Grocery Sales Database - Data Card

Overview

The Grocery Sales Database is a structured relational dataset designed for analyzing sales transactions, customer demographics, product details, employee records, and geographical information across multiple cities and countries. This dataset is ideal for data analysts, data scientists, and machine learning practitioners looking to explore sales trends, customer behaviors, and business insights.

Database Schema

The dataset consists of seven interconnected tables:

File Name	Description
categories.csv	Defines the categories of the products.
cities.csv	Contains city-level geographic data.
countries.csv	Stores country-related metadata.
customers.csv	Contains information about the customers who make purchases.
employees.csv	Stores details of employees handling sales transactions.
products.csv	Stores details about the products being sold.
sales.csv	Contains transactional data for each sale.

Table Descriptions

1. categories

Key	Column Name	Data Type	Description
PK	CategoryID	INT	Unique identifier for each product category.
	CategoryName	VARCHAR(45)	Name of the product category.

2. cities

Key	Column Name	Data Type	Description
PK	CityID	INT	Unique identifier for each city.
	CityName	VARCHAR(45)	Name of the city.
	Zipcode	DECIMAL(5,0)	Population of the city.
FK	CountryID	INT	Reference to the corresponding country.

3. countries

Key	Column Name	Data Type	Description
PK	CountryID	INT	Unique identifier for each country.
	CountryName	VARCHAR(45)	Name of the country.
	CountryCode	VARCHAR(2)	Two-letter country code.

4. customers

Key	Column Name	Data Type	Description
PK	CustomerID	INT	Unique identifier for each customer.
	FirstName	VARCHAR(45)	First name of the customer.
	MiddleInitial	VARCHAR(1)	Middle initial of the customer.
	LastName	VARCHAR(45)	Last name of the customer.
FK	cityID	INT	City of the customer.
	Address	VARCHAR(90)	Residential address of the customer.

5. employees

Key	Column Name	Data Type	Description
PK	EmployeeID	INT	Unique identifier for each employee.
	FirstName	VARCHAR(45)	First name of the employee.
	MiddleInitial	VARCHAR(1)	Middle initial of the employee.
	LastName	VARCHAR(45)	Last name of the employee.
	BirthDate	DATE	Date of birth of the employee.
	Gender	VARCHAR(10)	Gender of the employee.
FK	CityID	INT	unique identifier for city
	HireDate	DATE	Date when the employee was hired.

6. products

Key	Column Name	Data Type	Description
PK	ProductID	INT	Unique identifier for each product.
	ProductName	VARCHAR(45)	Name of the product.
	Price	DECIMAL(4,0)	Price per unit of the product.
	CategoryID	INT	unique category identifier
	Class ...

1) Data Introduction • The Grocery Sales Database is a retail dataset of relational tables of grocery store sales transactions, customer information, product details, employee records, geographic information, and more across cities and countries.

2) Data Utilization (1) Grocery Sales Database has characteristics that: • The data consists of seven tables, including product categories, city/country information, customer/employee/product details, and sales details, each of which is interconnected by a unique ID. • Sales data are linked to products, customers, employees, and regions, enabling a variety of business analyses, including monthly sales, popular products, customer behavior, and regional performance. (2) Grocery Sales Database can be used to: • Analysis of sales trends and popular products: It can be used to identify trends and derive best-selling products by analyzing sales by monthly and category and sales by product. • Customer Segmentation and Marketing Strategy: Define customer groups based on customer frequency of purchases, total expenditure, and regional information and apply them to developing customized marketing and promotion strategies.

Comprehensive database of event ticket sales data including pricing, zones, quantities, and timestamps for millions of historical ticket transactions across major venues and events. Ideal for analytics, market research, and AI/ML training.

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This Database created on SQL was a Sales project based on the famous database AdventureWorks2019 from Microsoft.

I decided to create a code and share this project on SQL to let people learn from it.

This database is not as bigger as AdventureWorks2019, it is a database made of 10 different tables linked to each other. After the step of creation of the tables, I did a step only to add the data into the table. I added the data from the AdventureWorks2019 database.

Music Sales by Format and Year

Sales data for music industry by format and year

By Charlie Hutcheson [source]

About this dataset

The Music Industry Sales by Format and Year dataset provides comprehensive information on the sales data for different music formats over a span of 40 years. The dataset aims to analyze and visualize the trends in music industry sales, specifically focusing on various formats and metrics used to measure these sales.

The dataset includes several key columns to facilitate data analysis, including Format which represents the different formats of music sales such as physical (CDs, vinyl) or digital (downloads, streaming). Additionally, the column Metric indicates the specific measure used to quantify the sales data, such as units sold or revenue generated. The column Year specifies the particular year in which the sales data was recorded.

To provide a more comprehensive understanding of each combination of format, metric, and year, additional columns are included. The Number of Records column denotes the total number of entries or records available for each unique combination. This information helps assess sample size reliability for further analysis. Moreover, there is an Actual Value column that presents precise numerical values representing the actual recorded sales figure corresponding to each format-metric-year combination.

This dataset is obtained from credible sources including RIAA's U.S Sales Database and was originally presented through a visualization by Visual Capitalist. It offers insights into historical trends in music industry sales patterns across different formats over four decades.

