Target revenue for the twelve months ending April 30, 2025 was $105.881B, a 0.69% decline year-over-year. Target annual revenue for 2025 was $106.566B, a 0.79% decline from 2024. Target annual revenue for 2024 was $107.412B, a 1.57% decline from 2023. Target annual revenue for 2023 was $109.12B, a 2.94% increase from 2022.

In 2024, Target Corporation's food and beverage product segment generated sales of approximately 23.8 billion U.S. dollars. In contrast, the hardline segment, which include electronics, toys, entertainment, sporting goods, and luggage, registered sales of 15.8 billion U.S. dollars. Target Corporation had revenues amounting to around 106.6 billion U.S. dollars that year.

This dataset tracks annual total revenue from 1990 to 2021 for Target Range Elementary School District

This dataset tracks annual average revenue per student from 1995 to 2021 for Target Range Elementary School District

On Premises Real Time Database Market Outlook

The global market size for on-premises real-time database solutions was estimated at USD 12.3 billion in 2023, and it is projected to reach USD 25.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.6% during the forecast period. This growth is driven by several factors, including the increasing need for efficient data management and real-time data analytics capabilities across various industry verticals such as BFSI, healthcare, retail, and manufacturing.

One of the primary growth factors for the on-premises real-time database market is the increasing volume of data generated by organizations. With the proliferation of IoT devices, social media platforms, and e-commerce activities, the amount of data generated is growing exponentially. Organizations are increasingly looking for robust database solutions that can handle real-time data processing and analytics to gain actionable insights and maintain a competitive edge. This trend is particularly evident in sectors like retail and manufacturing, where real-time data can significantly enhance operational efficiency and customer experience.

Another significant growth driver is the need for enhanced data security and compliance. While cloud-based solutions offer scalability and flexibility, many organizations, particularly in the BFSI and healthcare sectors, prefer on-premises databases due to stringent data security and compliance requirements. On-premises solutions provide organizations with greater control over their data, allowing them to implement tailored security measures and ensure compliance with industry-specific regulations such as GDPR, HIPAA, and others. This increased focus on data security is likely to continue driving the demand for on-premises real-time database solutions.

The technological advancements in database management systems are also propelling market growth. Innovations such as in-memory databases, multi-model databases, and enhanced query processing capabilities are enabling organizations to achieve faster data retrieval and improved performance. Additionally, the integration of artificial intelligence and machine learning algorithms in database systems is providing advanced analytics capabilities, further enhancing the value proposition of on-premises real-time databases. These technological advancements are expected to attract more organizations to invest in on-premises solutions.

Operational Database Management System (ODBMS) plays a pivotal role in the landscape of on-premises real-time databases. These systems are designed to handle a wide array of data management tasks, including transaction processing, data retrieval, and storage management, all in real-time. The efficiency of an ODBMS is crucial for businesses that require immediate access to data to make timely decisions. In sectors like finance and healthcare, where data accuracy and speed are paramount, the implementation of a robust ODBMS ensures that organizations can maintain high performance and reliability. Furthermore, with the integration of advanced features such as in-memory processing and multi-model support, ODBMS solutions are becoming increasingly sophisticated, offering enhanced capabilities to meet the growing demands of modern enterprises.

Regionally, North America holds the largest market share due to the early adoption of advanced technologies and the presence of major industry players. The region's strong emphasis on data security and regulatory compliance also supports the adoption of on-premises solutions. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the rapid digital transformation initiatives, increasing IT investments, and the growing importance of real-time data analytics in emerging economies such as China and India.

Component Analysis

When analyzing the on-premises real-time database market by component, it is essential to consider the three main segments: software, hardware, and services. The software component, which includes database management systems and related applications, is the largest segment. This dominance is due to the critical role that software plays in managing, storing, and analyzing real-time data. Organizations are continually seeking advanced software solutions that offer enhanced performance, reliability, and scalability. Innovations in database software, such as in-memory processing and multi-model datab

Annual total count and aggregate earnings of interjurisdictional employees for the provinces and territories by province of residence or employment. Estimates are available from 2002 to 2021.

