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 manufacturer, distributor or importer to track the tuna back to harvest and verify the dolphin-safe status of the tuna product.

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!

Catalog Management Systems Market size was valued at USD 1.42 Billion in 2024 and is projected to reach USD 2.42 Billion by 2031, growing at a CAGR of 7.60% from 2024 to 2031.

Global Catalog Management Systems Market Drivers

Rapid Growth of eCommerce and Digital Marketplaces: The increasing expansion of e-commerce is pushing the demand for robust catalog management systems. According to the US Census Bureau, e-commerce sales in the United States reached $870.8 billion in 2021, up 14.2% from 2020. This rapid expansion needs effective catalog management to accommodate the growing volume and complexity of product information across numerous channels.

Increasing Adoption of Cloud-based Solutions: The transition to cloud-based catalog management systems is a significant market driver. According to Gartner, the global public cloud services market will rise 21.7% to $591.8 billion by 2023, up from $490.3 billion in 2022. This trend underscores the growing demand for scalable, adaptable, and cost-effective catalog management solutions that can be seamlessly integrated with existing systems.

Rising Demand for Personalized Customer Experiences: The demand for individualized shopping experiences is driving firms to invest in advanced catalog management systems. According to an Accenture survey, 91% of consumers prefer to purchase with businesses that know, recall, and provide relevant offers and recommendations. Effective catalog management allows firms to personalize product information and recommendations based on consumer preferences and behaviors, resulting in increased sales and customer loyalty.

This data set contains preliminary retail sales data for cannabis and cannabis products by product type in the adult-use cannabis and medical marijuana markets based on total sales data. The data reported is compiled at specific points in time and only captures data current at the time the report is generated. The monthly data set captures retail cannabis sales from the first through the last day of the month. Data values may be updated and change over time as updates occur. Accordingly, monthly reported data may not exactly match annually reported data.

China Retail Price Index: Urban: Food: Milk and Its Product data was reported at 101.000 Prev Year=100 in 2022. This records a decrease from the previous number of 102.100 Prev Year=100 for 2021. China Retail Price Index: Urban: Food: Milk and Its Product data is updated yearly, averaging 101.300 Prev Year=100 from Dec 1994 (Median) to 2022, with 29 observations. The data reached an all-time high of 134.300 Prev Year=100 in 1994 and a record low of 98.845 Prev Year=100 in 2015. China Retail Price Index: Urban: Food: Milk and Its Product data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Inflation – Table CN.IB: Retail Price Index: Urban: Annual.

Chile Retail E-Commerce Index: Pharmaceutical, Cosmetic & Personal Care Products data was reported at 1,013.775 2018=100 in Jan 2025. This records a decrease from the previous number of 1,606.003 2018=100 for Dec 2024. Chile Retail E-Commerce Index: Pharmaceutical, Cosmetic & Personal Care Products data is updated monthly, averaging 679.173 2018=100 from Jan 2018 (Median) to Jan 2025, with 85 observations. The data reached an all-time high of 1,641.413 2018=100 in Oct 2024 and a record low of 51.626 2018=100 in Jan 2018. Chile Retail E-Commerce Index: Pharmaceutical, Cosmetic & Personal Care Products data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Chile – Table CL.H022: Retail E-Commerce Index.

The Relational Database Software Market size was estimated at USD 21.97 Billion in 2024 and is projected to reach USD 45.23 Billion by 2031, growing at a CAGR of 9.4 % from 2024 to 2031

Global Relational Database Software Market Drivers

Rising Demand for Efficient Data Management: Organizations across industries are generating and collecting ever-increasing volumes of data. This necessitates efficient and secure data management solutions. Relational databases, with their structured format and robust querying capabilities, offer a valuable tool to organize, manage, and analyze this data, leading to increased demand for this software.

Cloud Adoption and Scalability: The proliferation of cloud computing has significantly impacted the relational database market. Cloud-based database solutions offer scalability, flexibility, and reduced IT infrastructure burden for businesses. This makes them particularly attractive for small and medium-sized enterprises (SMEs) and facilitates easier data access for geographically dispersed teams.

Growing Importance of Data Security and Compliance: Data breaches and cyberattacks pose significant threats to businesses. Relational database software vendors are constantly innovating to enhance security features like encryption and access controls. Additionally, stringent data privacy regulations like GDPR and CCPA are driving the need for compliant data storage and management solutions, further propelling the market for secure relational databases.

Russia Structure of Retail Price: Sour Cream: With Fat: Total Production Cost data was reported at 69.870 % in 2017. This records an increase from the previous number of 69.750 % for 2016. Russia Structure of Retail Price: Sour Cream: With Fat: Total Production Cost data is updated yearly, averaging 67.940 % from Dec 2008 (Median) to 2017, with 10 observations. The data reached an all-time high of 70.370 % in 2013 and a record low of 62.080 % in 2008. Russia Structure of Retail Price: Sour Cream: With Fat: Total Production Cost data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Prices – Table RU.PF001: Structure of Retail Price: Food Products: Annual.

