Partitioning a billion-row table of soccer data using data context

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  • Spring Boot

    Spring Boot

  • My team has been developing the backend application that provides the most crucial data exploration features. We adopted Kotlin v1.6 running on top of a JVM (Java Virtual Machine) as the programming language, Spring Boot 2.5.3 as the framework, and Hibernate 5.4.32.Final as the ORM (Object Relational Mapping). The main reason why we opted for this technology stack is that speed is one of the most crucial business requirements. So, we needed a technology that could leverage heavy multi-thread processing, and Spring Boot turned out to be a reliable solution.

  • Hibernate

    Hibernate's core Object/Relational Mapping functionality

  • My team has been developing the backend application that provides the most crucial data exploration features. We adopted Kotlin v1.6 running on top of a JVM (Java Virtual Machine) as the programming language, Spring Boot 2.5.3 as the framework, and Hibernate 5.4.32.Final as the ORM (Object Relational Mapping). The main reason why we opted for this technology stack is that speed is one of the most crucial business requirements. So, we needed a technology that could leverage heavy multi-thread processing, and Spring Boot turned out to be a reliable solution.

  • 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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NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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