This is the sample database from sqlservertutorial.net. This is a great dataset for learning SQL and practicing querying relational databases.

Database Diagram:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4146319%2Fc5838eb006bab3938ad94de02f58c6c1%2FSQL-Server-Sample-Database.png?generation=1692609884383007&alt=media" alt="">

Terms of Use

The sample database is copyrighted and cannot be used for commercial purposes. For example, it cannot be used for the following but is not limited to the purposes: - Selling - Including in paid courses

This dataset contains a list of sales and movement data by item and department appended monthly. Update Frequency : Monthly

Dataset

This dataset was created by sirishav0919

Released under Database: Open Database, Contents: Database Contents

Contents

MealMe provides comprehensive grocery and retail SKU-level product data, including real-time pricing, from the top 100 retailers in the USA and Canada. Our proprietary technology ensures accurate and up-to-date insights, empowering businesses to excel in competitive intelligence, pricing strategies, and market analysis.

Retailers Covered: MealMe’s database includes detailed SKU-level data and pricing from leading grocery and retail chains such as Walmart, Target, Costco, Kroger, Safeway, Publix, Whole Foods, Aldi, ShopRite, BJ’s Wholesale Club, Sprouts Farmers Market, Albertsons, Ralphs, Pavilions, Gelson’s, Vons, Shaw’s, Metro, and many more. Our coverage spans the most influential retailers across North America, ensuring businesses have the insights needed to stay competitive in dynamic markets.

Key Features: SKU-Level Granularity: Access detailed product-level data, including product descriptions, categories, brands, and variations. Real-Time Pricing: Monitor current pricing trends across major retailers for comprehensive market comparisons. Regional Insights: Analyze geographic price variations and inventory availability to identify trends and opportunities. Customizable Solutions: Tailored data delivery options to meet the specific needs of your business or industry. Use Cases: Competitive Intelligence: Gain visibility into pricing, product availability, and assortment strategies of top retailers like Walmart, Costco, and Target. Pricing Optimization: Use real-time data to create dynamic pricing models that respond to market conditions. Market Research: Identify trends, gaps, and consumer preferences by analyzing SKU-level data across leading retailers. Inventory Management: Streamline operations with accurate, real-time inventory availability. Retail Execution: Ensure on-shelf product availability and compliance with merchandising strategies. Industries Benefiting from Our Data CPG (Consumer Packaged Goods): Optimize product positioning, pricing, and distribution strategies. E-commerce Platforms: Enhance online catalogs with precise pricing and inventory information. Market Research Firms: Conduct detailed analyses to uncover industry trends and opportunities. Retailers: Benchmark against competitors like Kroger and Aldi to refine assortments and pricing. AI & Analytics Companies: Fuel predictive models and business intelligence with reliable SKU-level data. Data Delivery and Integration MealMe offers flexible integration options, including APIs and custom data exports, for seamless access to real-time data. Whether you need large-scale analysis or continuous updates, our solutions scale with your business needs.

Why Choose MealMe? Comprehensive Coverage: Data from the top 100 grocery and retail chains in North America, including Walmart, Target, and Costco. Real-Time Accuracy: Up-to-date pricing and product information ensures competitive edge. Customizable Insights: Tailored datasets align with your specific business objectives. Proven Expertise: Trusted by diverse industries for delivering actionable insights. MealMe empowers businesses to unlock their full potential with real-time, high-quality grocery and retail data. For more information or to schedule a demo, contact us today!

https://raw.githubusercontent.com/Masterx-AI/Project_Retail_Analysis_with_Walmart/main/Wallmart1.jpg" alt="">

Description:

One of the leading retail stores in the US, Walmart, would like to predict the sales and demand accurately. There are certain events and holidays which impact sales on each day. There are sales data available for 45 stores of Walmart. The business is facing a challenge due to unforeseen demands and runs out of stock some times, due to the inappropriate machine learning algorithm. An ideal ML algorithm will predict demand accurately and ingest factors like economic conditions including CPI, Unemployment Index, etc.