Open Prices What is Open Prices? Open Prices is a project to collect and share prices of products around the world. It's a publicly available dataset that can be used for research, analysis, and more. Open Prices is developed and maintained by Open Food Facts. There are currently few companies that own large databases of product prices at the barcode level. These prices are not freely available, but sold at a high price to private actors, researchers and other organizations that can afford them. Open Prices aims to democratize access to price data by collecting and sharing product prices under an open licence. The data is available under the Open Database License (ODbL), which means that it can be used for any purpose, as long as you credit Open Prices and share any modifications you make to the dataset. Images submitted as proof are licensed under the Creative Commons Attribution-ShareAlike 4.0 International. Dataset description This dataset contains in Parquet format all price information contained in the Open Prices database. The dataset is updated daily. Here is a description of the most important columns: id: The ID of the price in DB product_code: The barcode of the product, null if the product is a "raw" product (fruit, vegetable, etc.) category_tag: The category of the product, only present for "raw" products. We follow Open Food Facts category taxonomy for category IDs. labels_tags: The labels of the product, only present for "raw" products. We follow Open Food Facts label taxonomy for label IDs. origins_tags: The origins of the product, only present for "raw" products. We follow Open Food Facts origin taxonomy for origin IDs. price: The price of the product, with the discount if any. price_is_discounted: Whether the price is discounted or not. price_without_discount: The price of the product without discount, null if the price is not discounted. price_per: The unit for which the price is given (e.g. "KILOGRAM", "UNIT") currency: The currency of the price location_osm_id: The OpenStreetMap ID of the location where the price was recorded. We use OpenStreetMap to identify uniquely the store where the price was recorded. location_osm_type: The type of the OpenStreetMap location (e.g. "NODE", "WAY") location_id: The ID of the location in the Open Prices database date: The date when the price was recorded proof_id: The ID of the proof of the price in the Open Prices DB owner: a hash of the owner of the price, for privacy. created: The date when the price was created in the Open Prices DB updated: The date when the price was last updated in the Open Prices DB proof_file_path: The path to the proof file in the Open Prices DB proof_type: The type of the proof. Possible values are RECEIPT, PRICE_TAG, GDPR_REQUEST, SHOP_IMPORT proof_date: The date of the proof proof_currency: The currency of the proof, should be the same as the price currency proof_created: The datetime when the proof was created in the Open Prices DB proof_updated: The datetime when the proof was last updated in the Open Prices DB location_osm_display_name: The display name of the OpenStreetMap location location_osm_address_city: The city of the OpenStreetMap location location_osm_address_postcode: The postcode of the OpenStreetMap location How can I download images? All images can be accessed under the https://prices.openfoodfacts.org/img/ base URL. You just have to concatenate the proof_file_path column to this base URL to get the full URL of the image (ex: https://prices.openfoodfacts.org/img/0010/lqGHf3ZcVR.webp). Can I contribute to Open Prices? Of course! You can contribute by adding prices, trough the Open Prices website or through Open Food Facts mobile app. To participate in the technical development, you can check the Open Prices GitHub repository.

A complete scraping and screening of one of your competitors' catalog in order to see price and discount tiers, assortment structures, product data and more. Data can be delivered as raw data or matched to your own assortment (on a product group/category level, or on a product level) to your business intelligence tool or any other system.

Database Automation Systems Market size was valued at USD 1875.08 Million in 2024 and is projected to reach USD 13193.79 Million by 2031, growing at a CAGR of 27.62% from 2024 to 2031.

Database Automation Systems Market: Definition/ Overview

Database automation systems refer to tools and technologies that automate tasks involved in database management, such as provisioning, patching, backup, monitoring, and security. These solutions reduce manual intervention, ensuring greater efficiency, accuracy, and system performance. Database automation is widely used across IT & Telecom, banking and finance, healthcare, retail, and manufacturing industries. In IT, it facilitates seamless data management, while in finance, it ensures compliance and secure handling of customer data. Healthcare organizations utilize these systems to manage medical records and regulatory compliance, and retail businesses rely on them for efficient handling of inventory and transaction data.

Russia Structure of Retail Price: Corvalol: Retailers Costs for Delivery and Sell data was reported at 17.900 % in 2017. This records a decrease from the previous number of 18.080 % for 2016. Russia Structure of Retail Price: Corvalol: Retailers Costs for Delivery and Sell data is updated yearly, averaging 17.590 % from Dec 2010 (Median) to 2017, with 8 observations. The data reached an all-time high of 20.000 % in 2014 and a record low of 11.150 % in 2010. Russia Structure of Retail Price: Corvalol: Retailers Costs for Delivery and Sell data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Prices – Table RU.PF002: Structure of Retail Price: Non Food Products: Annual.

