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Challenges in Building and Maintaining Data Products for AI and ML (Part 2)

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Building and maintaining data products for AI and ML is not just about collecting data—it is about ensuring data quality, scalability, and accessibility. Without addressing these challenges, AI models will produce unreliable results, and organizations will struggle to use data effectively. Two of the biggest challenges in this area are data quality and scalability. Ensuring […]

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Trade Monitoring and Pattern Matching with Flink and Kafka

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Financial markets generate one of the densest streams of real-time data we can observe today. Price ticks, order submissions, cancellations, executions, and settlement instructions all occur at millisecond scale. Within that torrent of activity, regulators and trading firms need to detect suspicious behavior: wash trades, spoofing, layering, or coordinated account activity. The traditional approach—batch analysis

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Apache Flink: Unleashing the Power of Stream Processing

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In the evolving landscape of real-time data processing, Apache Flink has emerged as one of the leading stream processing frameworks, offering capabilities that go far beyond traditional data batch processing systems. As businesses increasingly rely on real-time data to drive decision-making, Flink has become an essential tool, enabling organizations to process vast amounts of data

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