Walmart runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of all, which are the Super Bowl, Labour Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the challenge presented by this competition is modeling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data. Historical sales data for 45 Walmart stores located in different regions are available.

Acknowledgements

The dataset is taken from Kaggle.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t single & multiple features.
• Also evaluate the models & compare their respective scores like R2, RMSE, etc.

Success.ai delivers unparalleled access to Retail Store Data for Asia’s retail and e-commerce sectors, encompassing subcategories such as ecommerce data, ecommerce merchant data, ecommerce market data, and company data. Whether you’re targeting emerging markets or established players, our solutions provide the tools to connect with decision-makers, analyze market trends, and drive strategic growth. With continuously updated datasets and AI-validated accuracy, Success.ai ensures your data is always relevant and reliable.

Key Features of Success.ai's Retail Store Data for Retail & E-commerce in Asia:

Extensive Business Profiles: Access detailed profiles for 70M+ companies across Asia’s retail and e-commerce sectors. Profiles include firmographic data, revenue insights, employee counts, and operational scope.

Ecommerce Data: Gain insights into online marketplaces, customer demographics, and digital transaction patterns to refine your strategies.

Ecommerce Merchant Data: Understand vendor performance, supply chain metrics, and operational details to optimize partnerships.

Ecommerce Market Data: Analyze purchasing trends, regional preferences, and market demands to identify growth opportunities.

Contact Data for Decision-Makers: Reach key stakeholders, such as CEOs, marketing executives, and procurement managers. Verified contact details include work emails, phone numbers, and business addresses.

Real-Time Accuracy: AI-powered validation ensures a 99% accuracy rate, keeping your outreach efforts efficient and impactful.

Compliance and Ethics: All data is ethically sourced and fully compliant with GDPR and other regional data protection regulations.

Why Choose Success.ai for Retail Store Data?

Best Price Guarantee: We deliver industry-leading value with the most competitive pricing for comprehensive retail store data.

Customizable Solutions: Tailor your data to meet specific needs, such as targeting particular regions, industries, or company sizes.

Scalable Access: Our data solutions are built to grow with your business, supporting small startups to large-scale enterprises.

Seamless Integration: Effortlessly incorporate our data into your existing CRM, marketing, or analytics platforms.

Comprehensive Use Cases for Retail Store Data:

1. Market Entry and Expansion:

Identify potential partners, distributors, and clients to expand your footprint in Asia’s dynamic retail and e-commerce markets. Use detailed profiles to assess market opportunities and risks.

1. Personalized Marketing Campaigns:

Leverage ecommerce data and consumer insights to craft highly targeted campaigns. Connect directly with decision-makers for precise and effective communication.

1. Competitive Benchmarking:

Analyze competitors’ operations, market positioning, and consumer strategies to refine your business plans and gain a competitive edge.

1. Supplier and Vendor Selection:

Evaluate potential suppliers or vendors using ecommerce merchant data, including financial health, operational details, and contact data.

1. Customer Engagement and Retention:

Enhance customer loyalty programs and retention strategies by leveraging ecommerce market data and purchasing trends.

APIs to Amplify Your Results:

Enrichment API: Keep your CRM and analytics platforms up-to-date with real-time data enrichment, ensuring accurate and actionable company profiles.

Lead Generation API: Maximize your outreach with verified contact data for retail and e-commerce decision-makers. Ideal for driving targeted marketing and sales efforts.

Tailored Solutions for Industry Professionals:

Retailers: Expand your supply chain, identify new markets, and connect with key partners in the e-commerce ecosystem.

E-commerce Platforms: Optimize your vendor and partner selection with verified profiles and operational insights.

Marketing Agencies: Deliver highly personalized campaigns by leveraging detailed consumer data and decision-maker contacts.

Consultants: Provide data-driven recommendations to clients with access to comprehensive company data and market trends.

What Sets Success.ai Apart?

70M+ Business Profiles: Access an extensive and detailed database of companies across Asia’s retail and e-commerce sectors.

Global Compliance: All data is sourced ethically and adheres to international data privacy standards, including GDPR.

