About this dataset

Context

The dataset tabulates the Snowflake population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Snowflake. The dataset can be utilized to understand the population distribution of Snowflake by age. For example, using this dataset, we can identify the largest age group in Snowflake.

Key observations

The largest age group in Snowflake, AZ was for the group of age 10-14 years with a population of 916 (15.05%), according to the 2021 American Community Survey. At the same time, the smallest age group in Snowflake, AZ was the 80-84 years with a population of 43 (0.71%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Snowflake is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Snowflake total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Snowflake Population by Age. You can refer the same here

Data for the Arctic Embed walkthrough

This dataset coresponds to the walkthrough example for using the Arctic Embed training code in ArcticTraining. See that README for more details.

  Example: Selective downloads via Git LFS

Since this dataset contains various intermediate files not necessary for training, it can be helpful to use the Git LFS backend of Hugging Face Datasets to pull select files.

First, ensure you have installed git-lfs (see https://git-lfs.com/… See the full description on the dataset page: https://huggingface.co/datasets/Snowflake/arctic-embed-ft-v1.

This repository contains documentation for the dataset that accompanies our ICPE 2025 paper, "Shaved Ice: Optimal Compute Resource Commitments for Dynamic Multi-Cloud Workloads". It also includes example R and Python notebooks to read and visualize the data, including scripts to reproduce the figures and analysis results in the paper.

This project is archived on Zenodo, an open-access repository, to ensure long-term reproducibility of the research.

Dataset The dataset contains normalized and obfuscated hourly data about VM demand in four example Snowflake deployments over a period of 3 years from 2/1/2021 to 1/30/2024. Each hour includes (type of VM, region, number of VMs of that type) used at that time. This dataset is available in both compressed CSV and Parquet formats.

Schema

Timestamp: An hourly timestamp for the record. VM Type: This field is obfuscated with the precise VM identifier from the Cloud Service Provider mapped into a capital letter. Region: The region where the VM was deployed. This field is obfuscated with the precise region name from the Cloud Service Provider mapped into a number between 1 and 4. Count: The number of VMs of the specified type, region, and hour. This field is normalized such that the largest type, region, hour tuple is set to 1000 in each region and other values are scaled linearly to the nearest whole number.

Potential Use Cases Provides realistic industry dataset for further research into cloud demand forecasting, commitment optimization, and capacity planning.

About this dataset

Context

The dataset tabulates the population of Snowflake by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Snowflake. The dataset can be utilized to understand the population distribution of Snowflake by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Snowflake. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Snowflake.

Key observations

Largest age group (population): Male # 10-14 years (488) | Female # 15-19 years (371). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Snowflake population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Snowflake is shown in the following column.
• Population (Female): The female population in the Snowflake is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Snowflake for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Snowflake Population by Gender. You can refer the same here

Dataset overview

This dataset provides data and images of snowflakes in free fall collected with a Multi-Angle Snowflake Camera (MASC) The dataset includes, for each recorded snowflakes:

1. A triplet of gray-scale images corresponding to the three cameras of the MASC
2. A large quantity of geometrical, textural descriptors and the pre-compiled output of published retrieval algorithms as well as basic environmental information at the location and time of each measurement.

The pre-computed descriptors and retrievals are available either individually for each camera view or, some of them, available as descriptors of the triplet as a whole. A non exhaustive list of precomputed quantities includes for example:

• Textural and geometrical descriptors as in Praz et al 2017
• Hydrometeor classification, riming degree estimation, melting identification, as in Praz et al 2017
• Blowing snow identification, as in Schaer et al 2020
• Mass, volume, gyration estimation, as in Leinonen et al 2021

Data format and structure

The dataset is divided into four .parquet file (for scalar descriptors) and a Zarr database (for the images). A detailed description of the data content and of the data records is available here.

Supporting code

A python-based API is available to manipulate, display and organize the data of our dataset. It can be found on GitHub. See also the code documentation on ReadTheDocs.