URL: https://geoscience.data.qld.gov.au/dataset/cr107254

EPM 25546, MOUNT TARGET, ANNUAL REPORT FOR PERIOD ENDING 16/4/2018

Statistical information on population employment by age group, gender and place of residence has been provided. The information shall be provided quarterly from 1998. Survey population shall mean all permanent residents of Lithuania. “Statistical observation unit” means a private household and its member. The definition of the study is occupied residents, persons who do any work, receive remuneration for it in cash or in kind, or who have income or profit from it. The population occupied by the study shall be those aged 15 to 89 who, during the reference week, worked for at least one hour in any work for which they received cash wages or wages in kind or from which they had profit or income, as well as residents who had a job but were temporarily absent during the reference week.

URL: https://geoscience.data.qld.gov.au/dataset/cr096729

EPM 25546, MOUNT TARGET, ANNUAL REPORT FOR PERIOD ENDING 17/4/2016

See notice below about this dataset

This dataset provides the average earnings by student group per district.  Wage records are obtained from the Massachusetts Department of Unemployment Assistance (DUA) using a secure, anonymized matching process with limitations. For details on the process and suppression rules, please visit the Employment and Earnings of High School Graduates dashboard.

This dataset is one of three containing the same data that is also published in the Employment and Earnings of High School Graduates dashboard: Average Earnings by Student Group Average Earnings by Industry College and Career Outcomes

2025 Update on DESE Data on Employment and Earnings 

The data link between high school graduates and future earnings makes it possible to follow students beyond high school and college into the workforce, enabling long-term evaluation of educational programs using workforce outcomes.

While DESE has published these data in the past, as of June 2025 we are temporarily pausing updates due to an issue conducting the link that was brought to our attention in 2023 by a team of researchers. The issue impacts the earnings information for students who never attended a postsecondary institution or who only attended private or out-of-state colleges or universities, beginning with the 2017 high school graduation cohort, with growing impact in each successive high school graduation cohort.

The issue does not impact the earnings information for students who attended a Massachusetts public institution of higher education, and earnings data for those students will continue to be updated.

Once a solution is found, the past cohorts of data with low match rates will be updated. DESE and partner agencies are exploring linking strategies to maximize the utility of the information.

More detailed information can be found in the attached memo provided by the research team from the Annenberg Institute. We thank them for calling this issue to our attention.

With over 3 million businesses, all held in-house, BoldData has the largest supply of Dutch data. BoldData delivers custom made database based on your companies target audience. Our data experts select your perfect target based on numerous interesting selections: from 3,000 industries to region, turnover, sector, contact person and the number of employees. Simply tell your target and we provide you with a custom-made database.

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Annual total count and aggregate earnings of inter-jurisdictional employees for the provinces and territories by province of residence or employment. Estimates are available from 2002 to 2019.

