Cloud Data Warehouse Market is Segmented by Offering (Data Warehouse-As-A-Service (DWaaS), Cloud-Native Storage, and More), Deployment (Public Cloud and Private Cloud), End-User Enterprise Size (Large Enterprises, and Small and Medium Enterprises), End-Use Industry (BFSI, IT and Telecom, and More), and Geography (North America, South America, Europe, and More). The Market Forecasts are Provided in Terms of Value (USD).

The data warehouse as a service market soars from USD 8.27 billion in 2024 to reach game-changing USD 64.05 billion by 2034, at an explosive CAGR of 22.8% with cloud-native analytics.

The global Cloud Data Warehouse market size is expected to reach USD 43.55 Billion in 2032 registering a CAGR of 22.3%. Discover the latest trends and analysis on the Cloud Data Warehouse Market. Our report provides a comprehensive overview of the industry, including key players, market share, growt...

The Data Warehousing Market report segments the industry into By Type Of Deployment (On-Premise, Cloud, Hybrid), By Size Of Enterprise (Small And Medium-Sized Enterprises, Large Enterprises), By Industry Vertical (BFSI, Manufacturing, Healthcare, Retail, Other Industry Verticals), and Geography (North America, Europe, Asia-Pacific, Rest Of The World). Get five years of historical data and five-year market forecasts.

MedSynora DW – A Comprehensive Synthetic Hospital Patient Data Warehouse

Overview MedSynora DW is a huge synthetic dataset designed to simulate the operation flow by adopting a patient-based approach in a large hospital. This dataset covers patient encounters, treatments, lab tests, vital signs, cost details and more over a full year of 2024. It is developed to support data science, machine learning, and business intelligence projects in the healthcare domain.

Project Highlights • Realistic Simulation: Generated using advanced Python scripts and statistical models, the dataset reflects realistic hospital operations and patient flows without using any real patient data. • Comprehensive Schema: The data warehouse includes multiple fact and dimension tables: o Fact Tables: Encounter, Treatment, Lab Tests, Special Tests, Vitals, and Cost. o Dimension Tables: Patient, Doctor, Disease, Insurance, Room, Date, Chronic Diseases, Allergies, and Additional Services. o Bridge Tables: For managing many-to-many relationships (e.g., doctors per encounter) and some other… • Synthetic & Scalable: The dataset is entirely synthetic, ensuring privacy and compliance. It is designed to be scalable – the current version simulates around 145,000 encounter records.

Data Generation • Data Sources & Methods: Data is generated using bunch of Py libraries. Highly customized algorithms simulate realistic patient demographics, doctor assignments, treatment choices, lab test results, and cost breakdowns etc.. • Diverse Scenarios: With over 300 diseases and thousands of treatment variations, along with dozens of lab and special tests, the dataset offers profoundly rich variability to support complex analytical projects.

How to Use This Dataset • For Data Modeling & ETL Testing: Import the CSV files into your favorite database system (e.g., PostgreSQL, MySQL, or directly into a BI tool like Power BI) and set up relationships as described in the accompanying documentation. • For Machine Learning Projects: Use the dataset to build predictive models related to patient outcomes, cost analysis, or treatment efficacy. • For Educational Purposes: Ideal for learning about data warehousing, star schema design, and advanced analytics in healthcare.

Final Note MedSynora DW offers a unique opportunity to experiment with a comprehensive, realistic hospital data warehouse without compromising real patient information. Enjoy exploring, analyzing, and building with this dataset – and feel free to reach out if you have any questions or suggestions. In particular, inconsistencies, deficiencies or suggestions about the dataset by experts in the field will contribute to other version improvements.

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Enterprise Data Warehouse (EDW) Market Size 2025-2029

The enterprise data warehouse (edw) market size is valued to increase USD 43.12 billion, at a CAGR of 28% from 2024 to 2029. Data explosion across industries will drive the enterprise data warehouse (edw) market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 32% growth during the forecast period.
By Product Type - Information and analytical processing segment was valued at USD 4.38 billion in 2023
By Deployment - Cloud based segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 857.82 million
Market Future Opportunities: USD 43116.60 million
CAGR : 28%
APAC: Largest market in 2023

