- Let's summarize some of the most important pieces…of information about wide-column databases like Cassandra.…Build your data model based on how you will query your data.…Do not model based on the rules of normalization.…This is a common mistake among new Cassandra data modelers.…It can lead to terrible performance.…Start your data modeling work by documenting…the kinds of queries you will be responding to.…If you don't know what those queries are,…then stop modeling.…There's no point in proceeding until you know your queries.…Tables should be designed to answer one or more queries.…
Ideally, you should be able to use a table…for more than query, but that's not always possible.…Remember, tables in Cassandra are ordered…and sorting is not provided in the Cassandra query language.…Consider the importance of consistency over performance.…Cassandra implements eventual consistency…which means there are times when replicas are out of sync.…Be aware that a read operation can return inconsistent data…if all replicas are not in sync.…
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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