This is a relational database schema for a sales and order management system, designed to track customers, employees, products, orders, and payments. Below is a detailed breakdown of each table and their relationships:

1. productlines Table (Product Categories)

• Represents different product categories.
• Primary Key: productLine
• Attributes:
  • textDescription: A short description of the product line.
  • htmlDescription: A detailed HTML-based description.
  • image: Associated image (if applicable).
• Relationships:
  • One-to-Many with products: Each product belongs to one productLine.

2. products Table (Product Information)

• Stores details of individual products.
• Primary Key: productCode
• Attributes:
  • productName: Name of the product.
  • productLine: Foreign key linking to productlines.
  • productScale, productVendor, productDescription: Additional product details.
  • quantityInStock: Number of units available.
  • buyPrice: Cost price per unit.
  • MSRP: Manufacturer's Suggested Retail Price.
• Relationships:
  • Many-to-One with productlines (each product belongs to one category).
  • One-to-Many with orderdetails (a product can be part of many orders).

3. orderdetails Table (Line Items in an Order)

• Stores details of each product within an order.
• Composite Primary Key: (orderNumber, productCode)
• Attributes:
  • quantityOrdered: Number of units in the order.
  • priceEach: Price per unit.
  • orderLineNumber: The sequence number in the order.
• Relationships:
  • Many-to-One with orders (each order has multiple products).
  • Many-to-One with products (each product can appear in multiple orders).

4. orders Table (Customer Orders)

• Represents customer orders.
• Primary Key: orderNumber
• Attributes:
  • orderDate: Date when the order was placed.
  • requiredDate: Expected delivery date.
  • shippedDate: Actual shipping date (can be NULL if not shipped).
  • status: Order status (e.g., "Shipped", "In Process", "Cancelled").
  • comments: Additional remarks about the order.
  • customerNumber: Foreign key linking to customers.
• Relationships:
  • One-to-Many with orderdetails (an order contains multiple products).
  • Many-to-One with customers (each order is placed by one customer).

5. customers Table (Customer Details)

• Stores customer information.
• Primary Key: customerNumber
• Attributes:
  • customerName: Name of the customer.
  • contactLastName, contactFirstName: Contact person.
  • phone: Contact number.
  • addressLine1, addressLine2, city, state, postalCode, country: Address details.
  • salesRepEmployeeNumber: Foreign key linking to employees, representing the sales representative.
  • creditLimit: Maximum credit limit assigned to the customer.
• Relationships:
  • One-to-Many with orders (a customer can place multiple orders).
  • One-to-Many with payments (a customer can make multiple payments).
  • Many-to-One with employees (each customer has a sales representative).

6. payments Table (Customer Payments)

• Stores payment transactions.
• Composite Primary Key: (customerNumber, checkNumber)
• Attributes:
  • paymentDate: Date of payment.
  • amount: Payment amount.
• Relationships:
  • Many-to-One with customers (each payment is linked to a customer).

7. employees Table (Employee Information)

• Stores details of employees, including reporting hierarchy.
• Primary Key: employeeNumber
• Attributes:
  • lastName, firstName: Employee's name.
  • extension, email: Contact details.
  • officeCode: Foreign key linking to offices, representing the employee's office.
  • reportsTo: References another employeeNumber, establishing a hierarchy.
  • jobTitle: Employee’s role (e.g., "Sales Rep", "Manager").
• Relationships:
  • Many-to-One with offices (each employee works in one office).
  • One-to-Many with employees (self-referential, representing reporting structure).
  • One-to-Many with customers (each employee manages multiple customers).

8. offices Table (Office Locations)

• Represents company office locations.
• Primary Key: officeCode
• Attributes:
  • city, state, country: Location details.
  • phone: Office contact number.
  • addressLine1, addressLine2, postalCode, territory: Address details.
• Relationships:
  • One-to-Many with employees (each office has multiple employees).

Conclusion

This schema provides a well-structured design for managing a sales and order system, covering: ✅ Product inventory
✅ Order and payment tracking
✅ Customer and employee management
✅ Office locations and hierarchical reporting

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Explore our global B2B contact and company database, providing essential data fields including Name, Website, Contact First Name, Contact Last Name, Job Title, Email Address, Phone Number, Revenue Size, Employee Size, Location, City, State, Country, Zip Code, and additional customizable data fields upon request. Access a comprehensive repository tailored to meet your specific business needs, ensuring you have access to accurate and detailed information for effective networking and targeted outreach.

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.

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.

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.

A searchable electronic database of all real property upon which a deed restriction was imposed by the Department of Citywide Administrative Services, pursuant to Local Law 176 of 2016. Current data: 1966 - present and active requests.

Disclaimer: Data, descriptions and other information posted within this dataset, published and/or distributed by DCAS, or statements made by officials, agents and employees of the City concerning information contained within this dataset are for informational purposes only and should be independently verified by anyone accessing this data. The City does not warranty the completeness, accuracy, content or fitness for any particular purpose or use of the information provided herein nor are any such warranties to be implied or inferred with respect to the data furnished herein. The existence of this dataset shall not be construed to create a private right of action to enforce its provisions. The existence of any inaccuracies or deficiencies in the dataset shall not result in liability to the City. No such data, description or other information, or omissions thereof shall be deemed to be a representation or warranty and the viewer acknowledges not having relied on any representation or warranty or omissions thereof, concerning this data.

