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¿Qué es Apache Kafka®?
Apache Kafka es un sistema de streaming distribuido y de código abierto que se utiliza para el procesamiento en streaming, las pipelines de datos en tiempo real y las integraciones de datos a gran escala. Creada originalmente para gestionar los feeds de datos en tiempo real de LinkedIn en 2011, Kafka ha evolucionado rápidamente de una cola de mensajería a una plataforma de streaming de eventos completa que puede gestionar más de un millón de mensajes por segundo (y billones de mensajes al día).
Fundada por los creadores originales de Apache Kafka, Confluent ofrece los tutoriales, formación, servicios y asistencia de Kafka más completos del mercado. Además, Confluent ofrece servicios de streaming de datos totalmente gestionados y cloud-native que están diseñados para cualquier entorno en la nube, lo que garantiza la escalabilidad y la fiabilidad que necesitan las infraestructuras de datos de hoy día.
¿Por qué elegir Kafka?
Kafka tiene muchas ventajas. En la actualidad, más del 80 % de las empresas que aparecen en la lista Fortune 100 usan Kafka, en prácticamente todos los sectores y para innumerables casos de uso de cualquier tamaño. Kafka es la tecnología de facto que los desarrolladores y arquitectos utilizan para crear la última generación de aplicaciones de streaming de datos escalables en tiempo real.
Aunque esto se puede lograr con varias de las tecnologías disponibles en el mercado, aquí tienes algunas de los principales motivos por los que Kafka es tan popular.
High Throughput
Kafka is capable of handling high-velocity and high-volume data, processing millions of messages per second. This makes it ideal for applications requiring real-time data processing and integration across multiple servers.
High Scalability
Kafka clusters can be scaled up to a thousand brokers, handling trillions of messages per day and petabytes of data. Kafka's partitioned log model allows for elastic expansion and contraction of storage and processing capacities. This scalability ensures that Kafka can support a vast array of data sources and streams.
Low Latency
Kafka can deliver a high volume of messages using a cluster of machines with latencies as low as 2ms. This low latency is crucial for applications that require real-time data processing and immediate responses to data streams.
Permanent Storage
Kafka safely and securely stores streams of data in a distributed, durable, and fault-tolerant cluster. This ensures that data records are reliably stored and can be accessed even in the event of server failure. The partitioned log model further enhances Kafka's ability to manage data streams and provide exactly-once processing guarantees.
High Availability
Kafka can extend clusters efficiently over availability zones, or connect clusters across geographic regions. This high availability makes Kafka fault-tolerant with no risk of data loss. Kafka’s design allows it to manage multiple subscribers and external stream processing systems seamlessly.
How Does Apache Kafka Work?
Apache Kafka consists of a storage layer and a compute layer, which enable efficient, real-time data ingestion, streaming data pipelines, and storage across distributed systems. Its design facilitates simplified data streaming between Kafka and external systems, so you can easily manage real-time data and scale within any type of infrastructure.
Procesamiento a escala en tiempo real
Una plataforma de streaming de datos no estaría completa si no pudiese procesar y analizar los datos en el momento que se generan. La API de Kafka Streams es una biblioteca potente y ligera que facilita el procesamiento instantáneo, lo que permite añadir, crear ventanas con parámetros y unir datos dentro de un stream, entre muchas otras funciones. Está creada como una aplicación Java sobre Kafka, para que puedas mantener la continuidad del flujo de trabajo sin necesidad de clusters adicionales para gestionarla.
Almacenamiento duradero y persistente
Kafka ofrece un almacenamiento duradero al abstraer el registro distribuido de confirmaciones (commits) que se suele encontrar en las bases de datos distribuidas. Esto hace que Kafka pueda actuar como una «fuente única de información» y distribuir los datos en varios nodos para una alta disponibilidad en el despliegue, ya sea en un único centro de datos o en varias zonas de disponibilidad. Este almacenamiento duradero y persistente garantiza la integridad y la fiabilidad de los datos, incluso en caso de fallos en el servidor.
