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LinkedIn Learning

Advanced SQL for Data Scientists

via LinkedIn Learning

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Learn advanced techniques for analyzing large data sets with SQL. Find out how to build sophisticated data models, optimize queries, extend SQL with user-defined functions, and more.

Syllabus

Introduction
  • Advanced SQL techniques for data science
  • What you should know
1. Data Modeling: Tables
  • Rules of normalization
  • Denormalization
  • Partitioning data
  • Materialized views
  • Read replicas
  • Challenge: Design a data model for analytics
  • Solution: Design a data model for analytics
2. Data Modeling: Indexes
  • B-tree indexes
  • Bitmap indexes
  • Hash indexes
  • GiST and SP-GiST indexes
  • GIN and BRIN indexes
  • Challenge: Choosing an optimal indexing strategy
  • Solution: Choosing an optimal indexing strategy
3. Query Optimization
  • EXPLAIN and ANALYZE commands
  • Generating data with generate_sequence
  • Generating time series data
  • Analyzing a query with WHERE clauses and indexes
  • Analyzing a query with a join
  • Challenge: Optimize a query using an explain plan
  • Solution: Optimize a query using an explain plan
4. User-Defined Functions
  • Extending SQL with user-defined functions
  • SQL query functions
  • Function overloading
  • Function volatility
  • PL/Python functions
  • Challenge: Write a user-defined function
  • Solution: Write a user-defined function
5. Special-Purpose Functionality
  • Federated queries
  • Bloom filters
  • Hstore for key-value pairs
  • JSON for semi-structured data
  • Hierarchical data and ltrees
  • Challenge: Design a table to support unstructured data
  • Solution: Design a table to support unstructured data
Conclusion
  • Next steps

Taught by

Dan Sullivan

Reviews

4.7 rating at LinkedIn Learning based on 480 ratings

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