The Transportation and Logistics Tracking Dataset comprises multiple datasets related to various aspects of transportation and logistics operations. It includes information on on-time delivery impact, routes by rating, customer ratings, delivery times with and without congestion, weather conditions, and differences between fixed and main delivery times across different regions.

On-Time Delivery Impact: This dataset provides insights into the impact of on-time delivery, categorizing deliveries based on their impact and counting the occurrences for each category. Routes by Rating: Here, the dataset illustrates the relationship between routes and their corresponding ratings, offering a visual representation of route performance across different rating categories. Customer Ratings and On-Time Delivery: This dataset explores the relationship between customer ratings and on-time delivery, presenting a comparison of delivery counts based on customer ratings and on-time delivery status. Delivery Time with and Without Congestion: It contains information on delivery times in various cities, both with and without congestion, allowing for an analysis of how congestion affects delivery efficiency. Weather Conditions: This dataset provides a summary of weather conditions, including counts for different weather conditions such as partly cloudy, patchy light rain with thunder, and sunny. Difference between Fixed and Main Delivery Times: Lastly, the dataset highlights the differences between fixed and main delivery times across different regions, shedding light on regional variations in delivery schedules. Overall, this dataset offers valuable insights into the transportation and logistics domain, enabling analysis and decision-making to optimize delivery processes and enhance customer satisfaction.

Success.ai’s Transport and Logistics Data provides comprehensive, verified B2B contact and company information tailored for the global logistics sector. Drawing from a database of over 170 million verified professional profiles and 30 million company profiles, this dataset delivers accurate contact details, firmographic insights, and operational data on logistics service providers, freight forwarders, trucking companies, 3PLs, and supply chain management firms worldwide. Whether you’re targeting key decision-makers for partnerships, offering freight optimization technology, or conducting market research, Success.ai ensures your outreach and strategic planning are anchored in reliable, continuously updated, and AI-validated data.

Why Choose Success.ai’s Transport and Logistics Data?

1. Comprehensive Contact Information

   • Access verified work emails, direct phone numbers, and LinkedIn profiles of logistics leaders, operations managers, procurement officers, and supply chain directors.
   • AI-driven validation ensures 99% accuracy, allowing confident communication and reducing wasted outreach efforts.

2. Global Reach Across the Logistics Sector

   • Includes profiles from freight carriers, warehousing solutions, customs brokers, freight forwarders, and last-mile delivery companies.
   • Covers regions including North America, Europe, Asia-Pacific, South America, and the Middle East, giving you a panoramic view of logistics networks worldwide.

3. Continuously Updated Datasets

   • Real-time updates ensure your data remains current, reflecting changes in leadership, service offerings, and operational expansions in the rapidly evolving logistics industry.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global data privacy regulations, guaranteeing that your outreach and data usage align with legal and ethical standards.

Data Highlights:

• 170M+ Verified Professional Profiles: Connect with logistics professionals, key decision-makers, and operational leaders.
• 50M Work Emails: AI-validated for seamless communication with minimized bounce rates.
• 30M Company Profiles: In-depth firmographic and operational insights on logistics companies worldwide.
• 700M Global Professional Profiles: Enriched datasets to support competitive analysis, market entry strategies, and global expansion.

Key Features of the Dataset:

1. Logistics Decision-Maker Profiles

   • Identify and connect with CEOs, COOs, logistics managers, route planners, and procurement specialists influencing transportation strategies, warehousing decisions, and supply chain optimization.

2. Operational Firmographics and Insights

   • Access data on fleet size, service areas, warehouse locations, technology adoption, and shipping volumes to refine targeting and tailor your value propositions.
   • Utilize detailed operational insights to understand capacity constraints, specialization areas, and strategic priorities.

3. Advanced Filters for Precision Targeting

   • Filter contacts by mode of transportation (road, rail, air, sea), geographic coverage, company size, or industry specialization.
   • Align campaigns with unique market conditions, regulatory environments, and customer demands.

4. AI-Driven Enrichment

   • Profiles are enriched with actionable data, enabling personalized messaging, highlighting value-add solutions, and improving engagement outcomes with logistics stakeholders.

