For this project, you'll be working with one dataset: event_data. The directory of CSV files partitioned by date. Here are examples of filepaths to two files in the dataset:
event_data/2018-11-08-events.csv
event_data/2018-11-09-events.csv
To get started with the project, go to the workspace on the next page, where you'll find the project template (a Jupyter notebook file). You can work on your project and submit your work through this workspace.
The project template includes one Jupyter Notebook file, in which:
- you will process the
event_datafile_new.csvdataset to create a denormalized dataset - you will model the data tables keeping in mind the queries you need to run
- you have been provided queries that you will need to model your data tables for
- you will load the data into tables you create in Apache Cassandra and run your queries
Below are steps you can follow to complete each component of this project.
- Design tables to answer the queries outlined in the project template
- Write Apache Cassandra
CREATE KEYSPACEandSET KEYSPACEstatements - Develop your
CREATEstatement for each of the tables to address each question - Load the data with
INSERTstatement for each of the tables - Include
IF NOT EXISTSclauses in yourCREATEstatements to create tables only if the tables do not already exist. We recommend you also includeDROP TABLEstatement for each table, this way you can run drop and create tables whenever you want to reset your database and test your ETL pipeline - Test by running the proper select statements with the correct
WHEREclause
- Implement the logic in section Part I of the notebook template to iterate through each event file in
event_datato process and create a new CSV file in Python - Make necessary edits to Part II of the notebook template to include Apache Cassandra
CREATEandINSERTstatements to load processed records into relevant tables in your data model - Test by running
SELECTstatements after running the queries on your database