- [Instructor] Now let's spend a few minutes reviewing…some key concepts about graphs.…Nodes represent entities.…Entities can be pretty much anything…that can be described with a noun.…Nodes have properties that are represented…as key value pairs and stored with a node.…Properties of nodes are used like columns…in relational databases…and like key value pairs in document databases.…It's important to remember that graphs can contain…different types of nodes just as a relational database…can store different types of tables.…Edges have properties too.…
Edges are used to link entities…and represent relationships.…Relationships can be directed or undirected.…For example, if a graph represents a model…of how data passes through a workflow,…there may be a directed edge…from one application to another.…An employee may have a directed edge…from their node to their manager's node.…Edges may be symmetric or asymmetric.…Symmetric edges are relationships…that are the same in both directions.…For example, the distance between two cities is symmetric.…
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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