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.

Objectives:

• I leveraged advanced data visualization techniques to extract valuable insights from a comprehensive dataset. By visualizing sales patterns, customer behavior, and product trends, I identified key growth opportunities and provided actionable recommendations to optimize business strategies and enhance overall performance. you can find the GitHub repo here Link to GitHub Repository.

Data Description:

there are exactly 6 table and 1 is a fact table and the rest of them are dimension tables: Fact Table:

payment_key:
  Description: An identifier representing the payment transaction associated with the fact.
  Use Case: This key links to a payment dimension table, providing details about the payment method and related information.

customer_key:
  Description: An identifier representing the customer associated with the fact.
  Use Case: This key links to a customer dimension table, providing details about the customer, such as name, address, and other customer-specific information.

time_key:
  Description: An identifier representing the time dimension associated with the fact.
  Use Case: This key links to a time dimension table, providing details about the time of the transaction, such as date, day of the week, and month.

item_key:
  Description: An identifier representing the item or product associated with the fact.
  Use Case: This key links to an item dimension table, providing details about the product, such as category, sub-category, and product name.

store_key:
  Description: An identifier representing the store or location associated with the fact.
  Use Case: This key links to a store dimension table, providing details about the store, such as location, store name, and other store-specific information.

quantity:
  Description: The quantity of items sold or involved in the transaction.
  Use Case: Represents the amount or number of items associated with the transaction.

unit:
  Description: The unit or measurement associated with the quantity (e.g., pieces, kilograms).
  Use Case: Specifies the unit of measurement for the quantity.

unit_price:
  Description: The price per unit of the item.
  Use Case: Represents the cost or price associated with each unit of the item.

total_price:
  Description: The total price of the transaction, calculated as the product of quantity and unit price.
  Use Case: Represents the overall cost or revenue generated by the transaction.

Customer Table: customer_key:

Description: An identifier representing a unique customer.
Use Case: Serves as the primary key to link with the fact table, allowing for easy and efficient retrieval of customer-specific information.

name:

Description: The name of the customer.
Use Case: Captures the personal or business name of the customer for identification and reference purposes.

contact_no:

Description: The contact number associated with the customer.
Use Case: Stores the phone number or contact details for communication or outreach purposes.

nid:

Description: The National ID (NID) or a unique identification number for the customer.

Item Table: item_key:

Description: An identifier representing a unique item or product.
Use Case: Serves as the primary key to link with the fact table, enabling retrieval of detailed information about specific items in transactions.

item_name:

Description: The name or title of the item.
Use Case: Captures the descriptive name of the item, providing a recognizable label for the product.

desc:

Description: A description of the item.
Use Case: Contains additional details about the item, such as features, specifications, or any relevant information.

unit_price:

Description: The price per unit of the item.
Use Case: Represents the cost or price associated with each unit of the item.

man_country:

Description: The country where the item is manufactured.
Use Case: Captures the origin or manufacturing location of the item.

supplier:

Description: The supplier or vendor providing the item.
Use Case: Stores the name or identifier of the supplier, facilitating tracking of item sources.

unit:

Description: The unit of measurement associated with the item (e.g., pieces, kilograms).

Store Table: store_key:

Description: An identifier representing a unique store or location.
Use Case: Serves as the primary key to link with the fact table, allowing for easy retrieval of information about transactions associated with specific stores.

division:

Description: The administrative division or region where the store is located.
Use Case: Captures the broader geographical area in which...

Marketing Data Warehouse Market Outlook

According to our latest research, the global Marketing Data Warehouse market size reached USD 6.9 billion in 2024, reflecting the sectorÂ’s robust expansion and surging demand for data-driven marketing solutions. The market is projected to register a CAGR of 13.8% during the forecast period, propelling the market to a substantial USD 21.3 billion by 2033. This impressive growth is primarily driven by enterprisesÂ’ increasing focus on leveraging advanced analytics, AI-powered insights, and real-time data integration to optimize marketing strategies and enhance customer engagement.

The proliferation of digital channels and the exponential growth of data generated from various marketing touchpoints are pivotal growth drivers for the Marketing Data Warehouse market. Organizations are increasingly recognizing the value of centralized data repositories to unify disparate marketing data streams, enabling holistic customer views and more precise segmentation. This centralization is essential for extracting actionable insights from vast volumes of structured and unstructured data, which in turn empowers marketers to tailor campaigns, improve personalization, and maximize ROI. The adoption of cloud-based data warehouse solutions is further accelerating this trend, as businesses seek scalable and cost-effective platforms to manage their ever-growing datasets.