Publicar y suscribir
Kafka cuenta con un registro de confirmaciones (commits) humilde e inmutable. Los usuarios pueden suscribirse a ese registro y publicar datos en tantos sistemas o aplicaciones en tiempo real como elijan. A diferencia de las colas de mensajería tradicionales, Kafka es un sistema distribuido con una alta escalable y tolerancia a los errores. Esto permite a Kafka escalar desde aplicaciones individuales hasta implementaciones en toda la empresa. Por ejemplo, Kafka se utiliza para gestionar el emparejamiento de pasajeros y conductores en Uber, proporcionar análisis en tiempo real y mantenimiento predictivo en las casas inteligentes de British Gas y ofrecer numerosos servicios en tiempo real en LinkedIn.
What is Kafka Used For?
Commonly used to build real-time streaming data pipelines and real-time streaming applications, Kafka supports a vast array of use cases. Any company that relies on, or works with data, can find numerous benefits in utilizing Kafka.
Data Pipelines
In the context of Apache Kafka, a streaming data pipeline means ingesting the data from sources into Kafka as it’s created, and then streaming that data from Kafka to one or more targets. This allows for seamless data integration and efficient data flow across different systems.
Stream Processing
Stream processing includes operations like filters, joins, maps, aggregations, and other transformations that enterprises leverage to power many use cases. Kafka Streams, a stream processing library built for Apache Kafka, enables enterprises to process data in real-time, making it ideal for applications requiring immediate data processing and analysis.
Streaming Analytics
Kafka provides high throughput event delivery. When combined with open-source technologies such as Druid, it can form a powerful Streaming Analytics Manager (SAM). Druid consumes streaming data from Kafka to enable analytical queries. Events are first loaded into Kafka, where they are buffered in Kafka brokers, then they are consumed by Druid real-time workers. This allows for real-time analytics and decision-making.
Streaming ETL
Real-time ETL with Kafka combines different components and features such as Kafka Connect source and sink connectors, used to consume and produce data from/to any other database, application, or API; Single Message Transforms (SMT)—an optional Kafka Connect feature; and Kafka Streams for continuous data processing in real-time at scale. Altogether they ensure efficient data transformation and integration.
Event-Driven Microservices
Apache Kafka is the most popular tool for microservices, because it solves many issues related to microservices orchestration, while enabling attributes that microservices aim to achieve, such as scalability, efficiency, and speed. Kafka also facilitates inter-service communication, preserving ultra-low latency and fault tolerance. This makes it essential for building robust and scalable microservices architectures.
By using Kafka's capabilities, organizations can build highly efficient data pipelines, process streams of data in real time, perform advanced analytics, and develop scalable microservices—all ensuring they can meet the demands of modern data-driven applications.
Apache Kafka in Action
¿Quién utiliza Kafka?
Algunas de las marcas más importantes del mundo utilizan Kafka:
To Maximize Kafka, You Need Confluent
Founded by the original developers of Kafka, Confluent delivers the most complete distribution of Kafka, improving Kafka with additional community and commercial features designed to enhance the streaming experience of both operators and developers in production, at massive scale.
You love Apache Kafka®, but not managing it. Confluent's cloud-native, complete, and fully managed service goes above & beyond Kafka, so that your best people can focus on delivering value to your business.
Cloud-Native
We’ve re-engineered Kafka to provide a best-in-class cloud experience, for any scale, without the operational overhead of infrastructure management. Confluent offers the only truly cloud-native experience for Kafka—delivering the serverless, elastic, cost-effective, highly available, and self-serve experience that developers expect.
Complete
Creating and maintaining real-time applications requires more than just open-source software and access to scalable cloud infrastructure. Confluent makes Kafka enterprise-ready and provides customers with the complete set of tools they need to build apps quickly, reliably, and securely. Our fully managed features come ready out of the box, for every use case from proof of concept (POC) to production.
Everywhere
Distributed, complex data architectures can deliver the scale, reliability, and performance to unlock previously unthinkable use cases, but they're incredibly complex to run. Confluent's complete, multi-cloud data streaming platform makes it easy to get data in and out of Kafka with Connect, manage the structure of data using Confluent Schema Registry, and process it in real time using ksqlDB. Confluent meets customers wherever they need to be — powering and uniting real-time data across regions, clouds, and on-premises environments.
Get Started in Minutes
By integrating historical and real-time data into a single source of truth, Confluent makes it easy to build an entirely new category of modern, event-driven applications, gain a universal data pipeline, and unlock powerful new use cases with full scalability, security, and performance.
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