Join Dan Sullivan for an in-depth discussion in this video Prepare data for Cassandra, part of Advanced NoSQL for Data Science.
- Now it's time to load some data into Cassandra.…Now for that, we're going to need a data set.…I'd like to download a data set from the Kaggle website.…I've navigated to the Kaggle website.…I'm going to select Download.…Now you may be prompted to log in.…I've already logged in using my Google identity.…A zip file with US Stocks Fundamentals has been downloaded.…I'm going to show that in Finder.…Okay, as you can see in our Download folder,…we have US-Stock-Fundamentals.zip.…I'm just going to drag that to my NoSQLDS directory…so I can work on it there.…I will open the zip file,…and let's take a look at the files.…
We have three files.…We have Companies in a comma separated value format,…Companies in JSON, and an Indicators_by_company.csv.…I'll go into a little more detail…about the contents of these three in a moment.…First I'm just going to copy these,…navigate back up to here, paste these items,…and then I'm just going to clean up a little bit.…Next what I'd like to do is start a Jupyter Notebook.…I'm using a Terminal window, and I'm in my home directory.…
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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