Market Summary

The market is a dynamic and ever-evolving landscape, characterized by continuous innovation and adaptation to industry demands. Core technologies, such as cloud computing and big data analytics, are driving the market's growth, enabling organizations to manage and analyze vast amounts of data more effectively. In terms of applications, business intelligence and data mining are leading the way, providing valuable insights for strategic decision-making. Service types, including consulting, implementation, and support, are essential components of the EDW market. According to recent reports, the consulting segment is expected to dominate the market due to the increasing demand for expert advice in implementing and optimizing EDW solutions. However, data security concerns remain a significant challenge, with regulations like GDPR and HIPAA driving the need for robust security measures. Despite these challenges, the market continues to expand, with data explosion across industries fueling the demand for EDW solutions. For instance, the healthcare sector is projected to witness a compound annual growth rate (CAGR) of 15.3% between 2021 and 2028. Furthermore, the market is witnessing a significant focus on new solution launches, with major players like Microsoft, IBM, and Oracle introducing advanced EDW offerings to meet the evolving needs of businesses.

What will be the Size of the Enterprise Data Warehouse (EDW) Market during the forecast period?

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How is the Enterprise Data Warehouse (EDW) Market Segmented and what are the key trends of market segmentation?

The enterprise data warehouse (edw) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. Product TypeInformation and analytical processingData miningDeploymentCloud basedOn-premisesSectorLarge enterprisesSMEsEnd-userBFSIHealthcare and pharmaceuticalsRetail and E-commerceTelecom and ITOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth KoreaRest of World (ROW)

By Product Type Insights

The information and analytical processing segment is estimated to witness significant growth during the forecast period.

The market is experiencing significant growth, with data replication strategies becoming increasingly sophisticated to ensure capacity planning models accommodate expanding data volumes. ETL tool selection and business intelligence platforms are crucial components, enabling query optimization strategies and disaster recovery planning. Data warehouse migration, data profiling methods, and real-time data ingestion are essential for maintaining a competitive edge. Data warehouse automation, data quality metrics, and data warehouse modernization are ongoing priorities, with data cleansing techniques and dimensional modeling techniques essential for ensuring data accuracy. Data warehousing architecture, performance monitoring tools, and high availability solutions are integral to ensuring scalability and availability. Audit trail management, data lineage tracking, and data warehouse maintenance are critical for maintaining data security and compliance. Data security protocols and data encryption methods are essential for protecting sensitive information, while data virtualization techniques and access control mechanisms facilitate self-service business intelligence tools. ETL process optimization and data governance policies are key to streamlining operations and ensuring data consistency. The IT, BFSI, education, healthcare, and retail sectors are driving market growth, with information processing and analytical processing becoming increasingly important. The construction of web-based accessing tools integrated with web browsers is a current trend, enabling users to access data warehouses easily. According to recent studies, the market for data warehousing solutions is projected to grow by 18.5%, while the adoption of cloud data warehou

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%.

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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?

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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.

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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.

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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

Market Overview The global data warehouse solution market is expected to grow exponentially over the forecast period from 2023 to 2033. In 2023, the market size was valued at USD XX million, and it is projected to reach USD XX million by 2033, exhibiting a CAGR of XX% during the forecast period. The surging demand for data-driven decision-making, increasing adoption of cloud-based datawarehouse platforms, and the need for real-time analytics are the primary growth drivers for the market. Market Segments and Key Players The data warehouse solution market is segmented into applications such as finance, government, enterprise, and others. The application in the finance sector dominates the market due to the need for fraud detection, risk management, and financial forecasting. By type, the market is classified into data warehouse platforms, data warehouse tools, services, and others. Amazon Redshift, Snowflake, Google Cloud, IBM, Oracle, Microsoft Azure Synapse, SAP, Teradata, Vertica, Huawei Cloud, Alibaba Cloud, Baidu AI Cloud, KingbaseES, Yusys Technologies, Shenzhen Suoxinda Data Technology, CEC GienTech Technology, Transwarp Technology, Shenzhen Sandstone, China Soft International, and Futong Dongfang Technology are the prominent players in the global data warehouse solution market.

The Cloud Data Warehouse Tool market is rapidly evolving as organizations increasingly recognize the importance of leveraging data for strategic decision-making. These tools enable businesses to store, process, and analyze vast amounts of data in a flexible and scalable manner, making them essential for industries s

Academic Analytics Data Warehouse Dataset Description The following document describes the structure and potential analysis of the Academic Analytics Data Warehouse, a Star Schema dataset designed for predictive and proactive decision-making at a university.

