Join Dan Sullivan for an in-depth discussion in this video Wide-column data models, part of Advanced NoSQL for Data Science.
- [Instructor] It's time to turn our attention…to wide-column databases, such as Cassandra.…There are other wide-column data stores,…such as Google Bigtable,…but we will use Cassandra as our example database.…These databases are best known…for their use with big data applications.…Cassandra is designed to run with hundreds of servers…in a single cluster.…It provides high performance read and write operations,…but it's especially useful in applications…that demand high-speed rights.…Let's define some basic terminology used by Cassandra.…Like relational databases,…Cassandra is modeled using tables.…
Tables have keys that uniquely identify rows.…Tables are stored in keyspaces.…Keyspaces are a top-level data structure in Cassandra.…They act something like schemas in Oracle,…or databases in MySQL.…Partitions are subsets of a database…that are stored on a server.…A cluster with hundreds of servers…may contain a single database.…The way this is implemented is by dividing the database…into pieces known as partitions,…and storing those partitions on different servers.…
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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