Another significant growth factor is the rapid advancement in analytics technologies and the integration of artificial intelligence and machine learning into marketing data warehouses. These technological enhancements facilitate advanced capabilities such as predictive analytics, real-time reporting, and automated campaign optimization. As a result, organizations can anticipate customer behaviors, refine targeting, and deliver highly relevant content at optimal times. The increasing emphasis on data privacy and compliance is also prompting enterprises to invest in sophisticated data governance frameworks within their data warehouse environments, ensuring secure and compliant data management while maintaining analytical agility.

The evolving landscape of customer expectations and the competitive drive for hyper-personalization are compelling organizations across industries to invest heavily in marketing data warehouse solutions. Retail & e-commerce, BFSI, healthcare, and media & entertainment sectors are particularly proactive, leveraging these platforms to gain a competitive edge through enhanced customer analytics and campaign management. Furthermore, the rise of omnichannel marketing strategies and the need for seamless data integration across various platforms are pushing businesses to adopt robust data warehouse architectures. This trend is especially pronounced among large enterprises, though small and medium enterprises are rapidly catching up, aided by the democratization of cloud-based data warehousing solutions.

Data Warehousing plays a crucial role in the marketing landscape by serving as the backbone for storing and managing vast amounts of marketing data. As businesses increasingly rely on data-driven strategies, the ability to efficiently consolidate, store, and retrieve data becomes paramount. Data Warehousing solutions provide organizations with the infrastructure needed to handle large datasets, ensuring that data is accessible and actionable. This capability is essential for executing complex marketing campaigns, analyzing customer behavior, and making informed decisions. By leveraging advanced data warehousing technologies, companies can enhance their marketing efforts, improve customer targeting, and ultimately drive better business outcomes.

Regionally, North America continues to dominate the Marketing Data Warehouse market, underpinned by the presence of major technology vendors, early adoption of advanced analytics, and a mature digital marketing ecosystem. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid digital transformation, expanding e-commerce activities, and increasing investments in marketing technology infrastructure. Europe is also witnessing steady growth, driven by stringent data regulations and the widespread adoption of data-driven marketing practices. Latin America and the Middle East & Africa are gradually gaining momentum, supporte

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.

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Success.ai’s Ecommerce Store Data for the APAC E-commerce Sector provides a reliable and accurate dataset tailored for businesses aiming to connect with e-commerce professionals and organizations across the Asia-Pacific region. Covering roles and businesses involved in online retail, marketplace management, logistics, and digital commerce, this dataset includes verified business profiles, decision-maker contact details, and actionable insights.

With access to continuously updated, AI-validated data and over 700 million global profiles, Success.ai ensures your outreach, market analysis, and partnership strategies are effective and data-driven. Backed by our Best Price Guarantee, this solution helps you excel in one of the world’s fastest-growing e-commerce markets.

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Data Highlights:

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Key Features of the Dataset:

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Strategic Use Cases:

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   • Analyze regional e-commerce trends, consumer preferences, and logistics challenges to refine product offerings and business strategies.
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The Data Warehouse as a Service (DWaaS) market is experiencing robust growth, projected to reach $4.97 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 22.60% from 2025 to 2033. This expansion is driven by several key factors. The increasing adoption of cloud computing across various industries, particularly BFSI, Government, Healthcare, and E-commerce, fuels the demand for scalable and cost-effective data warehousing solutions. Businesses are increasingly recognizing the value of data-driven decision-making, leading to a greater need for efficient data storage, processing, and analysis capabilities that DWaaS offers. Furthermore, the rise of big data and the need for real-time analytics are pushing organizations towards cloud-based solutions that provide the necessary scalability and flexibility. The competitive landscape includes major players like Amazon Web Services, Microsoft, Google, and Oracle, fostering innovation and driving down costs, making DWaaS accessible to even SMEs. However, challenges such as data security concerns and the complexities of migrating existing data warehouses to the cloud might impede market growth to some extent. The segment breakdown reveals a strong contribution from large enterprises, reflecting their higher data volumes and analytical needs. However, the SME segment is expected to witness significant growth, driven by increased cloud adoption and the availability of affordable DWaaS solutions. Geographically, North America currently holds a substantial market share, benefiting from early adoption and the presence of major technology companies. However, the Asia-Pacific region, particularly China and India, is poised for rapid expansion due to its growing digital economy and increasing investment in cloud infrastructure. The continued development of advanced analytics capabilities within DWaaS platforms, along with the integration of artificial intelligence and machine learning, will further propel market growth in the coming years. This suggests a bright future for the DWaaS market, fueled by technological advancements and the growing need for data-driven insights across industries worldwide. Recent developments include: May 2022 - Dell partnered with Snowflake Inc to ease access to on-premises data. The partnership between Snowflake Inc. and Dell Technologies brings Snowflake Data Cloud's tools to on-premises object storage., January 2022 - Firebolt, a data warehouse startup, raised USD100 million at a USD1.4 billion valuation to provide quicker, cheaper analytics on massive data sets. It intended to utilize the funds to continue investing in its technological stack, increase business development, and add more expertise to its team to meet the data warehousing market.. Key drivers for this market are: Rapid Adoption of Cloud-based Solutions and Focus on Real-time Data Analysis, Rising use of Data Warehouse services in BFSI sector to drive the market.; Data analytics and business intelligence are expected to play a major role in enterprise management.. Potential restraints include: Rapid Adoption of Cloud-based Solutions and Focus on Real-time Data Analysis, Rising use of Data Warehouse services in BFSI sector to drive the market.; Data analytics and business intelligence are expected to play a major role in enterprise management.. Notable trends are: Rising use of Data Warehouse services in BFSI sector to drive the market..

