19. Advanced SQL and NoSQL for Data Science

Sections

Navigate through the learning materials and practice exercises.

1. 19

   Advanced Sql And Nosql For Data Science

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   This section covers advanced SQL techniques and introduces NoSQL databases...

2. 19.1

   Advanced Sql Concepts

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   This section covers advanced SQL techniques such as subqueries, common table...

3. 19.1.1

   Subqueries And Nested Queries

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   Subqueries are queries nested within other queries, enhancing SQL's ability...

4. 19.1.2

   Common Table Expressions (Ctes)

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   Common Table Expressions (CTEs) improve SQL query readability and allow recursion.

5. 19.1.3

   Window Functions

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   Window functions allow for calculations across a set of table rows that are...

6. 19.1.4

   Pivoting And Unpivoting Data

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   This section introduces pivoting and unpivoting data techniques in SQL,...

7. 19.1.5

   Advanced Joins And Set Operations

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   This section covers advanced SQL techniques such as various types of joins...

8. 19.2

   Sql Optimization Techniques

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   This section covers key SQL optimization techniques that enhance database...

9. 19.2.1

   Indexing

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   Indexing is a technique used to enhance data retrieval performance in...

10. 19.2.2

    Query Execution Plan Analysis

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    This section covers the use of query execution plans to identify performance...

11. 19.2.3

    Materialized Views

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    Materialized views store the results of database queries for faster access.

12. 19.2.4

    Partitioning And Sharding

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    Partitioning and Sharding are techniques used to enhance database...

13. 19.3

    Introduction To Nosql Databases

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    NoSQL databases provide flexible data models and scalability for...

14. 19.3.1

    Why Nosql?

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    NoSQL databases provide flexibility and scalability, making them ideal for...

15. 19.3.2

    Document Databases

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    This section introduces document databases, emphasizing their structure and...

16. 19.3.3

    Key-Value Stores

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    Key-value stores are the simplest NoSQL database structures, known for high...

17. 19.3.4

    Column-Family Stores

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    Column-family stores are a type of NoSQL database optimized for large-scale...

18. 19.3.5

    Graph Databases

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    Graph databases utilize structured graph data models to efficiently...

19. 19.4

    Working With Mongodb For Data Science

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    This section covers the core functionalities of MongoDB including CRUD...

20. 19.4.1

    Crud Operations

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    This section introduces the fundamental CRUD operations in MongoDB, which...

21. 19.4.2

    Aggregation Pipeline

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    The aggregation pipeline in MongoDB facilitates processing and transforming...

22. 19.4.3

    Indexing In Mongodb

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    Indexing in MongoDB significantly improves the read performance of query operations.

23. 19.4.4

    Geospatial And Text Search

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    This section introduces how geospatial and text search functionalities can...

24. 19.5

    Choosing Between Sql And Nosql

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    This section discusses the characteristics and use cases of SQL and NoSQL...

25. 19.6

    Using Sql And Nosql Together

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    This section discusses the advantages of using both SQL and NoSQL databases...