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Course Outline

Lesson 1 - SQL basics:

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

Lesson 2 - Data Modeling:

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

Lesson 3 - Index in the NoSQL/Data science world

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

Lesson 4 - NoSQL and non-structured data

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

Lesson 5 - SQL for data analytics

  • Windowing function.
  • Lateral Joins.
  • Lead & Lag.

Lesson 6 - HiveQL

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

Lesson 7 - Redshift

  • Design and structured.
  • Locks and shared resources.
  • Postgres differences.
  • When to use Redshift.

Requirements

  • An understanding of databases.
  • Experience with SQL is an asset.

Audience

  • Business analysts.
  • Software developers.
  • Database developers.
 14 Hours

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