Strategic Use Cases:

1. Sales and Business Development

   • Engage with freight forwarders, trucking companies, and 3PL executives to present transportation management systems, visibility tools, or cost-reduction strategies.
   • Build relationships with leaders who control capacity planning, vendor selection, and route optimization.

2. Market Research and Competitive Analysis

   • Gain insights into emerging logistics hubs, evolving supply chain models, and technology adoption rates.
   • Benchmark against industry leaders to inform product innovation, pricing strategies, and market entry plans.

3. Partnership and Network Building

   • Identify potential partners for intermodal solutions, joint ventures, or collaborative warehousing arrangements.
   • Target logistics professionals interested in sustainability initiatives, e-commerce integrations, and cross-border trade solutions.

4. Recruitment and Talent Acquisition

   • Find HR professionals and operations managers seeking qualified drivers, dispatchers, warehouse staff, and logistics analysts.
   • Offer recruitment services or training programs to companies aiming to enhance operational efficiency and workforce quality.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access top-quality verified data at competitive prices, ensuring maximum ROI for your outreach and expansion efforts in the logistics sector.

2. Seamless Integration

   • Integrate verified contact an...

Kaggle Dataset: Synthetic Logistics Operations Database (2022-2024)

About this Dataset

What's Inside

A complete operational database from a fictional Class 8 trucking company spanning three years. This isn't scraped web data or simplified tutorial content—it's a realistic simulation built from 12 years of real-world logistics experience, designed specifically for analysts transitioning into supply chain and transportation domains.

The dataset contains 85,000+ records across 14 interconnected tables covering everything from driver assignments and fuel purchases to maintenance schedules and delivery performance. Each table maintains proper foreign key relationships, making this ideal for practicing complex SQL queries, building data pipelines, or developing operational dashboards.

Who This Is For

SQL Learners: Master window functions, CTEs, and multi-table JOINs using realistic business scenarios rather than contrived examples.

Data Analysts: Build portfolio projects that demonstrate understanding of operational metrics: cost-per-mile analysis, fleet utilization optimization, driver performance scorecards.

Aspiring Supply Chain Analysts: Work with authentic logistics data patterns—seasonal freight volumes, equipment utilization rates, route profitability calculations—without NDA restrictions.

Data Science Students: Develop predictive models for maintenance scheduling, driver retention, or route optimization using time-series data with actual business context.

Career Changers: If you're moving from operations into analytics (like the dataset creator), this provides a bridge—your domain knowledge becomes a competitive advantage rather than a gap to explain.

Why This Dataset Exists

Most logistics datasets are either proprietary (unavailable) or overly simplified (unrealistic). This fills the gap: operational complexity without confidentiality concerns. The data reflects real industry patterns:

• Fuel prices track the 2022 diesel spike and 2023-2024 decline
• Driver turnover sits at 15% annually (industry standard)
• Equipment utilization averages 65% (typical for dry van operations)
• On-time delivery performance ranges 85-95% (realistic service levels)
• Maintenance intervals follow Class 8 PM schedules

Dataset Structure

Core Entities (Reference Tables): - Drivers (150 records) - Demographics, employment history, CDL info - Trucks (120 records) - Fleet specs, acquisition dates, status - Trailers (180 records) - Equipment types, current assignments - Customers (200 records) - Shipper accounts, contract terms, revenue potential - Facilities (50 records) - Terminals and warehouses with geocoordinates - Routes (60+ records) - City pairs with distances and rate structures

Operational Transactions: - Loads (57,000+ records) - Shipment details, revenue, booking type - Trips (57,000+ records) - Driver-truck assignments, actual performance - Fuel Purchases (131,000+ records) - Transaction-level data with pricing - Maintenance Records (6,500+ records) - Service history, costs, downtime - Delivery Events (114,000+ records) - Pickup/delivery timestamps, detention - Safety Incidents (114 records) - Accidents, violations, claims

Aggregated Analytics: - Driver Monthly Metrics (5,400+ records) - Performance summaries - Truck Utilization Metrics (3,800+ records) - Equipment efficiency