Real-Time Updates: Ensure your data remains accurate and relevant with our continuously updated datasets.

Dedicated Support: Our team of experts is available to help you maximize the value of our data solutions.

Empower Your Business with Success.ai:

Success.ai’s Retail Store Data for the retail and e-commerce sectors in Asia provides the insights and connections needed to thrive in this competitive market. Whether you’re entering a new region, launching a targeted campaign, or analyzing market trends, our data solutions ensure measurable success.

...

Context

Typically e-commerce datasets are proprietary and consequently hard to find among publicly available data. However, The UCI Machine Learning Repository has made this dataset containing actual transactions from 2010 and 2011. The dataset is maintained on their site, where it can be found by the title "Online Retail".

Content

"This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers."

Acknowledgements

Per the UCI Machine Learning Repository, this data was made available by Dr Daqing Chen, Director: Public Analytics group. chend '@' lsbu.ac.uk, School of Engineering, London South Bank University, London SE1 0AA, UK.

Image from stocksnap.io.

Inspiration

Analyses for this dataset could include time series, clustering, classification and more.

NoSQL Software Market Outlook

The global NoSQL software market size was valued at approximately USD 6 billion in 2023 and is projected to reach around USD 20 billion by 2032, growing at a compound annual growth rate (CAGR) of 14% during the forecast period. This market is driven by the escalating need for operational efficiency, flexibility, and scalability in database management systems, particularly in enterprises dealing with vast amounts of unstructured data.

One of the primary growth factors propelling the NoSQL software market is the exponential increase in data volumes generated by various digital platforms, IoT devices, and social media. Traditional relational databases often struggle to handle this surge efficiently, prompting organizations to shift towards NoSQL databases that offer more flexibility and scalability. The ability to store and process large sets of unstructured data without needing a predefined schema makes NoSQL databases an attractive choice for modern businesses seeking agility and speed in data management.

Moreover, the proliferation of cloud computing services has significantly contributed to the growth of the NoSQL software market. Cloud-based NoSQL databases provide cost-effective, scalable, and easily accessible solutions for enterprises of all sizes. The pay-as-you-go pricing model and the capacity to scale resources based on demand have made NoSQL databases a preferred option for startups and large enterprises alike. The seamless integration of NoSQL databases with cloud infrastructure enhances operational efficiencies and reduces the complexities associated with database management.

Another critical driver is the increasing adoption of NoSQL databases in various industry verticals such as retail, BFSI, IT, and healthcare. These industries require robust data management solutions to handle large volumes of diverse data types. NoSQL databases, with their flexible data models and high performance, cater to these requirements efficiently. In the retail sector, for example, NoSQL databases are used to manage customer data, product catalogs, and transaction histories, enabling more personalized and efficient customer services.

Regionally, North America holds a significant share of the NoSQL software market due to the presence of major technology companies and a mature IT infrastructure. The rapid digital transformation across enterprises in the region, alongside substantial investments in big data analytics and cloud computing, further fuels market growth. Additionally, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the expanding IT sector, increased adoption of cloud services, and significant investments in digital technologies in countries like China and India.

Graph Databases Software has emerged as a crucial component in the landscape of NoSQL databases, particularly for applications that require understanding complex relationships between data entities. Unlike traditional databases that store data in tables, graph databases use nodes, edges, and properties to represent and store data, making them ideal for scenarios where relationships are as important as the data itself. This approach is particularly beneficial in fields such as social networking, where the ability to analyze connections between users can provide deep insights into social dynamics and influence patterns. As businesses increasingly seek to leverage data for competitive advantage, the demand for graph databases is expected to grow, driven by their ability to efficiently model and query interconnected data.

Type Analysis

The NoSQL software market is segmented into various types, including Document-Oriented, Key-Value Store, Column-Oriented, and Graph-Based databases. Document-oriented databases, such as MongoDB, store data in JSON-like documents, offering flexibility in data modeling and ease of use. These databases are widely used for content management systems, e-commerce applications, and real-time analytics. Their ability to handle semi-structured data and scalability features make them a popular choice among developers and enterprises seeking agile database solutions.