This dataset integrates e-commerce product demand with logistics fulfillment and shipping timelines, providing a unified view of inventory levels, order status, and shipment tracking across warehouses and suppliers. It enables detailed supply chain analysis, demand forecasting, and operational optimization for e-commerce logistics teams. The flat structure supports easy integration with analytics tools and supply chain management systems.

Snapshot img

Data Warehouse As A Service Market Size 2024-2028

The data warehouse as a service market size is forecast to increase by USD 12.32 billion at a CAGR of 24.49% between 2023 and 2028.

The market is experiencing significant growth due to several key trends. One major trend is the shift from traditional on-premises data warehouses to cloud-based DWaaS solutions. Advanced storage technologies, such as columnar databases, in-memory storage, and cloud storage, are also driving market growth. 
However, data privacy and security risks are challenges that need to be addressed, as organizations move their data to the cloud. DWaaS providers are responding by implementing data security and data encryption techniques to mitigate these risks. Overall, the DWaaS market is poised for continued growth as more businesses seek to leverage the benefits of cloud-based data warehousing solutions.

What will be the Size of the Data Warehouse As A Service Market During the Forecast Period?

Request Free Sample

The market represents a significant shift in how businesses manage their data environments. DWaaS offers flexibility and scalability, enabling organizations to focus on their core competencies while leveraging cloud computing for their data warehousing needs. This market is driven by the increasing demand for Business Intelligence (BI) that can handle large data volumes and provide advanced analytics capabilities. 
Technological developments in cloud computing, software, computing, and storage have made DWaaS a viable alternative to traditional on-premises data warehouses. However, the adoption of DWaaS is not without challenges. Security issues and integration complexities are key concerns for businesses considering a move to the cloud.
Restricted customization is another challenge, as some organizations require specific configurations for their data warehouses. Despite these challenges, the benefits of DWaaS, such as reduced IT infrastructure complexity and improved data accessibility, continue to drive market growth. The DWaaS market is expected to expand as more businesses seek to harness the power of their data for enterprise management, visualization, and data analytics.

How is this Data Warehouse As A Service Industry segmented and which is the largest segment?

The DWaaS industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  BFSI
  Government
  Healthcare
  E-commerce and retail
  Others


Type

  Enterprise DWaaS
  Operational data storage


Geography

  North America

    US


  Europe

    Germany
    France


  APAC

    China
    Japan


  Middle East and Africa



  South America

By End-user Insights

The BFSI segment is estimated to witness significant growth during the forecast period.

The BFSI sector's reliance on managing and analyzing large financial data volumes has fueled the adoption of Data Warehouse as a Service (DWaaS) solutions. DWaaS offers flexibility and scalability, enabling BFSI companies to efficiently manage data from retail banking institutions, lending operations, credit underwriting procedures, and financial consulting firms. DWaaS solutions provide core competencies in cloud computing, business intelligence (BI), data analytics, enterprise management, visualization, and BI solutions. Technological developments, such as IoT technology and AI technology, further enhance DWaaS capabilities. However, challenges persist, including security issues, integration challenges, and restricted customization. Cloud solutions, including cloud data warehouses, offer a data environment that is software, computing, and storage-intensive.

DWaaS companies address concerns with service disruptions, latency, data integration, and data access. Security measures, such as data encryption and data masking, ensure data privacy. Despite these challenges, DWaaS adoption continues to grow, offering decision support services, data categorization, and data assessment to mid-size businesses and large enterprises.