Key Features

Temporal Coverage: January 2022 through December 2024 (3 years)

Geographic Scope: National operations across 25+ major US cities

Realistic Patterns: - Seasonal freight fluctuations (Q4 peaks) - Historical fuel price accuracy - Equipment lifecycle modeling - Driver retention dynamics - Service level variations

Data Quality: - Complete foreign key integrity - No orphaned records - Intentional 2% null rate in driver/truck assignments (reflects reality) - All timestamps properly sequenced - Financial calculations verified

Use Case Examples

Business Intelligence: Create executive dashboards showing revenue per truck, cost per mile, driver efficiency rankings, maintenance spend by equipment age, customer concentration risk.

Predictive Analytics: Build models forecasting equipment failures based on maintenance history, predict driver turnover using performance metrics, estimate route profitability for new lanes.

Operations Optimization: Analyze route efficiency, identify underutilized assets, optimize maintenance scheduling, calculate ideal fleet size, evaluate driver-to-truck ratios.

SQL Mastery: Practice window functions for running totals and rankings, write complex JOINs across 6+ tables, implement CTEs for hierarchical queries, perform cohort analysis on driver retention.

Sample Questions to Explore

1. Which routes generate the highest profit margin after fuel costs?
2. How does driver tenure correlate with fuel ef...

This dataset provides real-time data for smart logistics operations, capturing various aspects of supply chain management over the past year (2024). It includes information on asset tracking, inventory levels, shipment statuses, environmental conditions, traffic, and user behaviors. The dataset features multiple stakeholders within the logistics network, including asset IDs, timestamps, traffic conditions, waiting times, and reasons for delays. Additionally, the data is enriched with real-time information from IoT sensors, such as temperature, humidity, and asset utilization, to facilitate advanced logistics optimization and decision-making. The target variable, Logistics_Delay, helps in identifying delays in logistics processes, which is essential for enhancing supply chain efficiency through proactive management and optimization techniques. This dataset is designed to be used for research and machine learning applications focused on smart logistics and supply chain performance improvement.

Key Features: Timestamp: Date and time when the data was recorded, representing logistics activity. Asset_ID: Unique identifier for the logistical assets (e.g., trucks). Latitude & Longitude: Geographical coordinates of the asset for tracking and monitoring. Inventory_Level: Current level of inventory associated with the asset or shipment. Shipment_Status: Status of the shipment (e.g., In Transit, Delivered, Delayed). Temperature: Temperature recorded at the time of the shipment or transportation. Humidity: Humidity level at the time of recording. Traffic_Status: Current traffic condition (e.g., Clear, Heavy, Detour). Waiting_Time: Time spent waiting during the logistics process (in minutes). User_Transaction_Amount: Monetary amount associated with user transactions. User_Purchase_Frequency: Frequency of purchases made by the user. Logistics_Delay_Reason: Reason for any delays in the logistics process (e.g., Weather, Mechanical Failure). Asset_Utilization: Percentage of asset utilization, indicating how effectively assets are being used. Demand_Forecast: Predicted demand for the logistics services in the coming period. Logistics_Delay (Target): Binary variable indicating whether a logistics delay occurred (1 for delay, 0 for no delay).

Global supply routes for Transportation of Food and Non Food Items - Roads, Railways, Waterway, Airways.

This layer is built by linking origin/destination locations using the most direct route on main roads. In reality, the supply routes can divert from the ones displayed here depending on many local factors. The routes shown in this dataset are only indicative and have to be used as such.

This layer contains geodata about global railways

The United States pioneered in transportation and logistics innovation. A 2024 survey shows 38 percent of companies operating in this field used location data and internet-of-things for real-time tracking of their shipments and inventory. In turn, only 31 percent of German companies did the same, whereas UK competitors stood at 26 percent.

This layer contains airports locations. This dataset brings together various public sources such as OpenStreetMap or ourairports.com with WFP logistics information. It is updated regularly with inputs from WFP aviation unit but also from many partners through the Logistics Cluster and the Logistics Capacity Assessment (LCA: dlca.logcluster.org). The information is compiled at a global level by the Emergency and Preparedness Geospatial Information Unit at the World Food Programme (WFP) Headquarters in Rome, Italy.