The size and share of the market is categorized based on Application (Telecom And Information Technology, Banking And Financial Service, Oil And Gas, Manufacturing, Healthcare, Government, Travel And Transport, Retail, Entertainment And Media, Other) and Product (Cloud Identity And Access Management, Cloud Data And Cdn Security, Cloud Activity Monitoring, Others) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

United States Retail Sales Nowcast: sa: YoY: Contribution: E-Commerce: E-Commerce Transactions: Volume: Lifestyle: Jewelry & Luxury Products data was reported at 0.000 % in 03 Mar 2025. This stayed constant from the previous number of 0.000 % for 24 Feb 2025. United States Retail Sales Nowcast: sa: YoY: Contribution: E-Commerce: E-Commerce Transactions: Volume: Lifestyle: Jewelry & Luxury Products data is updated weekly, averaging 0.000 % from Feb 2020 (Median) to 03 Mar 2025, with 264 observations. The data reached an all-time high of 18.452 % in 09 Dec 2024 and a record low of 0.000 % in 03 Mar 2025. United States Retail Sales Nowcast: sa: YoY: Contribution: E-Commerce: E-Commerce Transactions: Volume: Lifestyle: Jewelry & Luxury Products 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.

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Data includes: Store Traffic Store Category Brand Name Store company full address Company Employee Count Company Year Founded Company LinkedIn URL Store Contact First Name Store Contact Last Name Owner Direct Email Company Generic Email Company Phone Number Owner Linkedin URL Store Revenue Store Top Seller Country Organic Traffic % Paid Traffic %

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The market size of the Big Data Professional Services Market is categorized based on Application (Telecommunication and Media, Financial Services, Retail, Manufacturing, Transport and Logistics, Healthcare, Public Sector, Energy, Others) and Product (Database Management Tools, Big Data Analytics Tools, Big Data Integration Tools, Data Warehousing Tools, Traditional BI Solutions, Data Analysis Services, Others) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and África).

Este informe proporciona información sobre el tamaño del mercado y pronostica el valor del mercado, expresado en USD millones, en estos segmentos definidos.

Russia Structure of Retail Price: Vodka 40% & up: Retailers Costs for Delivery and Sell data was reported at 8.810 % in 2017. This records a decrease from the previous number of 9.020 % for 2016. Russia Structure of Retail Price: Vodka 40% & up: Retailers Costs for Delivery and Sell data is updated yearly, averaging 10.330 % from Dec 2008 (Median) to 2017, with 10 observations. The data reached an all-time high of 12.260 % in 2010 and a record low of 8.810 % in 2017. Russia Structure of Retail Price: Vodka 40% & up: Retailers Costs for Delivery and Sell data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Prices – Table RU.PF001: Structure of Retail Price: Food Products: Annual.

Russia Structure of Retail Price: Vodka 40% & up: VAT Accrued by Retailer data was reported at 3.080 % in 2017. This records an increase from the previous number of 3.070 % for 2016. Russia Structure of Retail Price: Vodka 40% & up: VAT Accrued by Retailer data is updated yearly, averaging 3.705 % from Dec 2008 (Median) to 2017, with 10 observations. The data reached an all-time high of 11.220 % in 2008 and a record low of 3.070 % in 2016. Russia Structure of Retail Price: Vodka 40% & up: VAT Accrued by Retailer data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Prices – Table RU.PF001: Structure of Retail Price: Food Products: Annual.

Russia Structure of Retail Price: Butter: Commercial Expenses data was reported at 3.710 % in 2017. This records a decrease from the previous number of 3.800 % for 2016. Russia Structure of Retail Price: Butter: Commercial Expenses data is updated yearly, averaging 3.550 % from Dec 2008 (Median) to 2017, with 10 observations. The data reached an all-time high of 4.220 % in 2014 and a record low of 2.240 % in 2010. Russia Structure of Retail Price: Butter: Commercial Expenses data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Prices – Table RU.PF001: Structure of Retail Price: Food Products: Annual.

Russia Inventories Index: Mobile Phones data was reported at 108.800 Prev Mth=100 in Jan 2019. This records a decrease from the previous number of 113.900 Prev Mth=100 for Dec 2018. Russia Inventories Index: Mobile Phones data is updated monthly, averaging 101.600 Prev Mth=100 from Oct 2008 (Median) to Jan 2019, with 123 observations. The data reached an all-time high of 131.900 Prev Mth=100 in Oct 2015 and a record low of 78.600 Prev Mth=100 in Jan 2017. Russia Inventories Index: Mobile Phones data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Wholesale, Retail and Catering Sector – Table RU.RJB005: Retail Sales: Product Inventory and Inventories Index.