To this day, the Geodatindustry database is the world's most complete and accurate in the retail, commercial and industry area, with 25 years of experience and a qualified teams.

Geodatindustry Database is the perfect tool to lead your decision making, market analytics, strategy building, prospecting, advertizing compaigns, etc.

By purchasing this dataset, you gain access to more than 18,000 shopping malls all over the World, hosting millions of stores and welcoming millions of visitors each year.

Included Points of Interest in this dataset : -Shopping Malls and Centers -Outlets -Big Supermakets and Hypermarkets.

Information (if known) : shopping mall's name, physical address, number of shops, x,y coordinates, annual visitors counts (in millions), owner and managers, global area and GLA (in ranges), the website.

Global area and GLA Ranges : A = 0-2 500 m² B = 2 500-5 000 m² C = 5 000-10 000 m² D = 10 000-25 000 m²
E = 25 000-50 000 m² F = 50 000-75 000 m² G = 75 000-100 000 m² H = 100 000-1M m² I = 1M-10M m² J = 10M m² and +

Prices depend on the amount of Shopping Malls for each country. It goes from 59€ to 3990€ per country.

This dataset was created to simulate a market basket dataset, providing insights into customer purchasing behavior and store operations. The dataset facilitates market basket analysis, customer segmentation, and other retail analytics tasks. Here's more information about the context and inspiration behind this dataset:

Context:

Retail businesses, from supermarkets to convenience stores, are constantly seeking ways to better understand their customers and improve their operations. Market basket analysis, a technique used in retail analytics, explores customer purchase patterns to uncover associations between products, identify trends, and optimize pricing and promotions. Customer segmentation allows businesses to tailor their offerings to specific groups, enhancing the customer experience.

Inspiration:

The inspiration for this dataset comes from the need for accessible and customizable market basket datasets. While real-world retail data is sensitive and often restricted, synthetic datasets offer a safe and versatile alternative. Researchers, data scientists, and analysts can use this dataset to develop and test algorithms, models, and analytical tools.

Dataset Information:

The columns provide information about the transactions, customers, products, and purchasing behavior, making the dataset suitable for various analyses, including market basket analysis and customer segmentation. Here's a brief explanation of each column in the Dataset:

• Transaction_ID: A unique identifier for each transaction, represented as a 10-digit number. This column is used to uniquely identify each purchase.
• Date: The date and time when the transaction occurred. It records the timestamp of each purchase.
• Customer_Name: The name of the customer who made the purchase. It provides information about the customer's identity.
• Product: A list of products purchased in the transaction. It includes the names of the products bought.
• Total_Items: The total number of items purchased in the transaction. It represents the quantity of products bought.
• Total_Cost: The total cost of the purchase, in currency. It represents the financial value of the transaction.
• Payment_Method: The method used for payment in the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The city where the purchase took place. It indicates the location of the transaction.
• Store_Type: The type of store where the purchase was made, such as a supermarket, convenience store, department store, etc.
• Discount_Applied: A binary indicator (True/False) representing whether a discount was applied to the transaction.
• Customer_Category: A category representing the customer's background or age group.
• Season: The season in which the purchase occurred, such as spring, summer, fall, or winter.
• Promotion: The type of promotion applied to the transaction, such as "None," "BOGO (Buy One Get One)," or "Discount on Selected Items."

Use Cases:

• Market Basket Analysis: Discover associations between products and uncover buying patterns.
• Customer Segmentation: Group customers based on purchasing behavior.
• Pricing Optimization: Optimize pricing strategies and identify opportunities for discounts and promotions.
• Retail Analytics: Analyze store performance and customer trends.

Note: This dataset is entirely synthetic and was generated using the Python Faker library, which means it doesn't contain real customer data. It's designed for educational and research purposes.

