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Complex Event Processing (CEP)

Dataconomy

Financial markets: Continuous trading data and market movements. Event identification and analysis Techniques employed in CEP for event identification include pattern recognition, machine learning, and trend analysis. Apache Kafka: Vital for creating real-time data pipelines and streaming applications.

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Streaming Data Pipelines: What Are They and How to Build One

Precisely

Business success is based on how we use continuously changing data. That’s where streaming data pipelines come into play. This article explores what streaming data pipelines are, how they work, and how to build this data pipeline architecture. What is a streaming data pipeline?

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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.

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Navigating the Big Data Frontier: A Guide to Efficient Handling

Women in Big Data

These procedures are central to effective data management and crucial for deploying machine learning models and making data-driven decisions. The success of any data initiative hinges on the robustness and flexibility of its big data pipeline. What is a Data Pipeline?

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7 Best Machine Learning Workflow and Pipeline Orchestration Tools 2024

DagsHub

Image generated with Midjourney In today’s fast-paced world of data science, building impactful machine learning models relies on much more than selecting the best algorithm for the job. A primer on ML workflows and pipelines Before exploring the tools, we first need to explain the difference between ML workflows and pipelines.

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How to Manage Unstructured Data in AI and Machine Learning Projects

DagsHub

Unstructured data makes up 80% of the world's data and is growing. Managing unstructured data is essential for the success of machine learning (ML) projects. Without structure, data is difficult to analyze and extracting meaningful insights and patterns is challenging.

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Mastering Duplicate Data Management in Machine Learning for Optimal Model Performance

DagsHub

In today's data-driven world, machine learning practitioners often face a critical yet underappreciated challenge: duplicate data management. A massive amount of diverse data powers today's ML models. You will find sections on managing duplicate data, best practices, current trends and so on.