Key Table Information.Table Title.Percentage Distribution of Public Elementary-Secondary School System Revenue by Source: U.S. and State: 2012 - 2023.Table ID.GOVSTIMESERIES.GS00SS03.Survey/Program.Public Sector.Year.2024.Dataset.PUB Public Sector Annual Surveys and Census of Governments.Source.U.S. Census Bureau, Public Sector.Release Date.2025-05-01.Release Schedule.The Annual Survey of School System Finances occurs every year. Data are typically released in early May. There are approximately two years between the reference period and data release..Dataset Universe.Census of Governments - Organization (CG):The universe of this file is all federal, state, and local government units in the United States. In addition to the federal government and the 50 state governments, the Census Bureau recognizes five basic types of local governments. The government types are: County, Municipal, Township, Special District, and School District. Of these five types, three are categorized as General Purpose governments: County, municipal, and township governments are readily recognized and generally present no serious problem of classification. However, legislative provisions for school district and special district governments are diverse. These two types are categorized as Special Purpose governments. Numerous single-function and multiple-function districts, authorities, commissions, boards, and other entities, which have varying degrees of autonomy, exist in the United States. The basic pattern of these entities varies widely from state to state. Moreover, various classes of local governments within a particular state also differ in their characteristics. Refer to the Individual State Descriptions report for an overview of all government entities authorized by state.The Public Use File provides a listing of all independent government units, and dependent school districts active as of fiscal year ending June 30, 2024. The Annual Surveys of Public Employment & Payroll (EP) and State and Local Government Finances (LF):The target population consists of all 50 state governments, the District of Columbia, and a sample of local governmental units (counties, cities, townships, special districts, school districts). In years ending in '2' and '7' the entire universe is canvassed. In intervening years, a sample of the target population is surveyed. Additional details on sampling are available in the survey methodology descriptions for those years.The Annual Survey of Public Pensions (PP):The target population consists of state- and locally-administered defined benefit funds and systems of all 50 state governments, the District of Columbia, and a sample of local governmental units (counties, cities, townships, special districts, school districts). In years ending in '2' and '7' the entire universe is canvassed. In intervening years, a sample of the target population is surveyed. Additional details on sampling are available in the survey methodology descriptions for those years.The Annual Surveys of State Government Finance (SG) and State Government Tax Collections (TC):The target population consists of all 50 state governments. No local governments are included. For the purpose of Census Bureau statistics, the term "state government" refers not only to the executive, legislative, and judicial branches of a given state, but it also includes agencies, institutions, commissions, and public authorities that operate separately or somewhat autonomously from the central state government but where the state government maintains administrative or fiscal control over their activities as defined by the Census Bureau. Additional details are available in the survey methodology description.The Annual Survey of School System Finances (SS):The Annual Survey of School System Finances targets all public school systems providing elementary and/or secondary education in all 50 states and the District of Columbia..Methodology.Data Items and Other Identifying Records.Total percentage distribution of revenuePercentage distribution of revenue - Revenue from federal sources - Total Percentage distribution of revenue - Revenue from federal sources - Title IPercentage distribution of revenue - Revenue from state sources - TotalPercentage distribution of revenue - Revenue from state sources - General formula assistancePercentage distribution of revenue - Revenue from local sources - TotalPercentage distribution of revenue - Revenue from local sources - Taxes and parent government contributionsPercentage distribution of revenue - Revenue from local sources - Other local governmentsPercentage distribution of revenue - Revenue from local sources - Current chargesDefinitions can be found by clicking on the column header in the table or by accessing the Glossary.For detailed information, see Government Finance and Employment Classification Manual..Unit(s) of Observation.The basic reporting unit is the governmental unit, defined as an organized entity which in a...