Key-Value Store databases, such as Redis and Amazon DynamoDB, store data as a collection of key-value pairs, providing ultra-fast read and write operations. These databases are ideal for applications requiring high-speed data retrieval, such as caching, session manag

Metric Store Database Market Outlook

As per our latest research, the global metric store database market size reached USD 2.31 billion in 2024, driven by the escalating demand for real-time analytical capabilities across industries. The market is experiencing robust expansion, with a projected CAGR of 15.2% from 2025 to 2033. By leveraging this growth trajectory, the metric store database market is forecasted to achieve a value of USD 7.16 billion by 2033. This significant progress is largely attributed to the rapid adoption of digital transformation initiatives and the proliferation of IoT devices, which are generating vast volumes of time-series and metric data that necessitate specialized storage and analysis solutions.

One of the primary growth drivers for the metric store database market is the increasing emphasis on data-driven decision-making within organizations. Enterprises are increasingly recognizing the value of real-time insights for streamlining operations, enhancing customer experiences, and maintaining a competitive edge. Metric store databases, designed to efficiently store, process, and retrieve time-series data, are becoming indispensable for applications in data analytics, business intelligence, and real-time monitoring. The rise of technologies such as artificial intelligence and machine learning further amplifies the need for robust metric data management, as these technologies rely on high-quality, timely data to deliver actionable insights. The ability of metric store databases to handle high ingestion rates, scalability, and low-latency queries positions them as a critical component in modern data architectures.

Another significant factor fueling the market's expansion is the rapid adoption of IoT across multiple sectors, including manufacturing, healthcare, and smart cities. IoT devices continuously generate streams of metrics that require efficient storage and real-time analysis. Metric store databases are specifically architected to manage this influx, offering features such as compression, downsampling, and retention policies that optimize storage costs while ensuring data accessibility. The integration of metric store databases with cloud platforms has also democratized access to advanced analytics, enabling organizations of all sizes to harness the power of real-time data without the burden of extensive infrastructure investments. The convergence of IoT and cloud computing is expected to accelerate market growth further as businesses strive for operational efficiency and innovation.

The growing complexity of IT infrastructures and the need for proactive monitoring solutions represent another pivotal growth factor for the metric store database market. Modern enterprises are managing increasingly distributed and dynamic environments, encompassing on-premises, cloud, and hybrid deployments. Metric store databases play a crucial role in monitoring system performance, detecting anomalies, and ensuring service reliability. The integration of these databases with observability and DevOps tools streamlines incident response and reduces downtime, translating into tangible business benefits. As organizations continue to prioritize uptime and user experience, the adoption of metric store databases for real-time monitoring and alerting is poised to surge, further solidifying their role in the digital ecosystem.

From a regional perspective, North America currently dominates the metric store database market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. This leadership is attributed to the high concentration of technology-driven enterprises, early adoption of cloud-based solutions, and a robust ecosystem of data analytics vendors. Asia Pacific, however, is emerging as a high-growth region, fueled by rapid digitalization, expanding IT infrastructure, and increasing investments in smart city and IoT initiatives. The Middle East & Africa and Latin America are also witnessing steady growth, albeit from a smaller base, as businesses in these regions increasingly recognize the value of real-time data analytics for operational efficiency and innovation.

This is the dataset used for paper: "A Recommender System of Buggy App Checkers for App Store Moderators", published on the International Conference on Mobile Software Engineering and Systems (MOBILESoft) in 2015.

Dataset Collection We built a dataset that consists of a random sample of Android app metadata and user reviews available on the Google Play Store on January and March 2014. Since the Google Play Store is continuously evolving (adding, removing and/or updating apps), we updated the dataset twice. The dataset D1 contains available apps in the Google Play Store in January 2014. Then, we created a new snapshot (D2) of the Google Play Store in March 2014.

The apps belong to the 27 different categories defined by Google (at the time of writing the paper), and the 4 predefined subcategories (free, paid, new_free, and new_paid). For each category-subcategory pair (e.g. tools-free, tools-paid, sports-new_free, etc.), we collected a maximum of 500 samples, resulting in a median number of 1.978 apps per category.

