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Apache Kafka and Apache Flink: An open-source match made in heaven

IBM Journey to AI blog

It allows your business to ingest continuous data streams as they happen and bring them to the forefront for analysis, enabling you to keep up with constant changes. Apache Kafka boasts many strong capabilities, such as delivering a high throughput and maintaining a high fault tolerance in the case of application failure.

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Real-Time Sentiment Analysis with Kafka and PySpark

Towards AI

Real-time data streaming pipelines play a crutial role in achieving this objective. Within this article, we will explore the significance of these pipelines and utilise robust tools such as Apache Kafka and Spark to manage vast streams of data efficiently. Next, we run an SQL query to extract the data.

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Apache Kafka use cases: Driving innovation across diverse industries

IBM Journey to AI blog

Apache Kafka is an open-source , distributed streaming platform that allows developers to build real-time, event-driven applications. With Apache Kafka, developers can build applications that continuously use streaming data records and deliver real-time experiences to users. How does Apache Kafka work?

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Build Data Pipelines: Comprehensive Step-by-Step Guide

Pickl AI

Summary: This blog explains how to build efficient data pipelines, detailing each step from data collection to final delivery. Introduction Data pipelines play a pivotal role in modern data architecture by seamlessly transporting and transforming raw data into valuable insights.

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Apache Flink for all: Making Flink consumable across all areas of your business

IBM Journey to AI blog

Flink jobs, designed to process continuous data streams, are key to making this possible. How Apache Flink enhances real-time event-driven businesses Imagine a retail company that can instantly adjust its inventory based on real-time sales data pipelines. But that’s just the beginning.

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Comparing Tools For Data Processing Pipelines

The MLOps Blog

In this post, you will learn about the 10 best data pipeline tools, their pros, cons, and pricing. A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process.

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Discover the Most Important Fundamentals of Data Engineering

Pickl AI

Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering? Data Engineering is designing, constructing, and managing systems that enable data collection, storage, and analysis. They are crucial in ensuring data is readily available for analysis and reporting.