The following data is pulled from AWS official pricing API. Contains all pricing data across AWS services Source: https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/using-price-list-query-api.html Update Frequency: Gets auto updated weekly

AWS Spot Price History

This dataset tracks historical prices for AWS spot prices across all regions. It is updated automatically on the 1st of each month to contain data from the previous month.

Data format

Each month of data is stored as a ZStandard-compressed .tsv.zst file.

The data format matches that returned by AWS's describe-spot-instance-prices, with the exception that availability zones have been replaced by their global ID. For instance, here are some example lines from one capture:

euc1-az2 i4i.8xlarge Linux/UNIX 1.231800 2023-02-28T23:59:57+00:00
euc1-az3 r5b.8xlarge Red Hat Enterprise Linux 0.749600 2023-02-28T23:59:58+00:00
euc1-az3 r5b.8xlarge SUSE Linux 0.744600 2023-02-28T23:59:58+00:00
euc1-az3 r5b.8xlarge Linux/UNIX 0.619600 2023-02-28T23:59:58+00:00
euc1-az3 m5n.4xlarge Red Hat Enterprise Linux 0.476000 2023-02-28T23:59:59+00:00
euc1-az2 m5n.4xlarge Red Hat Enterprise Linux 0.492000 2023-02-28T23:59:59+00:00
euc1-az3 m5n.4xlarge SUSE Linux 0.471000 2023-02-28T23:59:59+00:00
euc1-az2 m5n.4xlarge SUSE Linux 0.487000 2023-02-28T23:59:59+00:00
euc1-az3 m5n.4xlarge Linux/UNIX 0.346000 2023-02-28T23:59:59+00:00
euc1-az2 m5n.4xlarge Linux/UNIX 0.362000 2023-02-28T23:59:59+00:00

When fetching spot instance pricing from AWS, results contain some prices from the previous month so that the price is known at the start of the month. These prices are adjusted in this dataset to be at the exact start of the month UTC:

euw3-az2 g4dn.4xlarge Linux/UNIX 0.558600 2023-01-01T00:00:00+00:00

For data from 2023-01 and before, this data was fetched more than one month at a time. This should have no negative impact unless, for example, an instance type was retired before the month began (and there should therefore be no price). These older files also only contain default regions. Data from 2023-02 and later contains all regions, including opt-in regions.

Using data

You can process each month individually. If you need the entire data stream at once, you can cat all files to zst together:

cat prices/*/*.tsv.zst | zstd -d

Dataset

This dataset was created by AkashSarda

Released under CC0: Public Domain

Contents

The Product Comparison dataset for online shopping is a new, manually annotated dataset with about 15K human generated sentences, which compare related products based on one or more of their attributes (the first such data we know of for product comparison). It covers ∼8K product sets, their selected attributes, and comparison texts.

Imbalance prices used for balancing responsible parties (BRPs)settlment. When imbalance prices are published on a quarter-hourly basis, the published prices have not yet been validated and can therefore only be used as an indication of the imbalance price. Only after the published prices have been validated can they be used for invoicing purposes. The records for month M are validated after the 15th of month M+1. Contains the historical data and is refreshed daily.This dataset contains data from 22/05/2024 (MARI local go-live) on.

System imbalance prices applied if an imbalance is found between injections and offtakes in a balance responsible parties (BRPs) balance area. When imbalance prices are published on a quarter-hourly basis, the published prices have not yet been validated and can therefore only be used as an indication of the imbalance price.Only after the published prices have been validated can they be used for invoicing purposes. The records for month M are validated after the 15th of month M+1. Contains the historical data and is refreshed daily.This dataset contains data until 21/05/2024 (before MARI local go-live).

The 1 min imbalance prices are published as fast as possible and give an indication for the final imbalance price of the ISP (imbalance settlement period which is 15min). This report contains data for the current hour and is refreshed every minute. Notice that in this report we only provide non-validated data. This dataset contains data from 22/05/2024 (MARI local go-live) on.

Obesity rates for each Census Tract in Allegheny County were produced for the study “Developing small-area predictions for smoking and obesity prevalence in the United States." The data is not explicitly based on population surveys or data collection conducted in Allegheny County, but rather estimated using statistical modeling techniques. In this technique, researchers applied the obesity rate of a demographically similar census tract to one in Allegheny County to compute an obesity rate.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

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Scarcity price-adders simulated by Elia in the context of the 2019 discretionary incentive on scarcity pricing laid upon Elia by CREG. This simulation is based on a scarcity pricing model as conceptualized in the note by CREG and UCL CORE on the general design of a mechanism for the remuneration of reserves in scarcity situations.

Imbalance prices used for balance responsible parties (BRPs) settlement for every quarter hour. This report contains data for the current day and is refreshed every quarter-hour. Notice that in this report we only provide non-validated data.This dataset contains data from 22/05/2024 (MARI local go-live) on.

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ECB reference exchange rate, Canadian dollar, US dollar, Mexican peso, UK pound sterling / Euro

Dataset Summary

The WMT14 injected synthetic dyslexia dataset is a modified version of the WMT14 English test set. This dataset was created to test the capabilities of SOTA machine translations models on dyslexic style text. This research was supported by AImpower.org.

  How the data is structured

In "Data/French_translated_data", each file within the dataset consists of a “.txt” or “.docx” file containing the translated sentences from AWS, Google, Azure and OpenAI. In… See the full description on the dataset page: https://huggingface.co/datasets/gpric024/wmt14_injected_synthetic_dyslexia.

