Technical requirements

You can find all the code in this chapter in chapter1 of the GitHub repository for this book:https://github.com/PacktPublishing/Elasticsearch-Query-Language-the-Definitive-GuideThe dataset that we will use in this chapter for our examples is a synthetic dataset. We chose to build a retro arcade dataset that contains fictitious purchases of video games across the world in the 80s and 90s.We have created a notebook to generate this dataset. The script is called chapter1.ipynb and can be found in our GitHub repository: https://github.com/PacktPublishing/Elasticsearch-Query-Language-the-Definitive-Guide/blob/main/chapter1/chapter1.ipynb. You can customize it at your convenience.The script is fairly straightforward and builds a CSV file where each document has the description of a given video game purchase order:

• purchase_id
• game_title
• genre
• platform
• release_year
• developer_name
• publisher_name
• game_description
• purchase_date
• price_at_purchase
• user_rating
• review_date
• game_keywords
• legacy_score
• country_code
• continent
• purchase_device
• payment_method
• discount_applied

The fields are self-explanatory and define what a purchase looked like in these years. With that in mind, here are the steps to import the data once you run the script and obtain a CSV file:

1. Connect to Kibana and navigate to Machine Learning | Data Visualizer | File. You will see the following screen:

   

2. Click on the Select or drag and drop a file button and upload your file; you should see a summary similar to this:

   

3. The file upload recognizes the CSV format and is ready to upload the content of your file to an index. You can overwrite the settings, but since we are all working out of the same script output, I recommend not changing anything.

4. After clicking on Import, set your index name. I chose retro_arcade_purchases_dataset:

   

5. The file upload will result in the following screen:

You now have 500,000 records you can work with!A quick look in the Discover tab will show you all the data imported: