Challenge 1: Preparing Your Data Feast in BigQuery

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Note This challenge focuses on establishing a solid data foundation for your AI applications. Think about how a robust, unified dataset can empower smarter insights and automations.

Introduction

Before AI can work its magic, it needs data. In the enterprise, this data often resides in disparate systems like SAP, CRM platforms (like Salesforce), and even public datasets. This challenge is about bringing all these “ingredients” together into a centralized, highly scalable data warehouse: Google BigQuery. You’ll learn to ingest structured data from various sources and create analytical views that unlock hidden value.

Description

Your goal in this challenge is to set up a BigQuery dataset that will serve as the single source of truth for your AI agents. You are responsible for:

Creating a new BigQuery dataset for your lab.
Load essential SAP Material Master data from Cloud Storage into your BigQuery dataset.
Load Salesforce Customer Case data from Cloud Storage into your BigQuery dataset..
Create a view over the FDA Food Enforcement Data to filter out only data related to ice cream

Important Pay close attention to the data formats and schemas required for loading data into BigQuery. Ensure your commands correctly specify source formats, skip headers, and define column names and types.

Success Criteria

Validate that a BigQuery dataset named icecream_lab exists in your GCP project.
Verify that the MaterialMasterData table contains SAP material information.
Confirm that the CustomerCases table contains Salesforce customer feedback.
Demonstrate that the fda_ice_cream_enforcements view exists and correctly filters public FDA data for “ice cream” related entries.
Show that all loaded tables and the view are accessible and contain data previews in the BigQuery Console.

Tips

The FDA FOOD enforcement data can be found in bigquery-public-data.fda_food.food_enforcement table. Try querying it to explore it

Learning Resources

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