Key Table Information.Table Title.Revenue of Public Elementary-Secondary School Systems in the United States: Fiscal Year 2012 - 2023.Table ID.GOVSTIMESERIES.GS00SS12.Survey/Program.Public Sector.Year.2024.Dataset.PUB Public Sector Annual Surveys and Census of Governments.Source.U.S. Census Bureau, Public Sector.Release Date.2025-05-01.Release Schedule.The Annual Survey of School System Finances occurs every year. Data are typically released in early May. There are approximately two years between the reference period and data release..Dataset Universe.Census of Governments - Organization (CG):The universe of this file is all federal, state, and local government units in the United States. In addition to the federal government and the 50 state governments, the Census Bureau recognizes five basic types of local governments. The government types are: County, Municipal, Township, Special District, and School District. Of these five types, three are categorized as General Purpose governments: County, municipal, and township governments are readily recognized and generally present no serious problem of classification. However, legislative provisions for school district and special district governments are diverse. These two types are categorized as Special Purpose governments. Numerous single-function and multiple-function districts, authorities, commissions, boards, and other entities, which have varying degrees of autonomy, exist in the United States. The basic pattern of these entities varies widely from state to state. Moreover, various classes of local governments within a particular state also differ in their characteristics. Refer to the Individual State Descriptions report for an overview of all government entities authorized by state.The Public Use File provides a listing of all independent government units, and dependent school districts active as of fiscal year ending June 30, 2024. The Annual Surveys of Public Employment & Payroll (EP) and State and Local Government Finances (LF):The target population consists of all 50 state governments, the District of Columbia, and a sample of local governmental units (counties, cities, townships, special districts, school districts). In years ending in '2' and '7' the entire universe is canvassed. In intervening years, a sample of the target population is surveyed. Additional details on sampling are available in the survey methodology descriptions for those years.The Annual Survey of Public Pensions (PP):The target population consists of state- and locally-administered defined benefit funds and systems of all 50 state governments, the District of Columbia, and a sample of local governmental units (counties, cities, townships, special districts, school districts). In years ending in '2' and '7' the entire universe is canvassed. In intervening years, a sample of the target population is surveyed. Additional details on sampling are available in the survey methodology descriptions for those years.The Annual Surveys of State Government Finance (SG) and State Government Tax Collections (TC):The target population consists of all 50 state governments. No local governments are included. For the purpose of Census Bureau statistics, the term "state government" refers not only to the executive, legislative, and judicial branches of a given state, but it also includes agencies, institutions, commissions, and public authorities that operate separately or somewhat autonomously from the central state government but where the state government maintains administrative or fiscal control over their activities as defined by the Census Bureau. Additional details are available in the survey methodology description.The Annual Survey of School System Finances (SS):The Annual Survey of School System Finances targets all public school systems providing elementary and/or secondary education in all 50 states and the District of Columbia..Methodology.Data Items and Other Identifying Records.Fall enrollmentTotal revenueTotal revenue from federal sourcesRevenue from federal sources - Distributed through the state - Title IRevenue from federal sources - Distributed through the state - Special EducationRevenue from federal sources - Distributed through the state - Child nutritionRevenue from federal sources - Distributed through the state - Other and nonspecifiedTotal revenue from state sourcesRevenue from state sources - General formula assistanceRevenue from state sources - Special educationRevenue from state sources - Transportation programsRevenue from state sources - Other and nonspecified state aidTotal revenue from local sourcesRevenue from local sources - Total taxesRevenue from local sources - Property taxesRevenue from local sources - Parent government contributionsRevenue from local sources - Revenue from cities and countiesRevenue from local sources - Revenue from other school systemsRevenue from local sources - Current chargesRevenue from local sources - Other local revenueDefinit...

Lending Club offers peer-to-peer (P2P) loans through a technological platform for various personal finance purposes and is today one of the companies that dominate the US P2P lending market. The original dataset is publicly available on Kaggle and corresponds to all the loans issued by Lending Club between 2007 and 2018. The present version of the dataset is for constructing a granting model, that is, a model designed to make decisions on whether to grant a loan based on information available at the time of the loan application. Consequently, our dataset only has a selection of variables from the original one, which are the variables known at the moment the loan request is made. Furthermore, the target variable of a granting model represents the final status of the loan, that are "default" or "fully paid". Thus, we filtered out from the original dataset all the loans in transitory states. Our dataset comprises 1,347,681 records or obligations (approximately 60% of the original) and it was also cleaned for completeness and consistency (less than 1% of our dataset was filtered out).

TARGET VARIABLE

The dataset includes a target variable based on the final resolution of the credit: the default category corresponds to the event charged off and the non-default category to the event fully paid. It does not consider other values in the loan status variable since this variable represents the state of the loan at the end of the considered time window. Thus, there are no loans in transitory states. The original dataset includes the target variable “loan status”, which contains several categories ('Fully Paid', 'Current', 'Charged Off', 'In Grace Period', 'Late (31-120 days)', 'Late (16-30 days)', 'Default'). However, in our dataset, we just consider loans that are either “Fully Paid” or “Default” and transform this variable into a binary variable called “Default”, with a 0 for fully paid loans and a 1 for defaulted loans.

EXPLANATORY VARIABLES

The explanatory variables that we use correspond only to the information available at the time of the application. Variables such as the interest rate, grade, or subgrade are generated by the company as a result of a credit risk assessment process, so they were filtered out from the dataset as they must not be considered in risk models to predict the default in granting of credit.