Get a glance at the Data Warehouse As A Service Industry report of share of various segments Request Free Sample

The BFSI segment was valued at USD 665.10 million in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 35% to the growth of the global market during the forecast period.

Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request Free Sample

The North American market for Data Warehouse as a Service (DWaaS) is experiencing significant growth due to the region's early adoption of advanced techn

About

This data is from E-Commerce. I used postgreSQL for data cleaning. I transformed NULL values to 'Not defined' and orginal data have only category name column(which was 'category_code') and that was 'DOT' seperated value which show us the products class from wide to specific. So I split them with delimeter('.').

The orignal data have record with 5 months but I only used December of 2019. If you want more data you can visit the link above and use.

File structure

Acknowledgements

Many thanks Thanks to REES46 Marketing Platform for this dataset and Michael Kechinov

Using datasets in your works, books, education materials

You can use this dataset for free. Just mention the source of it: link to this page and link to REES46 Marketing Platform and Origin data provider

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

We have millions of eCommerce data ready to go no matter where you are. We’ve acquired hundreds of clients from all over the world over the years and delivered data that they’re happy with.

Our team sources, validates, and lists Product Data based on requirements and requested data attributes. We track global and local Online Marketplaces, eCommerce Platforms, Social Media Platforms and Online Stores to deliver relevant information about product pricing, and its positioning on the market.

Our team extracts, validates, and delivers consumer and product data based on provided requirements and data fields. Sources: Amazon, Walmart, eBay, and others. Exemplary categories: Household Products, Beauty, Fashion, Food, Beverages, Pets, Electronics. Main markets: US, UK, Australia

Unlock the door to business expansion by investing in our real-time eCommerce leads list. Gain direct access to store owners and make informed decisions with data fields including 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.

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• 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

CONTEXT

E-commerce (electronic commerce) is the buying and selling of goods and services, or the transmitting of funds or data, over an electronic network, primarily the internet. These business transactions occur either as business-to-business (B2B), business-to-consumer (B2C), consumer-to-consumer or consumer-to-business

CONTENT

This is simple data set of US online_store from 2020.

INSPIRATION

So, the data cames with some questions !!

What was the highest Sale in 2020? What is average discount rate of charis? What are the highest selling months in 2020? What is the Profit Margin for each sales record? How much profit is gained for each product? What is the total Profit & Sales by Sub-Category? People from city/state shop the most? Develop a function, to return a dataframe which is grouped by a particular column (as an input)

If you have wonderful idea about this dataset, welcome to contribute !!! Happy Kaggling, please up-vote if you find this dataset helpful!🖤!

Context

In the field of e-commerce, the datasets are typically considered as proprietary, meaning they are owned and controlled by individual organizations and are not often made publicly available due to privacy and business considerations. In spite of this, The UCI Machine Learning Repository, known for its extensive collection of datasets beneficial for machine learning and data mining research, has curated and made accessible a unique dataset. This dataset comprises actual transactional data spanning from the year 2010 to 2011. For those interested, the dataset is maintained and readily available on the UCI Machine Learning Repository's site under the title "Online Retail".

Content

The dataset is a transnational one, capturing every transaction made from December 1, 2010, through December 9, 2011, by a UK-based non-store online retail company. As an online retail entity, the company doesn't have a physical store presence, and its operations and sales are conducted purely online. The company's primary product offering includes unique gifts for all occasions. While the company serves a diverse range of customers, a significant number of its clientele includes wholesalers.

Acknowledgements

In collaboration with the UCI Machine Learning Repository, the dataset was provided and made available by Dr. Daqing Chen. Dr. Chen is the Director of the Public Analytics group at London South Bank University, UK. Any correspondence regarding this dataset can be sent to Dr. Chen at 'chend' at 'lsbu.ac.uk'. We are grateful to him for providing such an invaluable resource for researchers and data science enthusiasts.

The image used has been sourced from Canva

Inspiration

The rich and extensive data within this dataset opens the door for a multitude of potential analyses. It lends itself well to various methods and techniques in data science, including but not limited to time series analysis, clustering, and classification. By exploring this dataset, one could derive key insights into customer behavior, transaction trends, and product performance, providing ample opportunities for deep and insightful explorations.

Graph and download economic data for E-Commerce Revenue for Warehousing and Storage, All Establishments, Employer Firms (DISCONTINUED) (ECREF493ALLEST) from 2006 to 2012 about e-commerce, warehousing, employer firms, revenue, establishments, services, and USA.

