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Complex Event Processing (CEP)

Dataconomy

Apache Flink: A powerful open-source framework for distributed stream processing with an emphasis on event-driven applications. Apache Kafka: Vital for creating real-time data pipelines and streaming applications. IBM InfoSphere Streams: Provides tailored solutions for real-time data analytics and processing.

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What Are AI Credits and How Can Data Scientists Use Them?

ODSC - Open Data Science

Confluent Confluent provides a robust data streaming platform built around Apache Kafka. AI credits from Confluent can be used to implement real-time data pipelines, monitor data flows, and run stream-based ML applications.

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Ask HN: Who wants to be hired? (July 2025)

Hacker News

Prior to that, I spent a couple years at First Orion - a smaller data company - helping found & build out a data engineering team as one of the first engineers. We were focused on building data pipelines and models to protect our users from malicious phonecalls.

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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. IoT applications : Collecting and distributing sensor data from connected devices. Thats where message brokers come in.

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Best Data Engineering Tools Every Engineer Should Know

Pickl AI

Summary: Data engineering tools streamline data collection, storage, and processing. Tools like Python, SQL, Apache Spark, and Snowflake help engineers automate workflows and improve efficiency. Learning these tools is crucial for building scalable data pipelines.

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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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Enhanced diagnostics flow with LLM and Amazon Bedrock agent integration

Flipboard

Amazon Elastic Kubernetes Service (Amazon EKS) retrieves data from Amazon DocumentDB , processes it, and invokes Amazon Bedrock Agents for reasoning and analysis. This structured data pipeline enables optimized pricing strategies and multilingual customer interactions.

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