In order to enhance this dataset visual representation and further explore its potential insights accurately, it would be necessary to perform an exploratory analysis assessing: seasonal patterns within each format; changes in market share across multiple years; growth rates comparison between physical and digital formats; etc. These analyses can help identify emerging trends in consumer preferences along with underlying factors driving shifts in market dynamics. Additionally,the presentation media (such as charts or graphs) could benefit from improvements such as clearer labeling, more detailed annotations,captions that allow viewers to easily interpret visualized information,and arrangement providing a logical flow conducive to understanding the data

How to use the dataset

Dataset Overview

The dataset consists of the following columns:

• Format: The format of the music sales, such as physical (CDs, vinyl) or digital (downloads, streaming).
• Metric: The metric used to measure the sales, such as units sold or revenue generated.
• Year: The year in which the sales data was recorded.
• Number of Records: The number of records or entries for each combination of format, metric and year.
• Value (Actual): The actual value of the sales for each combination of format, metric and year.

Key Considerations

Before diving into analyzing this dataset, here are some key points to consider:

• Categorical Variables: Both Format and Metric columns contain categorical variables that represent different aspects related to music industry sales.
• Numeric Variables: Year, Number of Records, and Value (Actual) are numeric variables providing chronological information about record counts and actual sale values.

Interpreting Insights

To make meaningful interpretations using this data set:

Analyzing Different Formats:

• You can compare different formats' popularity over time based on units sold/revenue generated.
• Explore how digital formats have influenced physical format sales over time.
• Understand which formats have experienced growth or decline in specific years.

Evaluating Different Metrics:

• Analyze revenue trends compared to unit count trends for different formats each year.
• Identify metrics showing exceptional growth/decline compared across differing years/formats.

Understanding Sales Trends:

• Examine the relationship between the number of records and actual sales value each year.
• Identify periods where significant changes in music industry sales occurred.
• Observe trends and fluctuations based on different formats/metrics.

Visualizing Data

To enhance your analysis, create visualizations using this dataset:

• Time Series Analysis: Create line plots to visualize the trend in music sales for different formats over time.
• Comparative Analysis: Generate bar charts or grouped bar plots...

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United States Existing Home Sales: US data was reported at 420,000.000 Unit in Sep 2018. This records a decrease from the previous number of 539,000.000 Unit for Aug 2018. United States Existing Home Sales: US data is updated monthly, averaging 436,000.000 Unit from Jan 1999 (Median) to Sep 2018, with 237 observations. The data reached an all-time high of 753,000.000 Unit in Jun 2005 and a record low of 218,000.000 Unit in Jan 2009. United States Existing Home Sales: US data remains active status in CEIC and is reported by National Association of Realtors. The data is categorized under Global Database’s USA – Table US.EB005: Existing Home Sales.

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NYC Real Estate Sales, January 2016 – September 2025 (DuckDB Format) This dataset contains records of real estate transactions in New York City from January 2016 through September 2025, stored as a single DuckDB database file. It includes details such as sale date, price, property address, borough, and other relevant attributes. The data is suitable for property market analysis, data science projects, and urban studies research. Data for each borough is in a database table named as such, eg Manhattan data is in the manhattan table, Staten Island data in the staten_island table, etc.

By 2025, the managed database services market will likely hit USD 445,020.1 million and grow to USD 1,497,335 million by 2035, with a CAGR of 12.9%. The rise of using multi-cloud and mixed cloud plans, rising AI use for smart database upkeep, and more people using Database-as-a-Service are guiding the future of the industry. Also, more worry about keeping data safe and following rules is driving market growth.

Metric	Value
Market Size (2025E)	USD 445,020.1 Million
Market Value (2035F)	USD 1,497,335 Million
CAGR (2025 to 2035)	12.9%

Country-wise Insights

Country	CAGR (2025 to 2035)
USA	13.1%

Country	CAGR (2025 to 2035)
UK	12.7%

Region	CAGR (2025 to 2035)
European Union (EU)	12.9%

Country	CAGR (2025 to 2035)
Japan	13.0%

Country	CAGR (2025 to 2035)
South Korea	13.2%

Managed Database Services Market - Segmentation Outlook

Service	Market Share (2025)
Database Administration	38.0%

Application	Market Share (2025)
Customer Relationship Management (CRM)	46.0%

Competitive Outlook

Company Name	Estimated Market Share (%)
Amazon Web Services (AWS)	18-22%
Microsoft Corporation (Azure)	14-18%
Google Cloud Platform (GCP)	12-16%
Oracle Corporation	10-14%
IBM Corporation	6-10%
Other Companies (combined)	30-40%

The Northwind database is a sample database that was originally created by Microsoft and used as the basis for their tutorials in a variety of database products for decades. The Northwind database contains the sales data for a fictitious company called “Northwind Traders,” which imports and exports specialty foods from around the world. The Northwind database is an excellent tutorial schema for a small-business ERP, with customers, orders, inventory, purchasing, suppliers, shipping, employees, and single-entry accounting. The Northwind database has since been ported to a variety of non-Microsoft databases, including PostgreSQL.

The Northwind dataset includes sample data for the following.

• Suppliers: Suppliers and vendors of Northwind
• Customers: Customers who buy products from Northwind
• Employees: Employee details of Northwind traders
• Products: Product information
• Shippers: The details of the shippers who ship the products from the traders to the end-customers
• Orders and Order_Details: Sales Order transactions taking place between the customers & the company

Context

Completely random product sales data to help explore data analysis

Content

This is a sqlite3 database containing a Sales star schema.
Fact table: sales
Dimension tables: regions, time, products

Originally the time table had virtual (generated) columns, but since sqlite does not support such columns, the data is inserted static.

Acknowledgements

Data originally generated by Mockaroo and saved in a mysql database.

The table Sale database 2019-2022 is part of the dataset Maryland Property Assessment - Summary, available at https://redivis.com/datasets/sy4g-4h33mdm5n. It contains 3968505 rows across 99 variables.