Join Dan Sullivan for an in-depth discussion in this video Graph data models, part of Advanced NoSQL for Data Science.
- [Instructor] Graph databases are a type…of NoSQL database, and are perhaps…the most different from relational databases.…Instead of explicitly grouping related objects…in a table or document, graphs track individual instances…of an entity as a separate object.…Graphs are made up of two types…of objects: entities and relationships.…Entities are used to model things…like persons, places, and things.…Entities are typically described by nouns.…Relationships describe how entities relate to one another.…For example, one employee might be…the manager of another employee,…and they both may be members of the same team.…
There are similarities between…graph and relational databases.…Tables and relational databases represent entities.…Relationships are implemented by using…foreign keys between tables.…Columns in relational tables are where attributes…of entities are stored.…When we want to work with entities across multiple types…of entities, we use joins to link the data.…In a graph database, entities are represented as nodes.…
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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