The global database market, currently valued at $131.67 billion (2025), is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 14.21% from 2025 to 2033. This surge is driven by several key factors. The increasing adoption of cloud-based solutions offers scalability and cost-effectiveness, fueling market expansion. Furthermore, the burgeoning demand for real-time data analytics across diverse sectors, including BFSI (Banking, Financial Services, and Insurance), retail & e-commerce, and healthcare, is significantly boosting database market growth. The rise of big data and the need for robust data management solutions to handle massive datasets are other significant contributors. While on-premises deployments still hold a significant market share, particularly among large enterprises with stringent security requirements, the cloud segment is projected to witness the highest growth rate over the forecast period. The market is segmented by deployment (cloud, on-premises), enterprise size (SMEs, large enterprises), and end-user vertical (BFSI, retail & e-commerce, logistics & transportation, media & entertainment, healthcare, IT & telecom, others). Competition is intense, with established players like MongoDB, MarkLogic, Redis Labs, and Teradata alongside tech giants such as Microsoft, Amazon, and Google vying for market share through innovation and strategic partnerships. The competitive landscape is characterized by both established vendors and new entrants, leading to continuous innovation in database technologies. The market is witnessing a shift towards NoSQL databases, driven by the need to handle unstructured data and the increasing popularity of cloud-native applications. However, challenges such as data security concerns, the complexity of managing distributed database systems, and the need for skilled professionals to manage and maintain these systems pose potential restraints. The market's growth trajectory is largely positive, with continued expansion anticipated across all key segments and regions. North America and Europe are currently the dominant markets, but rapid growth is expected in Asia-Pacific, driven by increased digitalization and technological advancements in developing economies such as India and China. This comprehensive report provides an in-depth analysis of the global database market, encompassing historical data (2019-2024), current estimates (2025), and future forecasts (2025-2033). It examines key market segments, growth drivers, challenges, and emerging trends, offering valuable insights for businesses, investors, and stakeholders seeking to navigate this dynamic landscape. The study period covers the significant evolution of database technologies, from traditional relational databases to the rise of NoSQL and cloud-based solutions. The report utilizes a robust methodology and extensive primary and secondary research to provide accurate and actionable market intelligence. Keywords include: database market size, database market share, cloud database, NoSQL database, relational database, database management system (DBMS), database market trends, database market growth, database technology. Recent developments include: January 2024: Microsoft and Oracle recently announced the general availability of Oracle Database@Azure, allowing Azure customers to procure, deploy, and use Oracle Database@Azure with the Azure portal and APIs.November 2023: VMware, Inc. and Google Cloud announced an expanded partnership to deliver Google Cloud’s AlloyDB Omni database on VMware Cloud Foundation, starting with on-premises private clouds.. Key drivers for this market are: Increasing Penetration Of Trends Like Big Data And IoT, Increase In The Volume Of Data Generated And Shift Of Enterprise Operations. Potential restraints include: Increasing Penetration Of Trends Like Big Data And IoT, Increase In The Volume Of Data Generated And Shift Of Enterprise Operations. Notable trends are: Retail and E-commerce to Hold Significant Share.

This core point of interest dataset consists of 1M location information of retail stores in the US and Canada. The POI database includes electronic stores, supermarkets and groceries, specialty retailers, home improvement and convenience stores, and apparel and accessories shops.

United States Retail Sales: Health and Personal Care Stores (HP) data was reported at 29.574 USD bn in Oct 2018. This records an increase from the previous number of 27.613 USD bn for Sep 2018. United States Retail Sales: Health and Personal Care Stores (HP) data is updated monthly, averaging 17.302 USD bn from Jan 1992 (Median) to Oct 2018, with 322 observations. The data reached an all-time high of 31.178 USD bn in Dec 2017 and a record low of 7.071 USD bn in Sep 1992. United States Retail Sales: Health and Personal Care Stores (HP) data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.H001: Retail Sales: By NAIC System.

Retail Sales: Other Services data was reported at 60,205.069 VND bn in Mar 2025. This records an increase from the previous number of 57,704.476 VND bn for Feb 2025. Retail Sales: Other Services data is updated monthly, averaging 37,805.584 VND bn from Jan 2010 (Median) to Mar 2025, with 181 observations. The data reached an all-time high of 63,480.068 VND bn in Dec 2024 and a record low of 11,273.432 VND bn in Jul 2010. Retail Sales: Other Services data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.H001: Retail Sales.

Survey made with customers of a well-known brand retail stores.