This dataset is at a global scale and is updated country by country. The last update date can be retrieved from the data of the country of interest.

Feel free to contribute to this dataset by contacting hq.gis@wfp.org.

This layer contains information about bridges

The Data Center Logistics Market size is expected to reach a valuation of USD 917.3 million in 2033 growing at a CAGR of 11.6%. The Data Center Logistics market research report classifies market by share, trend, demand, forecast and based on segmentation.

China Logistics Industry: Logistics Value data was reported at 352,400.000 RMB bn in 2023. This records an increase from the previous number of 347,600.000 RMB bn for 2022. China Logistics Industry: Logistics Value data is updated yearly, averaging 75,228.300 RMB bn from Dec 1991 (Median) to 2023, with 33 observations. The data reached an all-time high of 352,400.000 RMB bn in 2023 and a record low of 3,029.100 RMB bn in 1991. China Logistics Industry: Logistics Value data remains active status in CEIC and is reported by China Federation of Logistics & Purchasing. The data is categorized under China Premium Database’s Transportation and Storage Sector – Table CN.TL: Logistics Value.

Comprehensive dataset containing 1,163 verified Logistics businesses in United States with complete contact information, ratings, reviews, and location data.

Turkey TR: Logistics Performance Index: 1=Low To 5=High: Competence and Quality of Logistics Services data was reported at 3.312 NA in 2016. This records a decrease from the previous number of 3.641 NA for 2014. Turkey TR: Logistics Performance Index: 1=Low To 5=High: Competence and Quality of Logistics Services data is updated yearly, averaging 3.312 NA from Dec 2007 (Median) to 2016, with 5 observations. The data reached an all-time high of 3.641 NA in 2014 and a record low of 3.230 NA in 2010. Turkey TR: Logistics Performance Index: 1=Low To 5=High: Competence and Quality of Logistics Services data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Turkey – Table TR.World Bank.WDI: Transportation. Data are from Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. 2009 round of surveys covered more than 5,000 country assessments by nearly 1,000 international freight forwarders. Respondents evaluate eight markets on six core dimensions on a scale from 1 (worst) to 5 (best). The markets are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Details of the survey methodology are in Arvis and others' Connecting to Compete 2010: Trade Logistics in the Global Economy (2010). Respondents evaluated the overall level of competence and quality of logistics services (e.g. transport operators, customs brokers), on a rating ranging from 1 (very low) to 5 (very high). Scores are averaged across all respondents.; ; World Bank and Turku School of Economics, Logistic Performance Index Surveys. Data are available online at : http://www.worldbank.org/lpi. Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report.; Unweighted average;

Our location data powers the most advanced address validation solutions for enterprise backend and frontend systems.

A global, standardized, self-hosted location dataset containing all administrative divisions, cities, and zip codes for 247 countries.

All geospatial data for address data validation is updated weekly to maintain the highest data quality, including challenging countries such as China, Brazil, Russia, and the United Kingdom.

Use cases for the Address Validation at Zip Code Level Database (Geospatial data)

• Address capture and address validation

• Address autocomplete

• Address verification

• Reporting and Business Intelligence (BI)

• Master Data Mangement

• Logistics and Supply Chain Management

• Sales and Marketing

Product Features

• Dedicated features to deliver best-in-class user experience

• Multi-language support including address names in local and foreign languages

• Comprehensive city definitions across countries

Data export methodology

Our location data packages are offered in variable formats, including .csv. All geospatial data for address validation are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why do companies choose our location databases

• Enterprise-grade service

• Full control over security, speed, and latency

• Reduce integration time and cost by 30%

• Weekly updates for the highest quality

• Seamlessly integrated into your software

Note: Custom address validation packages are available. Please submit a request via the above contact button for more details.