For each app, we retrieved the following metadata: name, package, creator, version code, version name, number of downloads, size, upload date, star rating, star counting, and the set of permission requests.

In addition, for each app, we collected up to a maximum of the latest 500 reviews posted by users in the Google Play Store. For each review, we retrieved its metadata: title, description, device, and version of the app. None of these fields were mandatory, thus several reviews lack some of these details. From all the reviews attached to an app, we only considered the reviews associated with the latest version of the app —i.e., we discarded unversioned and old-versioned reviews. Thus, resulting in a corpus of 1,402,717 reviews (2014 Jan.).

Dataset Stats Some stats about the datasets:

• D1 (Jan. 2014) contains 38,781 apps requesting 7,826 different permissions, and 1,402,717 user reviews.

• D2 (Mar. 2014) contains 46,644 apps and 9,319 different permission requests, and 1,361,319 user reviews.

Additional stats about the datasets are available here.

Dataset Description To store the dataset, we created a graph database with Neo4j. This dataset therefore consists of a graph describing the apps as nodes and edges. We chose a graph database because the graph visualization helps to identify connections among data (e.g., clusters of apps sharing similar sets of permission requests).

In particular, our dataset graph contains six types of nodes: - APP nodes containing metadata of each app, - PERMISSION nodes describing permission types, - CATEGORY nodes describing app categories, - SUBCATEGORY nodes describing app subcategories, - USER_REVIEW nodes storing user reviews. - TOPIC topics mined from user reviews (using LDA).

Furthermore, there are five types of relationships between APP nodes and each of the remaining nodes:

• USES_PERMISSION relationships between APP and PERMISSION nodes
• HAS_REVIEW between APP and USER_REVIEW nodes
• HAS_TOPIC between USER_REVIEW and TOPIC nodes
• BELONGS_TO_CATEGORY between APP and CATEGORY nodes
• BELONGS_TO_SUBCATEGORY between APP and SUBCATEGORY nodes

Dataset Files Info

Neo4j 2.0 Databases

googlePlayDB1-Jan2014_neo4j_2_0.rar

googlePlayDB2-Mar2014_neo4j_2_0.rar We provide two Neo4j databases containing the 2 snapshots of the Google Play Store (January and March 2014). These are the original databases created for the paper. The databases were created with Neo4j 2.0. In particular with the tool version 'Neo4j 2.0.0-M06 Community Edition' (latest version available at the time of implementing the paper in 2014).

Neo4j 3.5 Databases

googlePlayDB1-Jan2014_neo4j_3_5_28.rar

googlePlayDB2-Mar2014_neo4j_3_5_28.rar Currently, the version Neo4j 2.0 is deprecated and it is not available for download in the official Neo4j Download Center. We have migrated the original databases (Neo4j 2.0) to Neo4j 3.5.28. The databases can be opened with the tool version: 'Neo4j Community Edition 3.5.28'. The tool can be downloaded from the official Neo4j Donwload page.

  In order to open the databases with more recent versions of Neo4j, the databases must be first migrated to the corresponding version. Instructions about the migration process can be found in the Neo4j Migration Guide.

  First time the Neo4j database is connected, it could request credentials. The username and pasword are: neo4j/neo4j

This data includes the name, type, and location of active retail tobacco and vapor product vendors operating in New York State. Active retail tobacco and vapor product vendors include only vendors that were categorized as active (i.e. open to the public) on the date the data was downloaded from a DOH database. The vendor type includes, for example, convenience stores or grocery supermarkets. The location of the vendor includes its street address, city, state, zip code, municipality, and county.

This comprehensive IKEA USA products dataset contains detailed information about thousands of authentic IKEA furniture items, home decor, and household products available in the United States market. The dataset provides complete product specifications, pricing, availability, and detailed descriptions for ecommerce analysis, price comparison, and furniture retail research.