A listing of all retail food stores which are licensed by the Department of Agriculture and Markets.

Relational Databases Software Market Outlook

The global relational databases software market size is projected to expand from an estimated $50 billion in 2023 to approximately $85 billion by 2032, growing at a compound annual growth rate (CAGR) of 6%. The primary drivers of this growth include the increasing reliance on data-driven decision-making processes, the surge in big data analytics, and the proliferation of cloud computing technologies. As organizations across various sectors accumulate vast amounts of data, the requirement for efficient data management and storage solutions becomes critical, further propelling the market's expansion.

One of the major growth factors driving the relational databases software market is the exponential increase in data generation from various sources, such as social media, IoT devices, and enterprise applications. With the advent of technologies like machine learning and artificial intelligence, the need to store, retrieve, and analyze massive datasets in real-time has become paramount. Relational databases software offers a structured way to manage data, providing quick access and robust querying capabilities, which are essential for leveraging data insights to drive business strategies.

Another significant growth factor is the widespread adoption of cloud computing. Cloud-based relational database solutions offer numerous advantages over traditional on-premises systems, such as scalability, flexibility, cost-effectiveness, and ease of maintenance. Many organizations are migrating their data management systems to the cloud to benefit from these advantages. Cloud vendors like Amazon Web Services, Microsoft Azure, and Google Cloud are continually enhancing their database offerings, adding advanced features to attract more customers, thereby fueling market growth.

The increasing trend toward digital transformation across various industries also contributes to the market's growth. As businesses strive to stay competitive in the digital age, they are investing heavily in modernizing their IT infrastructure, including their database management systems. Relational databases software enables organizations to handle complex transactions and support high-volume operations efficiently. This capability is particularly crucial for sectors such as banking and finance, healthcare, and retail, where data integrity and availability are critical for operations.

Regionally, North America currently holds the largest market share due to the early adoption of advanced technologies and the presence of major market players. Europe follows closely, with significant investments in digital transformation initiatives. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This growth can be attributed to the rapid technological advancements, increasing internet penetration, and the growing number of small and medium enterprises in countries like China and India. Governments in these regions are also promoting digital initiatives, further boosting market growth.

Deployment Mode Analysis

The relational databases software market is segmented by deployment mode into on-premises and cloud-based solutions. The on-premises segment, traditionally the dominant mode, involves deploying the database software within an organization's own IT infrastructure. This deployment mode offers stringent control over data security and compliance, making it a preferred choice for industries with critical data privacy concerns, such as banking and government sectors. Despite a gradual shift towards cloud solutions, on-premises deployments continue to be relevant due to these security advantages.

However, the cloud-based deployment mode is experiencing rapid growth and is expected to dominate the market by 2032. Cloud databases offer unparalleled scalability and flexibility, allowing organizations to scale their database capacity up or down based on demand. This elasticity is particularly beneficial for businesses with variable workloads, such as e-commerce platforms during peak shopping seasons. Additionally, cloud databases significantly reduce the need for heavy upfront capital expenditure in IT infrastructure, as they operate on a subscription or pay-as-you-go model, which is financially appealing to many enterprises.

Another factor contributing to the rise of cloud-based databases is the continuous innovation by leading cloud service providers. Companies like Amazon Web Services, Google Cloud Platform, and Microsoft Azure are integrating advanced features such as a

NoSQL Database Market size was valued at USD 7.43 Billion in 2024 and is projected to reach USD 60 Billion by 2031, growing at a CAGR of 30% during the forecast period from 2024 to 2031.

Global NoSQL Database Market Drivers

Big Data Management: The exponential growth of unstructured and semi-structured data necessitates flexible and scalable database solutions. Cloud Computing Adoption: The shift towards cloud-based applications and infrastructure is driving demand for NoSQL databases. Real-time Analytics: NoSQL databases excel at handling real-time data processing and analytics, making them suitable for applications like IoT and fraud detection.

Global NoSQL Database Market Restraints

Complexity and Management Challenges: NoSQL databases can be complex to manage and require specialized skills. Lack of Standardization: The absence of a standardized NoSQL query language can hinder data integration and migration.

