Join Dan Sullivan for an in-depth discussion in this video JSON structures, part of Advanced NoSQL for Data Science.
- [Instructor] Before continuing our discussion…of document databases,…I'd like to spend a few minutes describing JSON structures.…JSON structures are easy…for both humans and machines to read.…They're basically lists of key-value pairs.…These lists start with an open curly bracket,…have a list of key-value pairs,…and then end with a closing curly bracket.…Keys are typically strings.…Now values can take on a wider range including strings,…or variable length characters, numbers, Boolean values,…as well as arrays and embedded JSON objects.…
Here is an example with an employee collection.…Each document is made up of an employee ID,…first and last names, and a list of projects.…The projects list is an array of ID numbers…and that refers to project structures in another collection.…Now if you're familiar with relational modeling,…you'll probably recognize the similarity to foreign keys.…The project collection includes project ID, name,…and a set of team IDs that refer to teams…that are assigned to this project.…The structure is somewhat denormalized…
The course begins with an introduction to NoSQL, and then delves into the specifics of document, wide-column, and graph databases. Learn key details for performing data preparation, exploration, and extraction for each type of NoSQL database. Review case studies that show how to use various NoSQL databases with popular data science tools, including the document database MongoDB, the wide-column database Cassandra, and the graph database Neo4j.
- NoSQL compared to traditional relational databases
- Performing common data science tasks
- Preparing data with document databases
- Manipulating data in NoSQL
- Preparing, exploring, extracting, and model building
- Working with document, wide-column, and graph databases
- Reviewing case studies using MongoDB, Cassandra, and Neo4j
Skill Level Advanced
1. Why NoSQL?
Types of NoSQL databases2m 20s
2. Perform Common Data Science Tasks with NoSQL Databases
3. Document Databases for Data Science
4. Wide-Column Databases for Data Science
5. Graph Databases for Data Science
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