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¿Qué es Kafka?

Apache Kafka es un sistema de streaming distribuido de código abierto ( open-source ) que se utiliza para stream processing , data pipelines en tiempo real y las integraciones de datos a escala. Fue creado originalmente para gestionar feed de datos en tiempo real en LinkedIn en 2011 y ahora, Kafka ha evolucionado de forma rápida de una messaging queue a una event streaming platform completa que tiene la capacidad de gestionar más de un millón de mensajes por segundo, o incluso, billones de mensajes al día.

¿Por qué elegir Kafka?

Kafka tiene muchas ventajas. En la actualidad, más del 80 % de las empresas Fortune 100 usan Kafka en prácticamente todos los sectores para innumerables casos de uso, ya sean grandes o pequeños. Es la tecnología de facto que desarrolladores y arquitectos usan para construir la generación más reciente de aplicaciones de data streaming que son escalables en tiempo real. Aunque esto se puede lograr con diversas tecnologías que están disponibles en el mercado, a continuación, se muestran las principales razones por las que Kafka es tan popular.

High Throughput

Capable of handling high-velocity and high-volume data, Kafka can handle millions of messages per second.

High Scalability

Scale Kafka clusters up to a thousand brokers, trillions of messages per day, petabytes of data, hundreds of thousands of partitions. Elastically expand and contract storage and processing.

Low Latency

Can deliver these high volume of messages using a cluster of machines with latencies as low as 2ms

Permanent Storage

Safely, securely store streams of data in a distributed, durable, reliable, fault-tolerant cluster

High Availability

Extend clusters efficiently over availability zones or connect clusters across geographic regions, making Kafka highly available and fault tolerant with no risk of data loss.

How Kafka Works

Apache Kafka consists of a storage layer and a compute layer that combines efficient, real-time data ingestion, streaming data pipelines, and storage across distributed systems. In short, this enables 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 data streaming platform no estaría completa sin la capacidad de procesar y analizar los datos tan pronto como se generan. Kafka Streams API es una biblioteca potente y ligera que facilita el procesamiento al instante, lo que permite añadir, crear ventanas con parámetros, realizar uniones de datos dentro de una stream y mucho más. Lo mejor de todo es que está creada como una aplicación Java que se apoya en Kafka y mantiene su workflow intacto sin clusters adicionales.

Almacenamiento duradero y persistente

Apache Kafka proporciona un almacenamiento duradero para una abstracción commit log distribuida que se encuentra habitualmente en las bases de datos repartidas. Kafka puede servir como «fuente de información fiable», ya que es capaz de distribuir los datos a través de múltiples nodos para una implementación de alta disponibilidad en un único centro de datos o a través de múltiples áreas de disponibilidad.

Publicar y suscribir

En su núcleo se encuentra el humilde e inmutable commit log, que permite suscribir (subscribe) y publicar (publish) datos a cualquier número de sistemas o aplicaciones en tiempo real. A diferencia de las colas de mensajería (messaging queues), Kafka es un sistema distribuido altamente escalable y resistente a fallos, lo que permite implementarlo para aplicaciones que se encarguen de gestionar la búsqueda de pasajeros y conductores en Uber, proporcionar análisis en tiempo real y mantenimiento predictivo para la domótica de British Gas y realizar numerosos servicios en tiempo real en LinkedIn. Este nivel de rendimiento único lo hace ideal para pasar de una aplicación a un uso en toda la empresa.

What is Kafka Used For?

Commonly used to build real-time streaming data pipelines and real-time streaming applications, today, there are hundreds of Kafka use cases. Any company that relies on, or works with data can find numerous benefits.

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.

Stream Processing

Stream processing includes operations like filters, joins, maps, aggregations, and other transformations which enterprises leverage to power many use-cases. Kafka Streams is a stream processing library built for Apache Kafka enabling enterprises to process data in real-time.Learn more

Streaming Analytics

Kafka provides high throughput event delivery, and when combined with open-source technologies such as Druid can form a powerful Streaming Analytics Manager (SAM). Druid consumes streaming data from Kafka to enable analytical queries. Events are first loaded in Kafka, where they are buffered in Kafka brokers before they are consumed by Druid real-time workers.

Streaming ETL

Real-time ETL with Kafka combines different components and features such as Kafka Connect source and sink connectors to consume and produce data from/to any other database, application, or API, Single Message Transform (SMT) – an optional Kafka Connect feature, Kafka Streams for continuous data processing in real-time at scale.

Event-Driven Microservices

Apache Kafka is the most popular tool for microservices because it solves many of the issues of microservices orchestration while enabling attributes that microservices aim to achieve, such as scalability, efficiency, and speed. It also facilitates inter-service communication while preserving ultra-low latency and fault tolerance.

Apache Kafka in Action

¿Quién utiliza Kafka?

Airbnb logo
Netflix
Goldman Sachs
Linkedin
Microsoft
New York Times
Intuit

To Maximize Kafka, You Need Confluent

Founded by the original developers of Kafka, Confluent delivers the most complete distribution of Kafka with Confluent, 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 your best people can focus on what they do best - delivering value to your business.

Cloud Kafka

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 Kafka

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 POC to production.

Kafka Everywhere

Everywhere

Distributed, complex data architectures can deliver the scale, reliability, and performance that unlocks use cases previously unthinkable, 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 Connect, manage the structure of data using Confluent Schema Registry, and process it in real time using ksqlDB. Confluent meets our customers everywhere 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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