Remove 2011 Remove Analytics Remove Apache Kafka
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Build a Scalable Data Pipeline with Apache Kafka

Analytics Vidhya

Introduction Apache Kafka is a framework for dealing with many real-time data streams in a way that is spread out. It was made on LinkedIn and shared with the public in 2011.

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A Detailed Guide of Interview Questions on Apache Kafka

Analytics Vidhya

Introduction Apache Kafka is an open-source publish-subscribe messaging application initially developed by LinkedIn in early 2011. It is a famous Scala-coded data processing tool that offers low latency, extensive throughput, and a unified platform to handle the data in real-time.

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Big Data – Lambda or Kappa Architecture?

Data Science Blog

Big Data Analytics stands apart from conventional data processing in its fundamental nature. Lambda – Architecture Introduced in 2011 during the peak of Big Data’s prominence, the Lambda architecture remains a significant presence in the field. The Lambda architecture effectively balances speed, reliability, and scalability.

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Use streaming ingestion with Amazon SageMaker Feature Store and Amazon MSK to make ML-backed decisions in near-real time

AWS Machine Learning Blog

Streaming ingestion – An Amazon Kinesis Data Analytics for Apache Flink application backed by Apache Kafka topics in Amazon Managed Streaming for Apache Kafka (MSK) (Amazon MSK) calculates aggregated features from a transaction stream, and an AWS Lambda function updates the online feature store.

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Major Differences: Kafka vs RabbitMQ

Pickl AI

Two of the most popular message brokers are RabbitMQ and Apache Kafka. In this blog, we will explore RabbitMQ vs Kafka, their key differences, and when to use each. Kafka excels in real-time data streaming and scalability. RabbitMQ uses a push-based model, while Kafka follows a pull-based model.