Dataset Overview This comprehensive, ready-to-use data warehouse model is focused on the core operations of a university: Student Enrollment and Student Finance. It uses a Star Schema structure, prioritizing fast reporting and ease of use for non-technical leaders. The dataset allows for the generation of detailed, quick information about student activity, resource use, and university income.

Dataset Title Academic Analytics Data Warehouse: Enrollment and Finance Star Schema

Dataset Description: What's Inside This dataset is a model for a robust academic analytics system, capturing two core subject areas—Enrollment and Finance—and connecting them via a common set of context tables (Dimensions).

Key Features: Star Schema Design: Built around two main fact tables (FactEnrollment and FactFinance) for clear, separate analysis of academic and monetary events. Student Success Focus: Enables analysis of academic success, retention rates, and resource utilization based on courses and student demographics. Financial Tracking: Provides detailed insights into revenue sources, fee types, payment methods, and tax amounts for forecasting and optimization. Time-Series Ready: Includes a robust DimDate dimension for analyzing trends over time by Academic Year, Semester, Month, and Day. Organisational Context: Dimensions allow for filtering and comparing data across different Colleges and Campuses.

Columns Description (Data Model) The data model uses a Star Schema, built around two primary Fact Tables (containing measurements) and six Dimension Tables (providing context).

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

Question Paper Solutions of chapter Introduction to Data Warehousing of Data Warehousing and Data Mining, 3rd Semester , Master of Computer Applications (2 Years)

The table Chronic Conditions Algorithm is part of the dataset Chronic Conditions Data Warehouse, available at https://stanford.redivis.com/datasets/0z4y-2bv9vr3wt. It contains 29 rows across 4 variables.

From the earliest stages of planning the North West Shelf Joint Environmental Management Study it was evident that good management of the scientific data to be used in the research would be important for the success of the Study. A comprehensive review of data sets and other information relevant to the marine ecosystems, the geology, infrastructure and industries of the North West Shelf area had been completed (Heyward et al. 2006). The Data Management Project was established to source and prepare existing data sets for use, requiring the development and use of a range of tools: metadata systems, data visualisation and data delivery applications. These were made available to collaborators to allow easy access to data obtained and generated by the Study. The CMAR MarLIN metadata system was used to document the 285 data sets, those which were identified as potentially useful for the Study and the software and information products generated by and for the Study. This report represents a hard copy atlas of all NWSJEMS data products and the existing data sets identified for potential use as inputs to the Study. It comprises summary metadata elements describing the data sets, their custodianship and how the data sets might be obtained. The identifiers of each data set can be used to refer to the full metadata records in the on-line MarLIN system.

This list provides Underground Storage Tank (UST) site, tank, contact and Enforcement information for the approximately 45,000 commercial underground storage tanks (USTs) previously and currently registered in Connecticut, of which about 8,000 are still in use. (There are 3 other related data sets: 1-Facility and Tank Details, 2-Contacts, and 3-Compliance Details.) Annually, or when a UST is installed, removed, or altered, a notification form must be completed via EZFile and submitted to CT DEEP. Notification is required for non-residential underground storage tanks, including those for oil, petroleum and chemical liquids, as well as residential home heating oil tanks serving five or more units. See online at: https://www.ct.gov/deep/cwp/view.asp?q=322600 The underground storage tank regulations and the Connecticut underground storage tank enforcement program have been in effect since November 1985. This list is based on notification information submitted by the public since 1985, and is updated weekly. This list contains information on both active and on non-active USTs, as well as federally regulated and state regulated USTs. Factors to Consider When Using this data: -Not every required notification form is submitted to the DEEP. We can only enter the information we receive. -We know there are errors in the data although we strive to minimize them. Error examples may include: notification forms completed incorrectly by the owner/operator, data entry errors, duplicate site information, misspelled names and addresses and/or missing data.

Dataset Description: Text Summarization Dataset

This dataset is designed for users aiming to train models for text summarization. It contains 2,225 rows of data with two columns: "Text" and "Summary". Each row features a detailed news article or piece of text paired with its corresponding summary, providing a rich resource for developing and fine-tuning summarization algorithms.