Key Features:

• Complete IKEA USA product catalog with real pricing data
• Detailed product descriptions and specifications
• Product URLs, article numbers, and availability status
• Furniture categories including office chairs, storage solutions, outdoor furniture
• Home decor items like candle holders, planters, and textiles
• Kitchen cabinets, wardrobes, and organizational systems
• Material specifications and sustainability information
• Product dimensions, weights, and packaging details

Get Free Sample: Download your free sample dataset now to explore the data quality and structure before purchasing the complete IKEA USA products database. The free sample includes representative product entries with all key fields populated.

Applications: Perfect for furniture market analysis, home improvement research, interior design planning, competitive pricing analysis, and retail intelligence. This dataset enables businesses to understand IKEA pricing strategies, product positioning, and market trends in the home furnishing industry.

Product Categories Included: Office furniture, bedroom furniture, storage solutions, outdoor dining sets, kitchen systems, home organization products, decorative accessories, plant containers, and sustainable furniture options. All products include comprehensive details for business intelligence and market research applications.

Market Basket Analysis

Market basket analysis with Apriori algorithm

The retailer wants to target customers with suggestions on itemset that a customer is most likely to purchase .I was given dataset contains data of a retailer; the transaction data provides data around all the transactions that have happened over a period of time. Retailer will use result to grove in his industry and provide for customer suggestions on itemset, we be able increase customer engagement and improve customer experience and identify customer behavior. I will solve this problem with use Association Rules type of unsupervised learning technique that checks for the dependency of one data item on another data item.

Introduction

Association Rule is most used when you are planning to build association in different objects in a set. It works when you are planning to find frequent patterns in a transaction database. It can tell you what items do customers frequently buy together and it allows retailer to identify relationships between the items.

An Example of Association Rules

Assume there are 100 customers, 10 of them bought Computer Mouth, 9 bought Mat for Mouse and 8 bought both of them. - bought Computer Mouth => bought Mat for Mouse - support = P(Mouth & Mat) = 8/100 = 0.08 - confidence = support/P(Mat for Mouse) = 0.08/0.09 = 0.89 - lift = confidence/P(Computer Mouth) = 0.89/0.10 = 8.9 This just simple example. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.

Strategy

• Data Import
• Data Understanding and Exploration
• Transformation of the data – so that is ready to be consumed by the association rules algorithm
• Running association rules
• Exploring the rules generated
• Filtering the generated rules
• Visualization of Rule

Dataset Description

• File name: Assignment-1_Data
• List name: retaildata
• File format: . xlsx
• Number of Row: 522065

• Number of Attributes: 7

  • BillNo: 6-digit number assigned to each transaction. Nominal.
  • Itemname: Product name. Nominal.
  • Quantity: The quantities of each product per transaction. Numeric.
  • Date: The day and time when each transaction was generated. Numeric.
  • Price: Product price. Numeric.
  • CustomerID: 5-digit number assigned to each customer. Nominal.
  • Country: Name of the country where each customer resides. Nominal.

https://user-images.githubusercontent.com/91852182/145270162-fc53e5a3-4ad1-4d06-b0e0-228aabcf6b70.png">

Libraries in R

First, we need to load required libraries. Shortly I describe all libraries.

• arules - Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules).
• arulesViz - Extends package 'arules' with various visualization. techniques for association rules and item-sets. The package also includes several interactive visualizations for rule exploration.
• tidyverse - The tidyverse is an opinionated collection of R packages designed for data science.
• readxl - Read Excel Files in R.
• plyr - Tools for Splitting, Applying and Combining Data.
• ggplot2 - A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
• knitr - Dynamic Report generation in R.
• magrittr- Provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions.
• dplyr - A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
• tidyverse - This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step.

https://user-images.githubusercontent.com/91852182/145270210-49c8e1aa-9753-431b-a8d5-99601bc76cb5.png">

Data Pre-processing

Next, we need to upload Assignment-1_Data. xlsx to R to read the dataset.Now we can see our data in R.

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After we will clear our data frame, will remove missing values.

https://user-images.githubusercontent.com/91852182/145270286-05854e1a-2b6c-490e-ab30-9e99e731eacb.png">

To apply Association Rule mining, we need to convert dataframe into transaction data to make all items that are bought together in one invoice will be in ...