FULL LIST OF VARIABLES

Loan identification variables:

id: Loan id (unique identifier).

issue_d: Month and year in which the loan was approved.

Quantitative variables:

revenue: Borrower's self-declared annual income during registration.

dti_n: Indebtedness ratio for obligations excluding mortgage. Monthly information. This ratio has been calculated considering the indebtedness of the whole group of applicants. It is estimated as the ratio calculated using the co-borrowers’ total payments on the total debt obligations divided by the co-borrowers’ combined monthly income.

loan_amnt: Amount of credit requested by the borrower.

fico_n: Defined between 300 and 850, reported by Fair Isaac Corporation as a risk measure based on historical credit information reported at the time of application. This value has been calculated as the average of the variables “fico_range_low” and “fico_range_high” in the original dataset.

experience_c: Binary variable that indicates whether the borrower is new to the entity. This variable is constructed from the credit date of the previous obligation in LC and the credit date of the current obligation; if the difference between dates is positive, it is not considered as a new experience with LC.

Categorical variables:

emp_length: Categorical variable with the employment length of the borrower (includes the no information category)

purpose: Credit purpose category for the loan request.

home_ownership_n: Homeownership status provided by the borrower in the registration process. Categories defined by LC: “mortgage”, “rent”, “own”, “other”, “any”, “none”. We merged the categories “other”, “any” and “none” as “other”.

addr_state: Borrower's residence state from the USA.

zip_code: Zip code of the borrower's residence.

Textual variables

title: Title of the credit request description provided by the borrower.

desc: Description of the credit request provided by the borrower.

We cleaned the textual variables. First, we removed all those descriptions that contained the default description provided by Lending Club on its web form (“Tell your story. What is your loan for?”). Moreover, we removed the prefix “Borrower added on DD/MM/YYYY >” from the descriptions to avoid any temporal background on them. Finally, as these descriptions came from a web form, we substituted all the HTML elements by their character (e.g. “&” was substituted by “&”, “<” was substituted by “<”, etc.).

RELATED WORKS

This dataset has been used in the following academic articles:

Sanz-Guerrero, M. Arroyo, J. (2024). Credit Risk Meets Large Language Models: Building a Risk Indicator from Loan Descriptions in P2P Lending. arXiv preprint arXiv:2401.16458. https://doi.org/10.48550/arXiv.2401.16458

Ariza-Garzón, M.J., Arroyo, J., Caparrini, A., Segovia-Vargas, M.J. (2020). Explainability of a machine learning granting scoring model in peer-to-peer lending. IEEE Access 8, 64873 - 64890. https://doi.org/10.1109/ACCESS.2020.2984412

URL: https://geoscience.data.qld.gov.au/dataset/cr020275

EPM 5320, TARGET HILL, ELLENVALE TROUGH PROJECT, ANNUAL REPORT FOR PERIOD ENDING 12/4/1989

Tempe is among Arizona's most educated cities, lending to a creative, smart atmosphere. With more than a dozen colleges, trade schools and universities, about 40 percent of our residents over the age of 25 have Bachelor's degrees or higher. Having such an educated and accessible workforce is a driving factor in attracting and growing jobs for residents in the region.The City of Tempe is a member of the Greater Phoenix Economic Council (GPEC) and with the membership staff tracks collaborative efforts to recruit business prospects and locates. The Greater Phoenix Economic Council (GPEC) is a performance-driven, public-private partnership. GPEC partners with the City of Tempe, Maricopa County, 22 other communities and more than 170 private-sector investors to promote the region’s competitive position and attract quality jobs that enable strategic economic growth and provide increased tax revenue for Tempe.This dataset provides the target and actual job creation numbers for the City of Tempe and Greater Phoenix Economic Council (GPEC). The job creation target for Tempe is calculated by multiplying GPEC's target by twice Tempe's proportion of the population.This page provides data for the New Jobs Created performance measure.The performance measure dashboard is available at 5.02 New Jobs Created.Additional InformationSource:Contact: Madalaine McConvilleContact Phone: 480-350-2927Data Source Type: Excel filesPreparation Method: Extracted from GPEC monthly and annual reports and proprietary excel filesPublish Frequency: AnnuallyPublish Method: ManualData Dictionary

This data set contains frequency counts of target words in 16 million news and opinion articles from 10 popular news media outlets in the United Kingdom. The target words are listed in the associated report and are mostly words that denote prejudice or are often associated with social justice discourse. A few additional words not denoting prejudice are also available since they are used in the report for illustration purposes of the method.