Key Features:

• Text: Full-length articles or passages that serve as the input for summarization.
• Summary: Concise summaries of the articles, which are ideal for training models to generate brief, coherent summaries from longer texts.

Future Enhancements:

This evolving dataset is planned to include additional features, such as text class labels, in future updates. These enhancements will provide more context and facilitate the development of models that can perform summarization across different categories of news content.

Usage:

Ideal for researchers and developers focused on text summarization tasks, this dataset enables the training of models to effectively compress information while retaining the essence of the original content.

Acknowledgment

We would like to extend our sincere gratitude to the dataset creator for their contribution to this valuable resource. This dataset, sourced from the BBC News Summary dataset on Kaggle, was created by Pariza. Their work has provided an invaluable asset for those working on text summarization tasks, and we appreciate their efforts in curating and sharing this data with the community.

Thank you for supporting research and development in the field of natural language processing!

File Description

This script processes and consolidates text data from various directories containing news articles and their corresponding summaries. It reads the files from specified folders, handles encoding issues, and then creates a DataFrame that is saved as a CSV file for further analysis.

Key Components:

1. Imports:

   • numpy (np): Numerical operations library, though it's not used in this script.
   • pandas (pd): Data manipulation and analysis library.
   • os: For interacting with the operating system, e.g., building file paths.
   • glob: For file pattern matching and retrieving file paths.

2. Function: get_texts

   • Parameters:
     • text_folders: List of folders containing news article text files.
     • text_list: List to store the content of text files.
     • summ_folder: List of folders containing summary text files.
     • sum_list: List to store the content of summary files.
     • encodings: List of encodings to try for reading files.
   • Purpose:
     • Reads text files from specified folders, handles different encodings, and appends the content to text_list and sum_list.
     • Returns the updated lists of texts and summaries.

3. Data Preparation:

   • text_folder: List of directories for news articles.
   • summ_folder: List of directories for summaries.
   • text_list and summ_list: Initialize empty lists to store the contents.
   • data_df: Empty DataFrame to store the final data.

4. Execution:

   • Calls get_texts function to populate text_list and summ_list.
   • Creates a DataFrame data_df with columns 'Text' and 'Summary'.
   • Saves data_df to a CSV file at /kaggle/working/bbc_news_data.csv.

5. Output:

   • Prints the first few entries of the DataFrame to verify the content.

Column Descriptions:

• Text: Contains the full-length articles or passages of news content. This column is used as the input for summarization models.
• Summary: Contains concise summaries of the corresponding articles in the "Text" column. This column is used as the target output for summarization models.

Usage:

• This script is designed to be run in a Kaggle environment where paths to text data are predefined.
• It is intended for preprocessing and saving text data from news articles and summaries for subsequent analysis or model training.

Mass spectrometry, a popular technique for elucidating the molecular contents of experimental samples, creates data sets comprised of millions of three-dimensional (m/z, retention time, intensity) data points that correspond to the types and quantities of analyzed molecules. Open and commercial MS data formats are arranged by retention time, creating latency when accessing data across multiple m/z. Existing MS storage and retrieval methods have been developed to overcome the limitations of retention time-based data formats, but do not provide certain features such as dynamic summarization and storage and retrieval of point meta-data (such as signal cluster membership), precluding efficient viewing applications and certain data-processing approaches. This manuscript describes MzTree, a spatial database designed to provide real-time storage and retrieval of dynamically summarized standard and augmented MS data with fast performance in both m/z and RT directions. Performance is reported on real data with comparisons against related published retrieval systems.