Service Topology Graph Database Market Outlook

According to our latest research, the global service topology graph database market size reached USD 1.42 billion in 2024, demonstrating robust momentum with a compound annual growth rate (CAGR) of 21.8%. The market is expected to achieve a value of USD 10.62 billion by 2033. This impressive growth is primarily driven by the increasing demand for advanced data management solutions, the proliferation of complex IT infrastructures, and the rising necessity for real-time analytics and visualization across diverse industries. The market’s rapid expansion is further bolstered by technological advancements in graph database architectures and the growing adoption of cloud-based deployment models.

One of the most significant growth factors in the service topology graph database market is the escalating complexity of modern IT environments. As organizations transition toward hybrid and multi-cloud infrastructures, the need for solutions that can accurately map and manage intricate service relationships has become paramount. Graph databases excel at representing highly interconnected data, making them ideal for modeling service topologies. This capability enables enterprises to visualize dependencies, identify bottlenecks, and optimize resource allocation, thereby enhancing operational efficiency and minimizing downtime. Additionally, the growing integration of artificial intelligence and machine learning with graph databases allows for predictive analytics and automated anomaly detection, further fueling market growth.

Another key driver is the surge in demand for enhanced network management and security. With the increasing frequency and sophistication of cyber threats, organizations are seeking comprehensive solutions to monitor and secure their networks. Service topology graph databases provide unparalleled visibility into network structures, enabling proactive identification of vulnerabilities and facilitating rapid incident response. These databases support real-time monitoring and compliance tracking, which are critical for industries with stringent regulatory requirements such as BFSI and healthcare. The ability to correlate data from multiple sources and uncover hidden patterns is proving invaluable for security teams, making graph databases an essential component of modern cybersecurity strategies.

The expanding adoption of digital transformation initiatives across various sectors also contributes to the market’s growth. Enterprises are leveraging service topology graph databases to streamline asset management, optimize IT operations, and improve customer experiences. In the retail sector, for example, these databases help map customer journeys and personalize interactions by analyzing relationships between products, users, and transactions. In manufacturing, they facilitate predictive maintenance and supply chain optimization by modeling equipment dependencies and process flows. As organizations continue to prioritize data-driven decision-making, the demand for graph-based solutions is expected to rise significantly, further propelling the market forward.

From a regional perspective, North America currently leads the global market, accounting for the largest revenue share in 2024. This dominance is attributed to the presence of major technology vendors, early adoption of advanced IT solutions, and significant investments in research and development. Europe follows closely, driven by stringent data privacy regulations and the need for efficient compliance management. The Asia Pacific region is witnessing the fastest growth, fueled by rapid digitalization, expanding IT infrastructure, and increasing investments in cloud computing. Latin America and the Middle East & Africa are also experiencing steady growth, supported by government initiatives to modernize public services and enhance cybersecurity capabilities.

Component Analysis

The component segment of the service topology graph database market is bifurcated into software and services, each playing a pivotal role in driving overall market expansion. The software sub-segment dominates the market, owing to the continuous evolution of graph database platforms that offer enhanced scalability, flexibility, and integration capabilities. Modern graph database software solutions are equipped with advanced visualization tools, intuitive user interfaces, and robust APIs, enabling seamless in

App Store Data (November 2025)

This repository contains sample database with 1,000 apps. The full database has 1,165,456 applications.

  Selects

Open the database using any SQLite3 GUI app (e.g. SQLiteStudio or SQLiteBrowser) and make some queries. Some examples are below.

  Most Expensive Non-Free Apps

SELECT s.canonical_url, s.app_name, s.currency, s.total_ratings, s.rating_average, a.category, a.subcategory, MAX(s.price / 100.0 /… See the full description on the dataset page: https://huggingface.co/datasets/appstoredb/appstore_apps_database.