The data set contains information on retail market spot check audit purchases of tuna in airtight containers. Data are available from May 2001 to present with new data appended annually. Information includes the date, location, product type, store information where random spot check purchases were made throughout the United States and Puerto Rico. Information on purchased product allows the man...

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.

Document Databases Market Outlook

The global document databases market size was valued at approximately USD 3.5 billion in 2023 and is projected to reach around USD 8.2 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 9.7% over the forecast period. This impressive growth can be attributed to the increasing demand for more flexible and scalable database solutions that can handle diverse data types and structures.

One of the primary growth factors for the document databases market is the rising adoption of NoSQL databases. Traditional relational databases often struggle with the unstructured data generated by modern applications, social media, and IoT devices. NoSQL databases, such as document databases, offer a more flexible and scalable solution to handle this data, which has led to their increased adoption across various industry verticals. Additionally, the growing popularity of microservices architecture in application development also drives the need for document databases, as they provide the necessary agility and performance.

Another significant growth factor is the increasing volume of data generated globally. With the exponential growth of data, organizations require robust and efficient database management systems to store, process, and analyze vast amounts of information. Document databases excel in managing large volumes of semi-structured and unstructured data, making them an ideal choice for enterprises looking to harness the power of big data analytics. Furthermore, advancements in cloud computing have made it easier for organizations to deploy and scale document databases, further driving their adoption.

The rise of artificial intelligence (AI) and machine learning (ML) technologies is also propelling the growth of the document databases market. AI and ML applications require databases that can handle complex data structures and provide quick access to large datasets for training and inference purposes. Document databases, with their schema-less design and ability to store diverse data types, are well-suited for these applications. As more organizations incorporate AI and ML into their operations, the demand for document databases is expected to grow significantly.

Regionally, North America holds the largest market share for document databases, driven by the presence of major technology companies and a high adoption rate of advanced database solutions. Europe is also a significant market, with growing investments in digital transformation initiatives. The Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, fueled by rapid technological advancements and increasing adoption of cloud-based solutions in countries like China, India, and Japan. Latin America and the Middle East & Africa are also experiencing growth, albeit at a slower pace, due to increasing digitalization efforts and the need for efficient data management solutions.

NoSQL Databases Analysis

NoSQL databases, a subset of document databases, have gained significant traction over the past decade. They are designed to handle unstructured and semi-structured data, making them highly versatile and suitable for a wide range of applications. Unlike traditional relational databases, NoSQL databases do not require a predefined schema, allowing for greater flexibility and scalability. This has led to their adoption in industries such as retail, e-commerce, and social media, where the volume and variety of data are constantly changing.

The key advantage of NoSQL databases is their ability to scale horizontally. Traditional relational databases often face challenges when scaling up, as they require more powerful hardware and complex configurations. In contrast, NoSQL databases can easily scale out by adding more servers to the database cluster. This makes them an ideal choice for applications that experience high traffic and require real-time data processing. Companies like Amazon, Facebook, and Google have already adopted NoSQL databases to manage their massive data workloads, setting a precedent for other organizations to follow.

Another driving factor for the adoption of NoSQL databases is their performance in handling large datasets. NoSQL databases are optimized for read and write operations, making them faster and more efficient than traditional relational databases. This is particularly important for applications that require real-time analytics and immediate data access. For instance, e-commerce platforms use NoSQL databases to provide personalized recommendations to users based on th