Success.ai’s Ecommerce Merchant Data and B2B Contact Data for Global E-commerce Professionals provides a comprehensive and highly accurate database from over 170 million verified profiles. Specifically tailored for the e-commerce sector, this dataset features work emails, direct phone numbers, and enriched professional profiles to connect businesses with the leaders and decision-makers shaping the global e-commerce landscape. Continuously updated with advanced AI validation, this resource is ideal for enhancing marketing campaigns, sales initiatives, recruitment efforts, and market research.

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Autos include all passenger cars, including station wagons. The U.S. Bureau of Economic Analysis releases auto and truck sales data, which are used in the preparation of estimates of personal consumption expenditures.

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The State Contract and Procurement Registration System (SCPRS) was established in 2003, as a centralized database of information on State contracts and purchases over $5000. eSCPRS represents the data captured in the State's eProcurement (eP) system, Bidsync, as of March 16, 2009. The data provided is an extract from that system for fiscal years 2012-2013, 2013-2014, and 2014-2015

Data Limitations:
Some purchase orders have multiple UNSPSC numbers, however only first was used to identify the purchase order. Multiple UNSPSC numbers were included to provide additional data for a DGS special event however this affects the formatting of the file. The source system Bidsync is being deprecated and these issues will be resolved in the future as state systems transition to Fi$cal.

Data Collection Methodology:

The data collection process starts with a data file from eSCPRS that is scrubbed and standardized prior to being uploaded into a SQL Server database. There are four primary tables. The Supplier, Department and United Nations Standard Products and Services Code (UNSPSC) tables are reference tables. The Supplier and Department tables are updated and mapped to the appropriate numbering schema and naming conventions. The UNSPSC table is used to categorize line item information and requires no further manipulation. The Purchase Order table contains raw data that requires conversion to the correct data format and mapping to the corresponding data fields. A stacking method is applied to the table to eliminate blanks where needed. Extraneous characters are removed from fields. The four tables are joined together and queries are executed to update the final Purchase Order Dataset table. Once the scrubbing and standardization process is complete the data is then uploaded into the SQL Server database.

Secondary/Related Resources:

• State Contract Manual (SCM) vol. 2 http://www.dgs.ca.gov/pd/Resources/publications/SCM2.aspx
• State Contract Manual (SCM) vol. 3 http://www.dgs.ca.gov/pd/Resources/publications/SCM3.aspx
• Buying Green http://www.dgs.ca.gov/buyinggreen/Home.aspx
• United Nations Standard Products and Services Code, http://www.unspsc.org/

Global Relational Database Management System comes with the extensive industry analysis of development components, patterns, flows and sizes. The report also calculates present and past market values to forecast potential market management through the forecast period between 2024 - 2032. The report may be the best of what is a geographic area which expands the competitive landscape and industry perspective of the market.

Russia Ford Motor Company: Sales Profit (Loss) data was reported at 198,000.000 RUB th in 2016. This records an increase from the previous number of 114,000.000 RUB th for 2015. Russia Ford Motor Company: Sales Profit (Loss) data is updated yearly, averaging 1,527,500.000 RUB th from Dec 2007 (Median) to 2016, with 10 observations. The data reached an all-time high of 6,156,000.000 RUB th in 2008 and a record low of -1,813,000.000 RUB th in 2009. Russia Ford Motor Company: Sales Profit (Loss) data remains active status in CEIC and is reported by Company Financial Statement. The data is categorized under Russia Premium Database’s Automobile Sector – Table RU.RAH010: Company Financial Data: Automobile Sales: Ford Motor Company.

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 world has digitized rapidly, especially since the advent of the internet. Banks, financial institutions, hospitals, insurance companies, and e-commerce platforms rely heavily on databases to manage customer accounts, transactions, and sensitive financial data. With the advancements in the technology sector, the database monitoring software market is poised to be valued at a staggering US$ 2.40 billion in 2024.

Attributes	Details
Market Value for 2024	US$ 2.40 billion
Projected Market Value for 2034	US$ 10.10 billion
Value-based CAGR of the Market for 2024 to 2034	15.20%

Category-wise Insights

Attributes	Details
Component	Software
Market Share (2024)	63%

Attributes	Details
End User	BFSI
Market Share (2024)	29.30%

Country-wise Insights

Countries	CAGR (2024 to 2034)
South Korea	18.00%
Japan	17.20%
The United Kingdom	16.70%
China	16.20%
The United States	15.60%

United States Existing Home Sales: Condos data was reported at 51,000.000 Unit in Oct 2018. This records an increase from the previous number of 48,000.000 Unit for Sep 2018. United States Existing Home Sales: Condos data is updated monthly, averaging 47,000.000 Unit from Jan 2007 (Median) to Oct 2018, with 142 observations. The data reached an all-time high of 66,000.000 Unit in Jul 2007 and a record low of 18,000.000 Unit in Jan 2009. United States Existing Home Sales: Condos data remains active status in CEIC and is reported by National Association of Realtors. The data is categorized under Global Database’s United States – Table US.EB005: Existing Home Sales.

Japan Information Service Sales: Database Services data was reported at 10,619.000 JPY mn in Sep 2018. This records an increase from the previous number of 9,764.000 JPY mn for Aug 2018. Japan Information Service Sales: Database Services data is updated monthly, averaging 11,386.000 JPY mn from Feb 2007 (Median) to Sep 2018, with 140 observations. The data reached an all-time high of 16,133.000 JPY mn in Mar 2008 and a record low of 9,475.000 JPY mn in Jan 2016. Japan Information Service Sales: Database Services data remains active status in CEIC and is reported by Ministry of Economy, Trade and Industry. The data is categorized under Global Database’s Japan – Table JP.H016: Information Services Sales.