze food order delivery performance in real-time.

  • risingwave

    Cloud-native SQL stream processing, analytics, and management. KsqlDB and Apache Flink alternative. 🚀 10x more productive. 🚀 10x more cost-efficient.

    • We will spin up on our local environment the existing RisingWave fully-featured demo cluster on GitHub which is composed of multiple RisingWave components. To simplify this task, it leverages docker-compose.yaml file which includes additional containers for Kafka message broker, and data generation service.

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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  • PostgreSQL

    Mirror of the official PostgreSQL GIT repository. Note that this is just a *mirror* - we don't work with pull requests on github. To contribute, please see https://wiki.postgresql.org/wiki/Submitting_a_Patch

    • As RisingWave is wire-compatible with PostgreSQL, you can use the PostgreSQL JDBC driver to connect to RisingWave and interact with RisingWave from your Java applications. You can also use other clients’ libraries for different programming languages (NodeJS, Python, and Go).

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