- [Instructor] Now it's time to take a look at…how we can prepare data for use with document databases.…It's quite common to have to load data…from comma separated value files, or CSV files for short.…Tab separated value files, also known as TSV files,…are also frequently used for data transfers.…One way to work with these files is to…use a scripting language like Python.…Python has two useful libraries…for working with text and JSON files.…These are called the CSV and JSON libraries or packages.…
These libraries have functions…for reading and writing to these formats.…The csv.DictReader function in the CSV library…is especially useful for reading lines…from a tabular file into a Python dictionary.…These data structures are then easily mapped to JSON.…We can use the dump function in the JSON library…to write a list of dictionaries to a JSON file…which can then be loaded to a document database.…Now we don't have to write custom scripts…if we don't want to.…Document databases like MongoDB…have utility programs for loading data.…
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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