Discover the booming Analytical Data Store Software market! This comprehensive analysis reveals key trends, growth drivers, and leading players shaping this multi-billion dollar industry from 2019-2033, including insights into cloud adoption, regional growth, and emerging technologies like AI. Learn more about market segmentation and forecast predictions.

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"

Ecommerce Warehouse Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Global Data Warehouse as a Service (DWaaS) Market valued at USD 5.03 Billion in 2023 and is predicted to USD 30.37 Billion by 2032, with a CAGR of 22.1%.

Active Data Warehousing Market Overview The global active data warehousing market held a valuation of USD 5,467.1 million in 2025 and is anticipated to register a CAGR of 6.3% during the forecast period from 2025 to 2033. This growth is attributed to the increasing adoption of data-driven decision-making, the rise of big data, and the need for real-time data analysis. Key market drivers include the growth of e-commerce, the adoption of cloud-based data warehouses, and the increasing use of artificial intelligence and machine learning. Segmentation and Competitive Landscape The market is segmented based on type (cloud, on-premises), application (large enterprises, small and medium-sized enterprises), and region (North America, South America, Europe, Middle East & Africa, Asia Pacific). Major players in the market include Teradata, IBM, Microsoft, HP, Oracle, Cloudera, Kognitio, Greenplum, Sybase, and others. These companies are investing in research and development to enhance their offerings and gain market share. The market is fragmented, with several players competing on the basis of innovation, pricing, and customer service. Global Market Value: USD 10 billion (2023) Analyst Coverage: Grand View Research Report Link: Active Data Warehousing Market Report

As per Cognitive Market Research's latest published report, the Global E-commerce Logistics market size will be $1,864.6 Billion by 2030. E-commerce Logistics Industry's Compound Annual Growth Rate will be 19.4% from 2023 to 2030. Market Dynamics of E Commerce Logistics Market

Key Drivers for E Commerce Logistics Market

Rapid Expansion of Online Shopping and Digital Marketplaces: The increase in global e-commerce transactions has heightened the necessity for effective, rapid, and scalable logistics solutions to meet growing customer expectations.

Growth of Cross-Border and Global E-Commerce: Online retailers are progressively focusing on international markets, which is driving the demand for strong international shipping, customs clearance, and last-mile delivery systems.

Technological Innovations in Warehouse and Fleet Management: Automation, robotics, GPS tracking, and route optimization technologies are improving operational efficiency and decreasing delivery turnaround times.

Consumer Demand for Rapid and Same-Day Delivery Services: The rising popularity of same-day and next-day delivery options is compelling logistics providers to enhance fulfillment centers and urban distribution networks.

Key Restraints for E Commerce Logistics Market

High Last-Mile Delivery Expenses and Complexities: The final stage of delivery is costly and logistically intricate, particularly in rural or congested urban regions, affecting overall profitability.

Infrastructure and Traffic Issues in Developing Markets: Subpar road conditions, absence of digital address systems, and unreliable postal services obstruct seamless e-commerce logistics in emerging markets.

Challenges in Reverse Logistics and Return Management: Handling elevated return volumes—particularly for fashion and electronics—introduces additional costs and complexities to logistics operations.

Environmental Issues and Carbon Emissions: The rise in delivery vehicle traffic and packaging waste presents sustainability challenges, leading to demands for more eco-friendly logistics practices.

Key Trends for E Commerce Logistics Market

Expansion of Micro-Fulfillment Centers in Urban Locations: Retailers and logistics companies are establishing small, automated warehouses in urban areas to accelerate order processing and minimize delivery times.

Integration of AI and Data Analytics in Logistics Strategy: Machine learning and real-time data tools are enhancing inventory placement, delivery routing, and demand forecasting in e-commerce logistics.

Rise of Third-Party Logistics (3PL) and Fulfillment-as-a-Service Models: E-commerce companies are increasingly outsourcing logistics to specialized providers to scale quickly and reduce capital expenditure.

Focus on Sustainable and Eco-Friendly Delivery Methods: Companies are exploring electric vehicles, bicycle couriers, recyclable packaging, and carbon-neutral initiatives to align with green consumer values. Definition of E-commerce logistics:

E-commerce logistics is well-defined as the supply chain through which a company's online customer orders are fulfilled. This is the process from the point of manufacture until the product is delivered to the consumer-commerce logistics include providing warehousing, transportation, value-added services, packaging, and other services. The development of digital technology led to a surge in the demand for several applications in the e-commerce logistics market.