The Archive as a Service (AaaS) market is experiencing robust growth, driven by the increasing need for long-term data storage, compliance requirements, and the rising adoption of cloud computing. While precise figures for market size and CAGR are not provided, a reasonable estimation based on industry trends and the listed companies suggests a substantial market. Considering the involvement of major cloud providers like AWS and Microsoft Azure alongside specialized AaaS vendors, a 2025 market size of approximately $15 billion is plausible. A conservative compound annual growth rate (CAGR) of 15% from 2025 to 2033 could be anticipated, reflecting ongoing digital transformation and the growing volume of unstructured data generated across sectors like healthcare, finance, and entertainment. Key drivers include the escalating costs of on-premise storage, the need for disaster recovery and business continuity solutions, and the increasing demand for secure, scalable, and cost-effective data archiving. Market segmentation, as indicated by the listed companies and application areas, highlights the diverse needs met by AaaS solutions. The strong presence of major players indicates high market maturity, while the presence of smaller, specialized firms suggests opportunities for niche solutions. The geographic distribution, encompassing North America, Europe, and Asia-Pacific, points to a global market with potential for expansion in emerging economies. The long-term forecast for AaaS is positive, with continued growth anticipated due to factors such as the increasing generation of data from IoT devices and the ongoing shift towards cloud-based infrastructure. Government regulations regarding data retention and compliance are also significantly bolstering AaaS adoption. Challenges could include concerns around data security and privacy, as well as managing data migration and integration with existing systems. However, continuous innovation in data compression, storage technologies, and security measures is likely to mitigate these challenges. The competitive landscape, characterized by both established players and innovative startups, is likely to remain dynamic, with continued mergers, acquisitions, and product development driving further market evolution. The AaaS market presents a significant opportunity for businesses seeking to effectively manage their growing data volumes while complying with industry regulations and ensuring data accessibility and protection.

Raw US Energy Information Administration (EIA) Form 860M data, archived from https://www.eia.gov/electricity/data/eia860m/

This archive contains raw input data for the Public Utility Data Liberation (PUDL) software developed by Catalyst Cooperative. It is organized into Frictionless Data Packages. For additional information about this data and PUDL, see the following resources:

The PUDL Repository on GitHub

PUDL Documentation

Other Catalyst Cooperative data archives

ALL public utility data layers can be downloaded together with this single .mpkx (ArcGIS Pro map package file), updated every Friday evening. This .mpkx file can be opened directly with ArcGIS Pro version 3+. Alternatively, you can extract the file geodatabase within it by renaming the file ending .mpkx to .zip and treating it like a zip archive file, for use in any version of ArcGIS Pro or ArcMap software. You can also use QGIS, a powerful, free, and open-source GIS software.

This is an export of the data archived from the 2024 National Incident Feature Service.Sensitive fields and features have been removed.Each edit to a feature is captured in the Archive. The GDB_FROM and GDB_TO fields show the date range that the feature existed in the National Incident Feature Service.The National Incident Feature Service is based on the National Wildfire Coordinating Group (NWCG) data standard for Wildland Fire Event. The Wildland Fire Event data standard defines the minimum attributes necessary for collection, storage and dissemination of incident based data on wildland fires (wildfires and prescribed fires). The standard is not intended for long term data storage, rather a standard to assist in the creation of incident based data management tools, minimum standards for data exchange, and to assist users in meeting the NWCG Standards for Geospatial Operations (PMS 936).

Hourly Electricity Demand by State

This archive contains the output of the Public Utility Data Liberation (PUDL) Project state electricity demand allocation analysis, as of the v0.4.0 release of the PUDL Python package. Here is the script that produced this output. It was run using the Docker container and processed data that are included in PUDL Data Release v2.0.0.

The analysis uses hourly electricity demand reported at the balancing authority and utility level in the FERC 714 (data archive), and service territories for utilities and balancing authorities inferred from the counties served by each utility, and the utilities that make up each balancing authority in the EIA 861 (data archive), to estimate the total hourly electricity demand for each US state.