Logistics Data Lake Platform Market Outlook

According to our latest research, the global Logistics Data Lake Platform market size reached USD 1.8 billion in 2024, driven by the demand for scalable, real-time analytics in logistics operations. The market is expected to grow at a robust CAGR of 21.3% from 2025 to 2033, with the forecasted market size projected to reach USD 12.1 billion by 2033. This impressive growth is underpinned by the rapid digital transformation across logistics, increasing volumes of unstructured data, and the need for advanced analytics to optimize supply chain performance and cost efficiency. As per our latest research, logistics organizations worldwide are accelerating investments in data lake platforms to harness big data, enable predictive analytics, and drive operational excellence.

A key growth factor for the Logistics Data Lake Platform market is the exponential rise in data generated from IoT devices, telematics, RFID sensors, and connected supply chain assets. Logistics providers are increasingly leveraging these data sources to gain real-time visibility into shipments, inventory, and transportation networks. Data lake platforms empower these organizations to ingest, store, and process vast volumes of structured and unstructured data, which traditional data warehouses cannot handle efficiently. This capability is essential for advanced analytics, such as route optimization, demand forecasting, and anomaly detection, which drive both cost savings and improved customer satisfaction. As logistics companies strive for digital maturity, the adoption of data lake technologies is becoming a strategic imperative.

Another significant driver is the integration of artificial intelligence (AI) and machine learning (ML) with logistics data lake platforms. The convergence of AI/ML with big data enables predictive maintenance of fleet assets, dynamic pricing, and real-time supply chain risk management. Leading logistics enterprises are utilizing these platforms to automate and optimize complex workflows, from inventory management to last-mile delivery. The ability to unify disparate data sources and apply AI-driven insights is transforming decision-making processes, reducing manual intervention, and enhancing agility in responding to market disruptions. As the logistics sector faces increasing pressure to deliver faster, cheaper, and more reliable services, the role of data lake platforms in enabling intelligent automation is poised for rapid expansion.

Furthermore, regulatory compliance and data governance requirements are fueling the adoption of logistics data lake platforms. With the proliferation of data privacy regulations such as GDPR and CCPA, logistics companies must ensure secure, auditable, and compliant data management. Data lake platforms provide centralized control, data lineage tracking, and robust security features to meet these regulatory demands. Additionally, the growing need for seamless collaboration across global supply chain partners necessitates scalable data sharing and integration capabilities, which data lake solutions are uniquely positioned to deliver. As logistics networks become more interconnected and data-driven, robust data governance is emerging as a critical differentiator for industry leaders.

From a regional perspective, North America currently leads the Logistics Data Lake Platform market due to the early adoption of digital technologies by major logistics and e-commerce players. Europe follows closely, driven by stringent data regulations and a strong focus on supply chain innovation. The Asia Pacific region is witnessing the fastest growth, fueled by expanding e-commerce, infrastructure development, and government initiatives to modernize logistics. Latin America and the Middle East & Africa are also showing promising adoption trends, supported by investments in smart logistics and digital transformation projects. Regional dynamics are shaped by factors such as technology readiness, regulatory environments, and the maturity of logistics ecosystems, influencing the pace and scale of data lake platform adoption worldwide.

Component Analysis

The Component segment of the Logistics Data Lake Platform market is broadly categorized into Software and Services. The software component comprises core data lake platform solutions, including data ingestion, storage, processing, analytics, and visualizati

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Logistics Data Lake Platform Market Outlook

According to our latest research, the global logistics data lake platform market size reached USD 3.85 billion in 2024, with a robust year-on-year growth rate and a strong momentum driven by digital transformation across the logistics sector. The market is projected to grow at a CAGR of 21.7% from 2025 to 2033, reaching a forecasted value of USD 28.4 billion by 2033. This remarkable growth is primarily attributed to the increasing need for real-time data analytics, the proliferation of IoT devices in logistics operations, and the growing adoption of cloud-based solutions. As per our latest research, the logistics data lake platform market is witnessing significant investments from both established logistics companies and technology providers, aiming to streamline operations, enhance supply chain transparency, and enable predictive analytics capabilities.