The textual content of news and opinion articles from the outlets is available in the outlet's online domains and/or public cache repositories such as Google cache (https://webcache.googleusercontent.com), The Internet Wayback Machine (https://archive.org/web/web.php), and Common Crawl (https://commoncrawl.org). We used derived word frequency counts from these sources. Textual content included in our analysis is circumscribed to articles headlines and main body of text of the articles and does not include other article elements such as figure captions.

Targeted textual content was located in HTML raw data using outlet specific xpath expressions. Tokens were lowercased prior to estimating frequency counts. To prevent outlets with sparse text content for a year from distorting aggregate frequency counts, we only include outlet frequency counts from years for which there is at least 1 million words of article content from an outlet. This threshold was chosen to maximize inclusion in our analysis of outlets with sparse amounts of articles text per year.

Yearly frequency usage of a target word in an outlet in any given year was estimated by dividing the total number of occurrences of the target word in all articles of a given year by the number of all words in all articles of that year. This method of estimating frequency accounts for variable volume of total article output over time.

In a small percentage of articles, outlet specific XPath expressions might fail to properly capture the content of the article due to the heterogeneity of HTML elements and CSS styling combinations with which articles text content is arranged in outlets online domains. As a result, the total and target word counts metrics for a small subset of articles are not precise. In a random sample of articles and outlets, manual estimation of target words counts overlapped with the automatically derived counts for over 90% of the articles.

Most of the incorrect frequency counts are often minor deviations from the actual counts such as for instance counting the word "Facebook" in an article footnote encouraging article readers to follow the journalist’s Facebook profile and that the XPath expression mistakenly included as the content of the article main text.To conclude, in a data analysis of over 16 million articles, we cannot manually check the correctness of frequency counts for every single article and hundred percent accuracy at capturing articles’ content is elusive due to the small number of difficult to detect boundary cases such as incorrect HTML markup syntax in online domains. Overall however, we are confident that our frequency metrics are representative of word prevalence in print news media content (see Figure 2 of main manuscript for supporting evidence of the temporal precision of the method).

See notice below about this dataset

This dataset provides the number of graduates who enrolled in each type of postsecondary education per district.

Wage records are obtained from the Massachusetts Department of Unemployment Assistance (DUA) using a secure, anonymized matching process with limitations. For details on the process and suppression rules, please visit the Employment and Earnings of High School Graduates dashboard.

This dataset is one of three containing the same data that is also published in the Employment and Earnings of High School Graduates dashboard: Average Earnings by Student Group Average Earnings by Industry College and Career Outcomes

List of Outcomes

• Total Postsecondary Enrollment
• In-State Public 2-Year
• In-State Public 4-Year
• In-State Private
• Out-of-State
• Total Employed
• Total Missing

The data link between high school graduates and future earnings makes it possible to follow students beyond high school and college into the workforce, enabling long-term evaluation of educational programs using workforce outcomes.

While DESE has published these data in the past, as of June 2025 we are temporarily pausing updates due to an issue conducting the link that was brought to our attention in 2023 by a team of researchers. The issue impacts the earnings information for students who never attended a postsecondary institution or who only attended private or out-of-state colleges or universities, beginning with the 2017 high school graduation cohort, with growing impact in each successive high school graduation cohort.

The issue does not impact the earnings information for students who attended a Massachusetts public institution of higher education, and earnings data for those students will continue to be updated.

Once a solution is found, the past cohorts of data with low match rates will be updated. DESE and partner agencies are exploring linking strategies to maximize the utility of the information.

More detailed information can be found in the attached memo provided by the research team from the Annenberg Institute. We thank them for calling this issue to our attention.