A curated list of recommended resources—books, articles, courses, and more—that can help deepen our data engineering knowledge. Please add or update this list as new materials become relevant.

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1. “Designing Data-Intensive Applications” by Martin Kleppmann
   • Covers data systems, pipelines, and architectures.
2. “The Data Warehouse Toolkit” by Ralph Kimball
   • Fundamentals of dimensional modeling and data warehouse design.
3. “Building Microservices” by Sam Newman
   • Helpful if our architecture involves microservices and data flows.

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1. Uber Engineering Blog
   • Offers insight into large-scale data solutions, real-time analytics, and more.
2. Netflix Tech Blog
   • Shares how they handle high-volume streaming and data pipeline optimizations.

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1. Coursera: Data Engineering on Google Cloud Platform
   • Broad overview of GCP’s data tools and pipelines.
2. Udemy: Data Warehouse & BI Fundamentals
   • Intro to data warehousing concepts, ETL, and basic BI techniques.

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• Strata Data Conference
  • Leading event for data science, machine learning, and analytics.
• Data Council
  • Community-driven event focusing on data engineering and data science.

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