Blog Tags: Real Time Processing
Choosing the Right Agile Framework: A Data Engineer’s Guide for 12 Key Projects
Agile is fast becoming the rulebook for data engineers navigating high-stakes projects from migrations to real-time fraud detection. By tailoring frameworks like Scrum, Kanban, SAFe, or the Spotify Model to specific delivery challenges, teams can unlock speed, clarity, and resilience. In this blog, we share real-world project examples and best practices that show how the right Agile approach transforms data engineering outcomes.
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Building Dynamic Data Pipelines with Snowpark: Our Framework to Drive Modern Data Transformation
Learn about the challenges of traditional data transformation methods and how a dynamic approach using metadata configuration can help address these issues. By defining transformation rules and specifications, enterprises can create flexible pipelines that adapt to their evolving data processing needs, ultimately accelerating the process of extracting insights from data.
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Maximize Spark: Real-Time NiFi Alerts and Automated Log Capturing
Learn how to streamline log tracking for complex NiFi pipelines by using the NiFi REST API and configuring custom settings. See how to create an automated logging pipeline to capture logs in Spark tables and trigger email alerts for errors and statuses with a detailed, step-by-step guide to set up this process.
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