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Course Outline

Module 1 - Fundamentals of SQL: 

  • Select statements
  • Join types
  • Indexes
  • Views
  • Subqueries
  • Union
  • Table creation
  • Data loading
  • Data extraction
  • NoSQL

Module 2 - Data Modeling:

  • Transaction-based ER systems
  • Data warehousing 
  • Data warehouse architectures
    • Star schema
    • Snowflake schemas
  • Slowly changing dimensions (SCD)
  • Structured and unstructured data
  • Various table storage engines:
    • Column-based
    • Document-based
    • In-Memory

Module 3 - Indexing in the NoSQL and Data Science Context

  • Constraints (Primary)
  • Index-based scanning
  • Performance optimization

Module 4 - NoSQL and Unstructured Data

  • Scenarios for NoSQL implementation
  • Eventual consistency
  • Schema-on-read vs. Schema-on-write

Module 5 - SQL for Data Analytics

  • Window functions
  • Lateral joins
  • Lead & Lag functions

Module 6 - HiveQL

  • SQL support
  • External and internal tables
  • Joins
  • Partitions
  • Correlated subqueries
  • Nested queries
  • Use cases for Hive

Module 7 - Redshift

  • Design and structure
  • Locking and shared resources
  • Differences from Postgres
  • Use cases for Redshift

Requirements

  • Familiarity with database concepts
  • Prior experience with SQL is advantageous.

Target Audience

  • Business analysts
  • Software developers
  • Database developers
 14 Hours

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