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Articles by import org.apache.avro.Schema

Enhancing Avro With Semantic Metadata Using Logical Types

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By import org.apache.avro.Schema, import org.apache.avro.Conversion, Import UserProfile, Import Org.apache.avro.file. DataFileReader
| DZone Verified
Apache Avro is a widely used data format that keeps things compact and efficient while making it easy to evolve schemas over time. By default, it comes with basic data types like int, long, string, and bytes. But what if you need to store something more specific, like a date or a decimal number? That’s where logical types come in. Logical types let you add semantic meaning to your data.
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Beyond Java Serialization: 5 High-Performance Alternatives for Modern Applications

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By import org.apache.avro.Schema, Import Org.apache.avro.file. DataFileReader, Import Org.apache.avro.file. DataFileWriter, Import Org.apache.avro.specific.specific DatumReader
| DEV Community
As a best-selling author, I invite you to explore my books on Amazon. Don't forget to follow me on Medium and show your support. Thank you! Your support means the world! Java serialization represents a fundamental process in transferring objects across networks or storing them persistently. However, Java's native serialization mechanism presents notable challenges in performance, security, and flexibility.
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Como conseguir y la consumición entregada y ordenada de mensajes con Kafka

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By Import Config, import java.io.File, import java.io.IOException, import org.apache.avro.Schema
| DEV Community
Para asegurar que los eventos se envían y consumen en un orden totalmente consistente en Apache Kafka, es fundamental entender cómo funciona la partición de mensajes y la asignación de consumidores. Uso de Particiones en Kafka Particionamiento de Temas: Kafka organiza los mensajes en particiones dentro de un tema. Cada partición mantiene el orden de los mensajes que recibe, lo que significa que los mensajes se procesan en el orden en que fueron enviados a esa partición.
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