- [Instructor] When preparing to load data into Cassandra,…there are several factors to keep in mind.…As with other no SQL databases,…data is denormalized.…There's little choice in this matter.…Cassandra does not support sorting of query results,…it also lacks support for joins.…The only way to ensure your query results…include all the attributes you want…and are returned in the order you want,…is to denormalize in order rows by cluster key.…Do not model based on rules of normalization.…Focus on designing tables to answer queries.…
Normalizing data in a wide column database…will negatively impact performance.…Since different queries will have different attributes…and sort order requirements,…it is common to have multiple tables…with duplicate data in different orders.…Cassandra is used for big data applications.…Be sure to consider the volume of data…that you will need to load…and the time that could take.…Import utilities are usually faster…than code-written in Python…or other scripting languages,…but you can perform more…
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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