Download notes

• All files available here for download should be stored in the same folder, if the python-based API is used
• MASCdb.zarr.zip must be unzipped after download

The global snowflake ice machine market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, estimated at $1.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This growth is fueled by several key factors. The food and beverage industry's reliance on high-quality ice for cocktails, displays, and food preservation remains a significant driver. Furthermore, the burgeoning medical sector, utilizing snowflake ice for therapeutic applications and sample preservation, contributes significantly to market expansion. The rise of biological engineering and its need for precise temperature control further boosts demand. Technological advancements leading to more energy-efficient and reliable machines are also positively impacting market growth. While initial investment costs can be a restraint for some smaller businesses, the long-term operational efficiency and superior ice quality offered by snowflake ice machines offset this factor. The market is segmented by application (food, medical, biological engineering) and geography, with North America, Europe, and Asia Pacific representing the largest regional markets. Competition is relatively diverse, with both established players like Hoshizaki and emerging companies vying for market share. The competitive landscape involves both large multinational corporations and smaller, specialized manufacturers. These companies are focusing on product innovation, enhancing energy efficiency, and improving the durability of their machines. Strategic partnerships and acquisitions are also expected to shape the market dynamics in the coming years. Specific geographic regions may experience differing growth rates based on factors such as economic development, regulatory frameworks, and consumer preferences. However, the overall trend points to sustained expansion of the snowflake ice machine market throughout the forecast period. This consistent growth reflects the increasing importance of high-quality, consistent ice production across multiple industries. This report provides a detailed analysis of the global snowflake ice machine market, projecting a market value exceeding $2 billion by 2028. It delves into market dynamics, key players, emerging trends, and future growth prospects, offering valuable insights for stakeholders across the food, medical, and biological engineering sectors. The report leverages extensive market research and data analysis to present a comprehensive overview of this rapidly evolving industry.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

According to Cognitive Market Research, The Global Database Engines market size is USD 1.5 billion in 2023 and will grow at a compound annual growth rate (CAGR) of 25.50% from 2023 to 2030.

The demand for the database engine marketis rising due to theadvancements in Artificial Intelligence (AI) and Machine Learning (ML), technological progress, and the increasing volume of data.
Demand for storage engines remains higher in the database engines market.
The large enterprises category held the highest database engine market revenue share in 2023.
North America will continue to lead, whereas the Asia Pacific database engines market will experience the strongest growth until 2030.

Cloud Adoption Driving Market Expansion to Provide Viable Market Output

The growth of the database engines market is the widespread adoption of cloud computing. As businesses increasingly migrate their operations to the cloud, the demand for robust, scalable, and efficient database engines has surged. Cloud-based database solutions offer several advantages, including flexibility, cost-effectiveness, and accessibility. Enterprises are leveraging cloud-native database engines to manage vast amounts of data without the need for substantial on-premises infrastructure.

Dell Technologies introduced a new collaboration and market alignment with Snowflake in May 2022. This collaboration brings together Dell's on-premise storage system with Snowflake's cloud technology solutions, providing users with versatile operations in multi-cloud infrastructure and meeting data sovereignty requirements.

Moreover, cloud platforms provide tools and services for real-time analytics, artificial intelligence, and machine learning, enhancing the capabilities of database engines. The ease of deployment and management in the cloud environment has made it a preferred choice for businesses of all sizes, driving the market's growth.

(Source:www.dell.com/en-in/blog/snowflake-and-dell-partnership-gains-momentum/)

Data Security and Compliance Requirements to Propel Market Growth

The growing emphasis on data security and compliance. With the increasing frequency and sophistication of cyber-attacks, businesses are prioritizing secure data management solutions. Database engines equipped with advanced security features such as encryption, access controls, and audit trails are in high demand. Additionally, regulatory requirements related to data protection and privacy, such as GDPR in Europe and HIPAA in the United States, are compelling organizations to invest in database engines that ensure compliance. Due to the potential for extensive financial and reputational harm, companies are eager to allocate resources towards advanced database engines that offer strong security features. The focus on data security and compliance not only drives the adoption of database engines but also fosters innovation, leading to the development of more secure and efficient solutions in the market.

Rising demand for real-time data analysis

Market Dynamics of Database Engines

Data Privacy Concerns and Regulatory Challenges to Hinder Market Growth

The growing concern over data privacy and the evolving landscape of regulations. As data breaches become more prevalent and publicized, consumers and businesses are becoming increasingly cautious about how their data is collected, stored, and utilized. The increased consciousness surrounding this matter has resulted in strict regulations concerning data protection, such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States. Complying with these regulations obligates companies to enforce rigorous data security measures, which have an impact on the design and functioning of database engines.