We used the total electricity sales by state reported in the EIA 861 as a scaling factor to ensure that the magnitude of electricity sales is roughly correct, and obtains the shape of the demand curve from the hourly planning area demand reported in the FERC 714. The scaling is necessary partly due to imperfections in the historical utility and balancing authority service territory maps which we have been able to reconstruct from the data reported in the EIA 861 Service Territories and Balancing Authority tables.

The compilation of historical service territories based on the EIA 861 data is somewhat manual and could be improved, but overall the results seem reasonable. Additional predictive spatial variables will be required to obtain more granular electricity demand estimates (e.g. at the county level).

FERC 714 Respondents

The file ferc714_respondents.csv links FERC Form 714 respondents to what we believe to be their corresponding EIA utilities or balancing authorities.

• eia_code: An integer ID reported in the FERC Form 714 corresponding to the respondent's EIA ID. In some cases this is a Utility ID, and in others it is a Balancing Authority ID, but which is not specified and so we have had to infer the type of entity which is responding. Note that in many cases the same company acts as both a utility and a balancing authority, and the integer ID associated with the company is often the same in both roles, but it does not need to be.
• respondent_type: Either balancing_authority or utility depending on which type of entity we believe was responding to the FERC 714.
• respondent_id_ferc714: The integer ID of the responding entity within the FERC 714.
• respondent_name_ferc714: The name provided by the respondent in the FERC 714.
• balancing_authority_id_eia: If the respondent was identified as a balancing authority, the EIA ID for that balancing authority, taken from the EIA Form 861.
• balancing_authority_code_eia: If the respondent was identified as a balancing authority, the EIA short code used to identify the balancing authority, taken from the EIA Form 861.
• balancing_authority_name_eia: If the respondent was identified as a balancing authority, the name of the balancing authority, taken from the EIA Form 861.
• utility_id_eia: If the respondent was identified as a utility, the EIA utility ID, taken from the EIA Form 861.
• utility_name_eia: If the respondent was identified as a utility, the name of the utility, taken from the EIA 861.

FERC 714 Respondent Service Territories

The file ferc714_service_territories.csv describes the historical service territories for FERC 714 respondents for the years 2006-2019. For each respondent and year, their service territory is composed of a collection of counties, identified by their 5-digit FIPS codes. The file contains the following columns, with each row associating a single county with a FERC 714 respondent in a particular year:

• respondent_id_ferc714: The FERC Form 714 respondent ID, which is also found in ferc714_respondents.csv
• report_date: The first day of the year for which the service territory is being described.
• state: Two letter abbreviation for the state containing the county, for human readability.
• county: The name of the county, for human readability.
• state_id_fips: The 2-digit FIPS state code.
• county_id_fips: The 5-digit FIPS county code for use with other geospatial data resources, like the US Census DP1 geodatabase.

State Hourly Electricity Demand Estimates

The file demand.csv contains hourly electricity demand estimates for each US state from 2006-2019. It contains the following columns:

• state_id_fips: The 2-digit FIPS state code.
• utc_datetime: UTC time at hourly resolution.
• demand_mwh: Electricity demand for that state and hour in MWh. This is an allocation of the electricity demand reported directly in the FERC Form 714.
• scaled_demand_mwh: Estimated total electricity demand for that state and hour, in MWh. This is the reported FERC Form 714 hourly demand scaled up or down linearly such that the total annual electricity demand matches the total annual electricity sales reported at the state level in the EIA Form 861.

A collection of plots are also included, comparing the original and scaled demand time series for each state.

Acknowledgements

This analysis was funded largely by GridLab, and done in collaboration with researchers at the Lawrence Berkeley National Laboratory, including Umed Paliwal and Nikit Abhyankar.

• Ethan Welty wrote the final code and most of the algorithms.
• Yash Kumar did initial data explorations and geospatial analyses.

The data screening methods were originally designed to identify unrealistic data in the electricity demand timeseries reported to EIA on Form 930, and have been applied here to data form the FERC Form 714.