One of the primary growth factors for the logistics data lake platform market is the exponential rise in data volumes generated by logistics operations worldwide. The integration of IoT sensors, GPS tracking, RFID tags, and telematics devices within transportation fleets and warehouses has created vast streams of structured and unstructured data. Logistics companies are increasingly turning to data lake platforms to store, manage, and analyze this data in real time, facilitating better decision-making and operational efficiency. Furthermore, the surge in e-commerce activities and the need for rapid, reliable deliveries have compelled organizations to invest in advanced analytics platforms, which are best supported by scalable data lake architectures. The ability to consolidate disparate data sources and derive actionable insights is proving indispensable for companies striving to optimize routes, reduce costs, and improve customer satisfaction.

Another significant driver is the shift towards digital supply chain management, which necessitates agile, scalable, and secure data management solutions. Traditional data warehousing approaches are often inadequate in handling the volume, velocity, and variety of data in modern logistics ecosystems. Data lake platforms offer a flexible environment for ingesting, processing, and analyzing diverse data types, including sensor data, transactional records, and external market intelligence. This enables logistics providers to implement predictive maintenance, demand forecasting, and inventory optimization strategies more effectively. The increasing availability of AI and machine learning tools further amplifies the value of data lakes, as these technologies require vast, high-quality datasets for training and inference. As a result, organizations are leveraging data lakes to gain a competitive edge through advanced analytics and automation.

The ongoing trend of cloud adoption across the logistics industry is also propelling the logistics data lake platform market forward. Cloud-based data lake solutions offer unparalleled scalability, cost-efficiency, and accessibility, making them particularly attractive for both large enterprises and small and medium-sized businesses. These platforms facilitate seamless integration with other cloud-native applications, enabling organizations to build comprehensive digital logistics ecosystems. Additionally, cloud deployment mitigates the need for significant upfront investments in IT infrastructure, allowing companies to scale their data management capabilities in line with business growth. The increasing focus on regulatory compliance and data security is prompting vendors to enhance the security features of their cloud-based data lake offerings, further boosting adoption rates across regions.

From a regional perspective, North America currently leads the logistics data lake platform market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The presence of major logistics hubs, advanced IT infrastructure, and a strong emphasis on innovation have positioned these regions at the forefront of market growth. Meanwhile, the Asia Pacific region is expected to witness the highest CAGR during the forecast period, driven by rapid urbanization, expanding e-commerce markets, and significant investments in digital logistics solutions. Latin America and the Middle East & Africa are also experiencing steady growth, supported by increasing digitalization efforts and the modernization of supply chain networks. Overall, the global outlook for the logistics data lake platform market remains

France FR: Logistics Performance Index: 1=Low To 5=High: Overall data was reported at 3.901 NA in 2016. This records an increase from the previous number of 3.847 NA for 2014. France FR: Logistics Performance Index: 1=Low To 5=High: Overall data is updated yearly, averaging 3.847 NA from Dec 2007 (Median) to 2016, with 5 observations. The data reached an all-time high of 3.901 NA in 2016 and a record low of 3.760 NA in 2007. France FR: Logistics Performance Index: 1=Low To 5=High: Overall data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s France – Table FR.World Bank.WDI: Transportation. Logistics Performance Index overall score reflects perceptions of a country's logistics based on efficiency of customs clearance process, quality of trade- and transport-related infrastructure, ease of arranging competitively priced shipments, quality of logistics services, ability to track and trace consignments, and frequency with which shipments reach the consignee within the scheduled time. The index ranges from 1 to 5, with a higher score representing better performance. Data are from Logistics Performance Index surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. 2009 round of surveys covered more than 5,000 country assessments by nearly 1,000 international freight forwarders. Respondents evaluate eight markets on six core dimensions on a scale from 1 (worst) to 5 (best). The markets are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. Scores for the six areas are averaged across all respondents and aggregated to a single score using principal components analysis. Details of the survey methodology and index construction methodology are in Arvis and others' Connecting to Compete 2010: Trade Logistics in the Global Economy (2010).; ; World Bank and Turku School of Economics, Logistic Performance Index Surveys. Data are available online at : http://www.worldbank.org/lpi. Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report.; Unweighted average;

Comprehensive dataset containing 20 verified Logistics businesses in New York, United States with complete contact information, ratings, reviews, and location data.