Discover the unparalleled potential of our comprehensive eCommerce leads database, featuring essential data fields such as Store Name, Website, Contact First Name, Contact Last Name, Email Address, Physical Address, City, State, Country, Zip Code, Phone Number, Revenue Size, Employee Size, and more on demand.

With a focus on Shopify, Amazon, eBay, and other global retail stores, this database equips you with accurate information for successful marketing campaigns. Supercharge your marketing efforts with our enriched contact and company database, providing real-time, verified data insights for strategic market assessments and effective buyer engagement across digital and traditional channels.

• 4M+ eCommerce Companies • 40M+ Worldwide eCommerce Leads • Direct Contact Info for Shop Owners • 47+ eCommerce Platforms • 40+ Data Points • Lifetime Access • 10+ Data Segmentations • Sample Data"

An example of the Row Table.

This database contains a time series of consumer-level prices for a sample of retail markets in Honolulu between 2007-2011. Data include weekly prices for fish species prevalent in Honolulu retail seafood markets. Additionally, each record contains information on the product form, origin of the fish (if known), labelling schemes, quality (where applicable), and the use of preservation methods (such as CO-treatment).

An example of the Schema Table.

Cloud Database Market Outlook

According to our latest research, the global cloud database market size reached USD 21.8 billion in 2024, reflecting robust adoption across industries due to increasing digital transformation initiatives. The market is expected to maintain a strong growth trajectory, with a CAGR of 15.2% from 2025 to 2033. By the end of the forecast period in 2033, the cloud database market is projected to achieve a value of USD 58.7 billion. This growth is primarily fueled by the escalating demand for scalable, flexible, and cost-efficient data management solutions, as organizations leverage cloud technologies to drive innovation and operational efficiency.

One of the primary growth factors for the cloud database market is the exponential increase in data generation across enterprises of all sizes and sectors. The proliferation of IoT devices, mobile applications, and digital business models has resulted in vast amounts of structured and unstructured data that require agile and scalable storage solutions. Cloud databases offer seamless scalability, high availability, and real-time data processing capabilities, making them an ideal choice for organizations aiming to harness big data analytics and derive actionable insights. Furthermore, the shift to remote and hybrid work environments has accelerated cloud adoption, as businesses seek to ensure data accessibility and collaboration across distributed teams.

Another significant driver is the continuous advancement in cloud computing technologies, including the integration of artificial intelligence (AI) and machine learning (ML) with cloud databases. Leading cloud service providers are investing heavily in enhancing their database offerings with advanced analytics, automated management, and security features. These innovations are lowering the barriers to entry for enterprises, enabling them to deploy sophisticated database solutions without the need for extensive in-house IT expertise. Additionally, the rise of multi-cloud and hybrid cloud strategies is giving organizations greater flexibility to optimize workloads, enhance disaster recovery, and comply with data sovereignty regulations.

The rapid digitalization of core business processes across industry verticals is also contributing to the robust growth of the cloud database market. Sectors such as BFSI, healthcare, retail, and manufacturing are leveraging cloud databases to modernize legacy systems, improve customer experiences, and launch new digital services. Regulatory compliance, data security, and the need for real-time analytics are driving enterprises to adopt cloud-native and managed database solutions. As a result, cloud databases are becoming integral to enterprise IT strategies, enabling organizations to remain competitive in an increasingly data-driven economy.

From a regional perspective, North America continues to dominate the global cloud database market, owing to the presence of major cloud service providers, early technology adoption, and mature digital infrastructure. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid economic growth, increasing cloud investments, and the digital transformation of small and medium enterprises (SMEs). Europe is also witnessing significant adoption, particularly in sectors such as BFSI and government, where data privacy and compliance are paramount. The Middle East & Africa and Latin America are gradually catching up, driven by growing awareness of cloud benefits and government-led digital initiatives.

Database Type Analysis

The cloud database market is segmented by database type into SQL, NoSQL, NewSQL, and others, each catering to distinct data management needs and application scenarios. SQL databases remain the backbone of enterprise data management, favored for their robust transactional support, data integrity, and mature ecosystem. These databases are widely used in industries with stringent data consistency and regulatory requirements, such as banking, government, and h