They are adapted from code published and modified by:

• Tyler Ruggles
• Greg Schivley

And described at:

• Developing reliable hourly electricity demand data through screening and imputation
• EIA Cleaned Hourly Electricity Demand Code (Zenodo)
• EIA Cleaned Hourly Electricity Demand Code (GitHub)

The imputation methods were designed for multivariate time series forecasting.

They are adapted from code published by:

• Xinyu Chen chenxy346@gmail.com

And described at:

• Low-Rank Autoregressive Tensor Completion for Multivariate Time Series Forecasting
• Scalable Low-Rank Tensor Learning for Spatiotemporal Traffic Data Imputation
• Tensor Learning (张量学习)

About PUDL & Catalyst Cooperative

For additional information about this data and PUDL, see the following resources:

• The PUDL Repository on GitHub
• The PUDL Documentation
• Other Catalyst Cooperative data archives on Zenodo

The North America Enterprise Information Archiving Market is anticipated to reach a value of 5.06 million by 2033, expanding at a CAGR of 11.08% from 2023 to 2033. The increasing volume of enterprise data, regulatory compliance requirements, and the need for efficient information management are driving the market growth. The adoption of cloud-based archiving solutions and the rising demand for data security are further contributing to the market expansion. The market is segmented based on offering (software and service), deployment (cloud and on-premises), organization size (SMEs and large enterprises), and end-user industries (BFSI, IT and Telecom, Retail and E-commerce, Healthcare, Government, Media and Entertainment, Education, and others). The BFSI sector holds a significant market share due to stringent regulatory requirements and the need to preserve financial records for extended periods. Other key end-user industries include healthcare, government, and education, which are increasingly adopting information archiving solutions to meet compliance mandates and ensure data integrity. Recent developments include: December 2023- Preservica launched a FOIA tool and public records archiving service. The new tool can save officials from labor-intensive efforts to keep minutes, communications, and other documents because it integrates with Microsoft 365. Officials and staff can use a unified information government strategy across the complete records life cycle without learning and using separate vendors' specific compliance archiving and discovery tools due to the new technology., June 2023- Accenture Federal Services won a contract of USD 329 million to manage the information assurance and privacy program for the United States Agency for International Development (USAID), in which Accenture would deliver a full suite of services, including risk, compliance support, and privacy program for the government Agency of the US government, which shows the increasing demand trend of enterprise information archiving need in the government sector of the region, which would fuel the market growth in the future.. Key drivers for this market are: Rising Adoption of Cloud-based and Subscription-based Model, Rapid Increase in the Data Volumes in Enterprises; Integration of Big Data Analytics and AI Technologies. Potential restraints include: Lack of Technical Expertise in Dealing With High Content Volume, Concerns Related to Security and Privacy of Enterprise Data. Notable trends are: Cloud Segment to Witness Major Growth.

The United States Wind Turbine Database (USWTDB) provides the locations of land-based and offshore wind turbines in the United States, corresponding wind project information, and turbine technical specifications. Wind turbine records are collected and compiled from various public and private sources, digitized and position-verified from aerial imagery, and quality checked. The USWTDB is available for download in a variety of tabular and geospatial file formats, to meet a range of user/software needs. Dynamic web services are available for users that wish to access the USWTDB as a Representational State Transfer Services (RESTful) web service. Archived from https://energy.usgs.gov/uswtdb/

This archive contains raw input data for the Public Utility Data Liberation (PUDL) software developed by Catalyst Cooperative. It is organized into "https://specs.frictionlessdata.io/data-package/">Frictionless Data Packages. For additional information about this data and PUDL, see the following resources:

• The PUDL Repository on GitHub
• PUDL Documentation
• Other Catalyst Cooperative data archives

All Employees: Transportation and Utilities: Warehousing and Storage in Kentucky was 39.40000 Thous. of Persons in March of 2022, according to the United States Federal Reserve. Historically, All Employees: Transportation and Utilities: Warehousing and Storage in Kentucky reached a record high of 42.70000 in December of 2021 and a record low of 5.80000 in April of 1990. Trading Economics provides the current actual value, an historical data chart and related indicators for All Employees: Transportation and Utilities: Warehousing and Storage in Kentucky - last updated from the United States Federal Reserve on March of 2025.