This vast repository houses crucial information on international trade transactions, capturing the intricate details of both export and import activities of Zimbabwe. The Export Database contains meticulous records of outbound shipments, offering valuable insights into the products, exporters, and destinations involved in each transaction. On the other hand, the Import Database provides a comprehensive view of inbound shipments, shedding light on the importers, origins, and details of the products acquired. Together, these two databases present a holistic perspective on global trade dynamics, encompassing critical metadata such as dates, product descriptions, quantities, values, and transportation specifics. Whether you are an analyst, researcher, or business professional, this comprehensive database will undoubtedly prove to be an invaluable resource for gaining a deep understanding of international trade patterns and market dynamics. Explore the wealth of information within and unlock new opportunities in the world of trade and commerce.

The Export Database contains information related to export transactions. Each entry in the database represents a specific export event. The metadata fields in this database hold crucial details about the exported products and the transaction itself. The "DATE" field indicates the date of the export. "EXPORTER NAME" refers to the name of the entity or company responsible for exporting the goods. "DESTINATION COUNTRY" indicates the country to which the products are being shipped. The "HS CODE" represents the Harmonized System code, a standardized numerical system used to classify traded products. The "PRODUCT DESCRIPTION" field provides a brief description of the exported item. The "BRAND" field specifies the brand associated with the product. "QUANTITY" indicates the total quantity of the product being exported, and "UNIT OF QUANTITY" represents the measurement unit used for quantity. "SUBITEM QUANTITY" refers to the quantity of a subitem within the main exported product. The "PACKAGES" field indicates the number of packages used for shipment. "GROSS WEIGHT" represents the total weight of the exported products. "SUBITEM FOB VALUE" and "TOTAL FOB VALUE" denote the Free on Board (FOB) value of the subitem and the total FOB value of the export, respectively. "TOTAL CIF VALUE" indicates the total cost, insurance, and freight value. "ITEM NUMBER" is a unique identifier for each product item. "TRANSPORT TYPE" specifies the mode of transportation used for the export. "INCOTERMS" refers to the standardized international trade terms defining the responsibilities of buyers and sellers during transportation. "CUSTOMS" indicates the customs information related to the export. "VARIETY" and "ATTRIBUTES" hold additional details about the product. The "OPERATION TYPE" field indicates the type of export operation, such as direct export or re-export. "MONTH" and "YEAR" represent the month and year when the export occurred.

The Import Database contains information related to import transactions. Each entry in the database represents a specific import event. The metadata fields in this database hold crucial details about the imported products and the transaction itself. The "DATE" field indicates the date of the import. "IMPORTER NAME" refers to the name of the entity or company responsible for importing the goods. "SALES COUNTRY" indicates the country from which the products are being purchased. "ORIGIN COUNTRY" denotes the country where the imported products originate. The "HS CODE" represents the Harmonized System code, a standardized numerical system used to classify traded products. The "PRODUCT DESCRIPTION" field provides a brief description of the imported item. "QUANTITY" indicates the total quantity of the product being imported, and "UNIT OF QUANTITY" represents the measurement unit used for quantity. "SUBITEM QUANTITY" refers to the quantity of a subitem within the main imported product. The "PACKAGES" field indicates the number of packages used for shipment. "GROSS WEIGHT" represents the total weight of the imported products. "TOTAL CIF VALUE" indicates the total cost, insurance, and freight value. "TOTAL FREIGHT VALUE" and "TOTAL INSURANCE VALUE" represent the respective values for freight and insurance. "ITEM FOB VALUE," "SUBITEM FOB VALUE," and "ITEM CIF VALUE" denote the Free on Board (FOB) value of the item, subitem, and the cost, insurance, and freight value of the item, respectively. "ORIGIN PORT" specifies the port from which the products were shipped. "TRANSPORT TYPE" specifies the mode of transportation used for the import. "INCOTERMS" refers to the standardized international trade terms defining the responsibilities of buyers and sellers during transportation. "ITEM NUMBER" is a unique identifier for each product item. "CUSTOMS" indicates the customs information related to the import. "OPERATION TYPE" field indicates the type of import operation, such as direct...