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Course Outline

Lesson 1 - SQL Fundamentals:

  • Select statements
  • Join types
  • Indexes
  • Views
  • Subqueries
  • Union operations
  • Creating tables
  • Loading data
  • Dumping data
  • NoSQL overview

Lesson 2 - Data Modeling:

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

Lesson 3 - Indexing in the NoSQL and Data Science Realm:

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

Lesson 4 - NoSQL and Unstructured Data:

  • When to use NoSQL
  • Eventually consistent data
  • Schema-on-read vs. Schema-on-write

Lesson 5 - SQL for Data Analytics:

  • Windowing functions
  • Lateral Joins
  • Lead and Lag functions

Lesson 6 - HiveQL:

  • SQL Support
  • External and Internal Tables
  • Joins
  • Partitions
  • Correlated subqueries
  • Nested queries
  • When to use Hive

Lesson 7 - Redshift:

  • Design and structure
  • Locks and shared resources
  • Differences from Postgres
  • When to use Redshift

Requirements

  • A fundamental understanding of databases.
  • Experience with SQL is advantageous.

Audience

  • Business analysts
  • Software developers
  • Database developers
 14 Hours

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