Archive of Calls for Service data from 2012 to 2019 provided as a single source. Staging for sub-layers for specified time periods.

This dataset shows the Value added of services sub-sector (Utilities, Transport, Storage & Communication) by state, 2005-2020 at constant prices Notes: Supra State covers production activities that beyond the centre of predominant economic interest for any state Not applicable For base year 2005, the values for year 2012 are estimate and the values for year 2013 are preliminary For base year 2010, the values for year 2016 are estimate and the values for year 2017 are preliminary For base year 2015, the values for year 2019 are estimate and the values for year 2020 are preliminary DEPARTMENT OF STATISTICS MALAYSIA

Aggregate national, state, and plant-level electricity generation statistics, including fuel quality and consumption, for grid-connected plants with nameplate capacity of 1 megawatt or greater Archived from https://www.eia.gov/opendata/bulkfiles.php This archive contains raw input data for the Public Utility Data Liberation (PUDL) software developed by Catalyst Cooperative. It is organized into Frictionless Data Packages. For additional information about this data and PUDL, see the following resources: The PUDL Repository on GitHub PUDL Documentation Other Catalyst Cooperative data archives

Market Analysis for Archive Storage Services The global archive storage market is projected to reach a valuation of USD X million by 2033, exhibiting a CAGR of XX% during the forecast period (2025-2033). Cloud computing, data protection concerns, and the proliferation of digital data are key drivers fueling market growth. The rising need for preserving and managing valuable records and archives in sectors such as healthcare, government, and education is another major factor contributing to market expansion. Key segments of the archive storage market include application and type. By application, the corporate sector holds the largest share due to the vast amount of data generated by businesses. Healthcare research institutions and government agencies are also significant segments, with a growing demand for secure storage of sensitive information. In terms of type, both physical and digital archive storage are witnessing substantial growth. Cloud-based digital storage solutions offer convenience, scalability, and cost-effectiveness, while physical storage remains essential for preserving physical archives and ensuring disaster recovery. Major players in the market include Amazon Web Services, IBM, and Iron Mountain, among others. Regional analysis reveals that North America and Europe account for a significant portion of the market, followed by Asia Pacific and the Middle East & Africa.

Graph and download economic data for All Employees: Transportation and Utilities: Warehousing and Storage in Los Angeles-Long Beach-Glendale, CA (MD) (SMU06310844349300001SA) from Jan 1990 to Jan 2025 about warehousing, employment, and USA.

The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey. Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values. Resources in this dataset:Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided).Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

An appropriate deployment of energy storage technologies is of primary importance for the transition towards an energy system. For that reason, this database has been created as a complement for the Study on energy storage - contribution to the security of the electricity supply in Europe.

The database includes three different approaches:

• Energy storage technologies: All existing energy storage technologies with their characteristics.

• Front of the meter facilities: List of all energy storage facilities in the EU-28, operational or in project, that are connected to the generation and the transmission grid with their characteristics.

• Behind the meter energy storage: Installed capacity per country of all energy storage systems in the residential, commercial and industrial infrastructures.

The purpose of this database is to give a global view of all energy storage technologies. They are sorted in five categories, depending on the type of energy acting as a reservoir. Relevant types of data for each technology have been highlighted.

Raw Federal Energy Regulatory Commission (FERC) Form 1 data, archived from https://www.ferc.gov/industries-data/electric/general-information/electric-industry-forms/form-1-electric-utility-annual

This archive contains raw input data for the Public Utility Data Liberation (PUDL) software developed by Catalyst Cooperative. It is organized into Frictionless Data Packages. For additional information about this data and PUDL, see the following resources:

The PUDL Repository on GitHub

PUDL Documentation

Other Catalyst Cooperative data archives