Join Dan Sullivan for an in-depth discussion in this video Advantages of NoSQL databases, part of Advanced NoSQL for Data Science.
- [Instructor] NoSQL databases…share some common and important features.…They include flexible schemas.…This is really useful…because it means that we don't have to predefine…exactly what our database will look like.…Most NoSQL databases allow us to couple…keys and values,…kind of like columns and the values in those columns.…But because they're not a fixed definition,…we can add them and change them as we need to.…Another important shared feature…is that NoSQL databases distribute their data.…
This means that data's written to multiple servers…and that's important…because that allows multiple users…to read and write the data…without creating a bottleneck at any one server.…In addition, we can also increase the size of our cluster…if we need to.…For example, if more people start querying the database,…or if we get larger amounts of data.…A third important feature is called replication…and this is basically the idea or the process…of saving multiple copies of our data.…
This is useful…because it helps avoid bottlenecks when we're reading.…
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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