Impact of COVID–19 on the Database Engines Market

The COVID-19 pandemic significantly impacted the Database Engines Market as businesses across the globe faced unprecedented challenges. With remote work becoming the norm, the demand for cloud-based database solutions surged, driven by the need for scalable, accessible, and secure data management. Enterprises accelerated their digital transformation initiatives, leading to increased adoption of database engines that support online collaboration, e-commerce, and digital services. However, the economic uncertainties caused some organizations to de...

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13,25 (Thousand hectare) in 2016. Share of arable land under crops. Indicator reports updated information on the size of arable land under crops adjusted for its actual agricultural exploitation. Indicator accounts for all types of farms and is based on the results of total survey of farms not classified as small business entities and on the results of sample survey of small business entities (including peasant farm enterprises, farms of sole proprietors, individual subsidiary farms and other farms of citizens).

24 (Number per 100 households) in 2014. Indicator is based on the results of the sample survey of households' budgets addressing the end-year number of durable articles of a cultural and social nature owned by the households irrespectively to whether they were bought, created by the household members or received free. Indicator includes both working and broken articles pending current repair. Articles taken on hire or temporary use from relatives or acquaintance are excluded.

93,00 (Gram) in 2016. Indicator is based on the results of the sample survey of households' budgets. Daily average consumption of proteins, fats and carbonhydrates containing in basic food by households' members is calculated by dividing total quantity of nutrients consumed by the number of members that were actually present (fed) in the household during the survey period.

103,50 (Gram) in 2016. Indicator is based on the results of the sample survey of households' budgets. Daily average consumption of proteins, fats and carbonhydrates containing in basic food by households' members is calculated by dividing total quantity of nutrients consumed by the number of members that were actually present (fed) in the household during the survey period.

15.3 (100 kilogram per hectare) in 2016. Crop yield characterizes average amount of cropping per unit of harvested area. Indicator accounts for all types of farms and is based on the results of total survey of farms not classified as small business entities and on the results of sample survey of small business entities (including peasant farm enterprises, farms of sole proprietors, individual subsidiary farms and other farms of citizens).

15,056,057 (Rubles) in 2016. Indicator is based on the results of the sample survey of households' budgets. Average of 10 % groups

19,0 (100 kilogram per hectare) in 2016. Crop yield characterizes average amount of cropping per unit of harvested area. Indicator accounts for all types of farms and is based on the results of total survey of farms not classified as small business entities and on the results of sample survey of small business entities (including peasant farm enterprises, farms of sole proprietors, individual subsidiary farms and other farms of citizens).

73.373 (Rubles) in 2016. Indicator is based on the results of the sample survey of households' budgets. Households consumer expenditure is the part of money expenditure on purchasing consumer goods and services. Consumer expenditure does not include spending on purchasing pieces of art, antiques and jewelry made for the purpose of capital investment, it also excludes payments for materials and works for construction being classified as an investment. Consumer expenditure is classified by types of goods and services.

163,3 (100 kilogram per hectare) in 2016. Crop yield characterizes average amount of cropping per unit of harvested area. Indicator accounts for all types of farms and is based on the results of total survey of farms not classified as small business entities and on the results of sample survey of small business entities (including peasant farm enterprises, farms of sole proprietors, individual subsidiary farms and other farms of citizens).

5 (Number per 100 households) in 2014. Indicator is based on the results of the sample survey of households' budgets addressing the end-year number of durable articles of a cultural and social nature owned by the households irrespectively to whether they were bought, created by the household members or received free. Indicator includes both working and broken articles pending current repair. Articles taken on hire or temporary use from relatives or acquaintance are excluded.

5.463 (Rubles) in 2016. Indicator is based on the results of the sample survey of households' budgets. Households consumer expenditure is the part of money expenditure on purchasing consumer goods and services. Consumer expenditure does not include spending on purchasing pieces of art, antiques and jewelry made for the purpose of capital investment, it also excludes payments for materials and works for construction being classified as an investment. Consumer expenditure is classified by types of goods and services.