Join Dan Sullivan for an in-depth discussion in this video Tips for using graph databases for data science, part of Advanced NoSQL for Data Science.
- [Narrator] Here are some tips for using graph databases.…When modeling, think in terms…of entities and their relationships.…Graphs are especially good at modeling networks…like social networks, computer networks,…flows of information, and physical flows,…like traffic in a city.…Graphs can be non-hierarchical or hierarchical.…If you have to model complex structures,…like an electronic device…with multiple components and subcomponents,…then a graph may be a good structure for you.…Think of the way you would like to query nodes and edges,…and be sure to include properties…that allow you to filter on those characteristics.…
There are many ways to analyze graphs.…You can start at a node…and explore its neighborhood of linked entities.…If there are multiple types of entities in a graph,…you might want to analyze patterns of links…between different types.…Some nodes are more important than others.…For example, airlines sometimes use…a few major airports as hubs.…Hubs have many incoming and outgoing links.…If you find nodes in your network…
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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