737 PI accessions from the US Department of Agriculture, Agricultural Research Service Sweetpotato Collection were grown in the field and in greenhouse pots, and storage roots were harvested. The periderm (skin) and stele (flesh) of storage roots were measured using a Konica Minolta Chroma Meter (CR 400, Konica Minolta, Inc., Tokyo, Japan), and data were recorded using the CIE 1976 Lab and CIE LCh color spaces. Data from this study is contained in a manuscript that will be submitted to Genetic Resources and Crop Evolution under the title "Color Analysis of Storage Roots from the USDA, ARS Sweetpotato Germplasm Collection." Data parameters presented are lightness (L), red-green coordinate (a), yellow-blue coordinate (b), color intensity or chroma (C), and hue angle (H*). Also included in this data set are percentage dry matter and root densities as these data are correlated to color values. Resources in this dataset:Resource Title: Sweetpotato Periderm-Field. File Name: Sweetpotato-Periderm-Field.xlsxResource Description: Colorimeter data for the periderm of field-grown sweetpotato storage rootsResource Title: Sweetpotato Periderm - Pots. File Name: Sweetpotato-Periderm-Pots.xlsxResource Description: Colorimeter data for the periderm of pot-grown sweetpotato storage rootsResource Title: Sweetpotato Stele - Field. File Name: Sweetpotato-Stele-Field.xlsxResource Description: Colorimeter data for the stele of field-grown sweetpotato storage rootsResource Title: Sweetpotato Stele - Pots. File Name: Sweetpotato-Stele-Pots.xlsxResource Description: Colorimeter data for the stele of pot-grown sweetpotato storage rootsResource Title: Sweetpotato Dry Weights - Field. File Name: Sweetpotato-DryWt-Field.xlsxResource Description: Dry weight and root density data for the stele of field-grown sweetpotato storage rootsResource Title: Sweetpotato Periderm - Summary Table. File Name: Sweetpotato-Periderm-Summary Table.xlsxResource Description: Colorimeter and dry weight summaries for the periderm of field-grown and pot-grown sweetpotato storage rootsResource Title: Sweetpotato Stele - Summary Table. File Name: Sweetpotato-Stele-Summary Table.xlsxResource Description: Colorimeter and dry weight summaries for the stele of field-grown and pot-grown sweetpotato storage rootsResource Title: Sweetpotato Stele - Summary Table. File Name: Sweetpotato-Stele-Summary Table.csvResource Description: CSV version of the data for Colorimeter and dry weight summaries for the stele of field-grown and pot-grown sweetpotato storage roots Resource Title: Sweetpotato Dry Weights - Field. File Name: Sweetpotato-DryWt-Field.csvResource Description: CSV version of Dry weight and root density data for the stele of field-grown sweetpotato storage roots Resource Title: Sweetpotato Stele - Field. File Name: Sweetpotato-Stele-Field.csvResource Description: CSV version of the Colorimeter data for the stele of field-grown sweetpotato storage roots Resource Title: Sweetpotato Periderm-Field. File Name: Sweetpotato-Periderm-Field.csvResource Description: CSV version of Colorimeter data for the periderm of field-grown sweetpotato storage roots Resource Title: Data Dictionary - Color Analysis of Storage Roots from the USDA, ARS Sweetpotato Germplasm Collection. File Name: sweetpotato_DD.csvResource Title: Sweetpotato Stele - Pots. File Name: Sweetpotato-Stele-Pots.csvResource Description: CSV version of Colorimeter data for the stele of pot-grown sweetpotato storage roots Resource Title: Sweetpotato Periderm - Pots. File Name: Sweetpotato-Periderm-Pots.csvResource Description: CSV version of Colorimeter data for the periderm of pot-grown sweetpotato storage roots Resource Title: Sweetpotato Periderm - Summary Table. File Name: Sweetpotato-Periderm-Summary Table.csvResource Description: CSV version of data for Colorimeter and dry weight summaries for the periderm of field-grown and pot-grown sweetpotato storage roots

Pure Storage, Inc. provides data storage technologies, products, and services in the United States and internationally. The company's Purity software is shared across its products and provides enterprise-class data services, such as data reduction, data protection, and encryption, as well as storage protocols, including block, file, and object. Its products portfolio includes FlashArray for block-oriented storage, addressing databases, applications, virtual machines, and other traditional workloads; FlashArray//XL; and FlashArray//C, an all-QLC flash array. The company also provides FlashBlade, a solution for unstructured data workloads of various types; FlashStack that combines compute, network, and storage to provide an infrastructure platform; FlashRecover, an all-flash modern data-protection solution; and AIRI, a full-stack AI-ready infrastructure. In addition, it offers evergreen storage subscription, Pure as-a-Service, and Cloud Block Store, as well as Portworx a cloud-native Kubernetes data management solution It also offers technical and professional, training and education, and certification services. The company sells its products and subscription services through direct sales force and channel partners. The company was formerly known as OS76, Inc. and changed its name to Pure Storage, Inc. in January 2010. Pure Storage, Inc. was incorporated in 2009 and is headquartered in Mountain View, California.