This estimate of the percent of distressed housing units in each Census Tract was prepared using data from the American Community Survey and the Allegheny County Property Assessment database. The estimate was produced by the Reinvestment Fund in their work with the Allegheny County Department of Economic Development.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

The public cloud storage service market is experiencing robust growth, driven by the increasing adoption of cloud computing across various sectors. The market's expansion is fueled by several key factors, including the escalating demand for data storage and management solutions, the rising need for scalability and flexibility in IT infrastructure, and the increasing preference for cost-effective and efficient storage options. Businesses across BFSI, education, manufacturing, telecom & IT, and other industries are increasingly migrating their data to the cloud, contributing significantly to the market's expansion. The strong growth is further propelled by advancements in technologies like object storage, which offers highly scalable and cost-effective solutions for handling massive datasets. The dominance of major players like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) is shaping the market landscape, with these companies continuously innovating and expanding their service offerings to cater to diverse customer needs. Competition is intense, driving continuous improvements in performance, security, and pricing. Regional variations in market growth exist, with North America and Asia Pacific currently leading in terms of adoption and market size due to robust technological infrastructure and high digitalization rates. However, growth is expected across all regions as cloud adoption matures globally. The diverse range of application types, including web services APIs and thin client applications, further contributes to the market's complexity and opportunities for specialized solutions. While the precise market size for 2025 is not provided, a reasonable estimation can be made considering industry reports. Assuming a current market size of approximately $150 billion (a conservative estimate given the scale of the cloud market), and a hypothetical CAGR of 15% (common for fast-growing tech sectors), the market size in 2025 could be estimated at around $250 billion. The forecast period (2025-2033) suggests continued expansion, driven by ongoing technological advancements and increasing digital transformation initiatives worldwide. This continued expansion will likely see the market surpass several trillion dollars in value by 2033. Challenges such as data security concerns and regulatory compliance requirements remain key restraining factors, though these are increasingly addressed through enhanced security protocols and robust compliance frameworks offered by leading cloud providers.

Median employee, employer and total contribution rates for employees eligible for automatic enrolment in the public, private and all sectors.

The report contains a forecast of the average quarter-hourly system imbalance in the next quarter hour as well as an estimated probability distribution of the average quarter-hourly system imbalance in the next quarter hour. The data reflects Elia's own forecasts of the system imbalance. It must be noted that these forecasts can have a significant error margin, are not binding for Elia and are therefore merely shared for informational purposes and that under no circumstances the publication or the use of this information imply a shift in responsibility or liability towards Elia.

The Big Data Analytics in Banking market is experiencing robust growth, projected to reach $8.58 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 23.11%. This expansion is fueled by several key factors. The increasing need for enhanced customer experience personalization, driven by the vast amounts of customer data generated through digital banking channels, is a major driver. Furthermore, the imperative to detect and prevent fraudulent activities, comply with stringent regulatory requirements, and optimize risk management strategies is pushing banks to invest heavily in advanced analytics solutions. Data discovery and visualization (DDV) tools are currently leading the market, providing banks with the capability to gain actionable insights from complex datasets. However, the adoption of advanced analytics (AA) is rapidly increasing, offering more sophisticated predictive modeling and AI-powered capabilities for improved decision-making. Geographically, North America is expected to maintain a significant market share, given the early adoption of Big Data technologies and the presence of major technology providers. However, rapid digitalization in regions like Asia-Pacific and increasing investments in financial technology are fostering significant growth opportunities in these markets. The competitive landscape is characterized by a mix of established technology vendors like IBM and Oracle, alongside specialized Big Data analytics firms and niche players focusing on specific banking needs. The market's ongoing growth trajectory is further cemented by the increasing availability of cloud-based solutions, which offer scalability and cost-effectiveness. The continued expansion of this market is anticipated to be driven by several factors, including the increasing adoption of open banking initiatives fostering data sharing and collaboration, the proliferation of Internet of Things (IoT) devices generating further banking-relevant data, and the growing adoption of cloud-based infrastructure for data processing and storage. While the market shows strong prospects, challenges remain, including concerns surrounding data security and privacy, the need for skilled professionals to manage and interpret complex datasets, and the high initial investment costs associated with implementing big data analytics solutions. Nevertheless, the potential for improved operational efficiency, enhanced customer engagement, and reduced risk will continue to propel market growth throughout the forecast period (2025-2033). Recent developments include: March 2023 - Alteryx has declared that it had successfully earned the Google Cloud Ready - AlloyDB Designation. Customers may access data from various databases using Alteryx's growing library of connectors, enabling them to use more data than ever before. Cloud Ready - AlloyDB is a new moniker for the products offered by Google Cloud's technology partners that interact with AlloyDB. By receiving this recognition, Alteryx has worked closely with Google Cloud to incorporate support for AlloyDB into its solutions and fine-tune its current capabilities for the best results., January 2023 - Aspire Systems has announced its rise to the AWS Advanced Consulting Partner tier, where partnership lets Aspire bolster its cloud solutions with AWS resources to support government and space agencies, leaders in education, and nonprofits. Using the resources gleaned from the much sought-after APN Immersion Days, Aspire provides exclusive, state-of-the-art AWS solutions to its customers.. Key drivers for this market are: Enforcement of Government Initiatives, Risk Management and Internal Controls Across the Bank to Witness the Growth; Increasing Volume of Data Generated by Banks. Potential restraints include: 7.1 Lack of General Awareness And Expertise7.2 Data Security Concerns. Notable trends are: Risk Management and Internal Controls Across the Bank to Witness the Growth.