Big Data and AI

Scenario You are a BI analyst for a grocery store chain monitoring dietary trends that influence in‑store purchases. Your company wants you to analyze which types of Hass avocados are purchased most often. Avocados are categorized into four sizes—small, medium, large, and extra large—and each sale includes the average price, total volume, and date. Using this dataset, you will create a historical table to demonstrate how partitions and clusters work in BigQuery. Your goal is to answer the question: What is the distribution of avocado sales from 2015 to 2021? Create a Baseline Table (No Partition, No Cluster) Start by creating a new table without partitions or clustering. This baseline will help you compare performance later. Name the table avocados . After running the SQL, your table should look like this: Create a Partitioned Table Next, create a table partitioned by an integer range representing the years 2015–2022. Name this table avocados_partitio...

Introduction In today's data-driven business landscape, having access to accurate and comprehensive insights is essential for making informed decisions. As a Business Intelligence (BI) professional, you play a key role in gathering and organizing data from multiple stakeholders across different teams. BigQuery , a powerful cloud data warehouse, enables fast querying, filtering, aggregation, and complex operations on large datasets. To better understand how BI professionals use modern data tools, you can also explore how data warehouses support BI workflows . In this post, we explore how Aviva, a BI professional, uses BigQuery to merge data from various stakeholders to answer important business questions for a fictional coffee shop chain. The Problem: Identifying Popular and Profitable Seasonal Menu Items Aviva is tasked with helping leadership identify which seasonal menu items are both popular and profitable. These insights will guide pricing decisions, promotio...