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This course explores the Lakehouse architecture and Medallion design for scalable data workflows, focusing on Unity Catalog for secure data governance, access control, and lineage tracking. The curriculum includes building reliable, ACID-compliant pipelines with Delta Lake. You'll examine Spark optimization techniques, such as partitioning, caching, and query tuning, and learn performance monitoring, troubleshooting, and best practices for efficient data engineering and analytics to address real-world challenges.

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What You'll Learn

  • Monitoring and Optimizing Apache Spark Workloads on Databricks

Who Should Attend

• N/A
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Prerequisites

  • Basic programming knowledge
  • Familiarity with Python
  • Basic understanding of SQL queries (SELECT, JOIN, GROUP BY)
  • Familiarity with data processing concepts
  • No prior Spark or Databricks experience required

Learning Journey

Coming Soon...

Monitoring and Optimizing Apache Spark Workloads on Databricks

  • Apache Spark and Databricks
  • Using Apache Spark with Delta Lake
  • Demo: Introduction to Delta Lake
  • Lab: Introduction to Delta Lake
  • Optimizing Apache Spark
  • Demo: Optimizing Apache Spark
  • Lab: Optimizing Apache Spark

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Frequently Asked Questions (FAQs)

None

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Course Curriculum

Course Curriculum

Training Schedule

Training Schedule

Exam & Certification

Exam & Certification

FAQs

Frequently Asked Questions

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