SNAP is the largest nutrition assistance program in the US. Understanding where SNAP dollars can be redeemed is thus a critical part of understanding food access environments.Data compiled from the USDA Food and Nutrition Service SNAP Retail locator downloaded from https://www.fns.usda.gov/snap/retailer-locatorXY coordinates for each retailer were re-verified using google maps due to geocoding inaccuracies in the USDA database. Retailers are categorized into five types of stores. “Big Box” are large commercial retail chains (superstores) that sell a range of products such as clothing, electronics, furniture, hardware, household supplies, pharmaceuticals and groceries. “Grocery” are retailers that primarily sell food and are distinguished from their counterparts by offering a wide diversity of perishable and nonperishable items including vegetables, fruit, meat, poultry, fish, bread and cereal, and dairy products. Most grocery stores are WIC approved. “Small Box” are retail store chains that sell a range standardized food products with minimal perishable options along with clothing, electronics, hardware, household supplies and pharmaceuticals. “Convenience” are retail locations that stock prepared food items, snacks, beverages, and only a very limited range of foods for home preparation. “Farmers Markets” sell agricultural produce including vegetables, fruits, meats, poultry, and dairy products usually in the form of direct farm to consumer sales. “Specialty” retailers carry limited food items focusing on a select product types such as international foods, butcheries, bakeries etc.All stores were called to identify whether they carry fresh produce and accept WIC payments. These categories are reflected in the data as well.

The Directorate General for Economic and Financial Affairs of the European Commission conducts regular harmonised surveys for different sectors of the economies in the European Union (EU) and in the applicant countries. They are addressed to representatives of the industry (manufacturing), services, retail trade and construction sectors, as well as to consumers. These surveys allow comparisons among different countries' business cycles and have become an indispensable tool for monitoring the evolution of the EU and the euro area economies, as well as monitoring developments in the applicant countries. Url of original source : https://ec.europa.eu/info/business-economy-euro/indicators-statistics/economic-databases/business-and-consumer-surveys/download-business-and-consumer-survey-data/time-series_en

United States Retail Sales Nowcast: sa: YoY data was reported at 4.089 % in 12 May 2025. This records an increase from the previous number of 3.963 % for 05 May 2025. United States Retail Sales Nowcast: sa: YoY data is updated weekly, averaging 3.924 % from Feb 2020 (Median) to 12 May 2025, with 274 observations. The data reached an all-time high of 44.471 % in 17 May 2021 and a record low of -13.873 % in 25 May 2020. United States Retail Sales Nowcast: sa: YoY data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s United States – Table US.CEIC.NC: CEIC Nowcast: Retail Sales.

Retail Sales: Goods: Culture and Education Accessories data was reported at 5,236.193 VND bn in Jan 2025. This records a decrease from the previous number of 5,547.409 VND bn for Dec 2024. Retail Sales: Goods: Culture and Education Accessories data is updated monthly, averaging 4,154.932 VND bn from Feb 2015 (Median) to Jan 2025, with 118 observations. The data reached an all-time high of 6,103.882 VND bn in Aug 2024 and a record low of 2,498.447 VND bn in Jul 2015. Retail Sales: Goods: Culture and Education Accessories data remains active status in CEIC and is reported by Ministry of Industry and Trade. The data is categorized under Global Database’s Vietnam – Table VN.H001: Retail Sales.

Food Intakes Converted to Retail Commodities Databases (FICRCD) provide data for foods consumed in the United States national dietary intake surveys at the retail commodity level. The survey foods are converted into 65 retail-level commodities. The commodities are grouped into eight major categories: Dairy Products; Fats and Oils; Fruits; Grains; Meat, Poultry, Fish and Eggs; Nuts; Caloric Sweeteners; and Vegetables, Dry Beans and Legumes. The Food Intakes Converted to Retail Commodities Databases were jointly developed by USDA's Agricultural Research Service (ARS) and Economic Research Service (ERS) for the following six surveys: Continuing Survey of Food Intakes by Individuals 1994-1996 and 1998.

Retail Sales: Goods: Vehicles & Parts, Accs. (excl Motor) data was reported at 20,848.288 VND bn in Jan 2025. This records a decrease from the previous number of 21,101.814 VND bn for Dec 2024. Retail Sales: Goods: Vehicles & Parts, Accs. (excl Motor) data is updated monthly, averaging 17,683.934 VND bn from Feb 2015 (Median) to Jan 2025, with 118 observations. The data reached an all-time high of 21,796.026 VND bn in Dec 2022 and a record low of 11,151.499 VND bn in Sep 2016. Retail Sales: Goods: Vehicles & Parts, Accs. (excl Motor) data remains active status in CEIC and is reported by Ministry of Industry and Trade. The data is categorized under Global Database’s Vietnam – Table VN.H001: Retail Sales.