As of June 2024, the most popular commercial database management system (DBMS) in the world was Oracle, with a ranking score of 1244. MySQL was the most popular open source DBMS at that time, with a ranking score of 1061.

As of June 2024, the most popular database management system (DBMS) worldwide was Oracle, with a ranking score of 1244.08; MySQL and Microsoft SQL server rounded out the top three. Although the database management industry contains some of the largest companies in the tech industry, such as Microsoft, Oracle and IBM, a number of free and open-source DBMSs such as PostgreSQL and MariaDB remain competitive. Database Management Systems As the name implies, DBMSs provide a platform through which developers can organize, update, and control large databases. Given the business world’s growing focus on big data and data analytics, knowledge of SQL programming languages has become an important asset for software developers around the world, and database management skills are seen as highly desirable. In addition to providing developers with the tools needed to operate databases, DBMS are also integral to the way that consumers access information through applications, which further illustrates the importance of the software.

With 56 Million Businesses in the United States of America, Techsalerator has access to the highest B2B count of Data/ Business Data in the country.

Thanks to our unique tools and large data specialist team, we are able to select the ideal targeted dataset based on the unique elements such as sales volume of a company, the company's location, no. of employees etc...

Whether you are looking for an entire fill install, access to our API's or if you are just looking for a one-time targeted purchase, get in touch with our company and we will fulfill your international data need.

We cover all states and cities in the country : Example covered.

All states :

Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho IllinoisIndiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri MontanaNebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon PennsylvaniaRhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

A few cities : New York City NY Los Angeles CA Chicago IL Houston TX Phoenix AZ Philadelphia PA San Antonio TX San Diego CA Dallas TX Austin TX San Jose CA Fort Worth TX Jacksonville FL Columbus OH Charlotte NC Indianapolis IN San Francisco CA Seattle WA Denver CO Washington DC Boston MA El Paso TX Nashville TN Oklahoma City OK Las Vegas NV Detroit MI Portland OR Memphis TN Louisville KY Milwaukee WI Baltimore MD Albuquerque NM Tucson AZ Mesa AZ Fresno CA Sacramento CA Atlanta GA Kansas City MO Colorado Springs CO Raleigh NC Omaha NE Miami FL Long Beach CA Virginia Beach VA Oakland CA Minneapolis MN Tampa FL Tulsa OK Arlington TX Wichita KS Bakersfield CA Aurora CO New Orleans LA Cleveland OH Anaheim CA Henderson NV Honolulu HI Riverside CA Santa Ana CA Corpus Christi TX Lexington KY San Juan PR Stockton CA St. Paul MN Cincinnati OH Greensboro NC Pittsburgh PA Irvine CA St. Louis MO Lincoln NE Orlando FL Durham NC Plano TX Anchorage AK Newark NJ Chula Vista CA Fort Wayne IN Chandler AZ Toledo OH St. Petersburg FL Reno NV Laredo TX Scottsdale AZ North Las Vegas NV Lubbock TX Madison WI Gilbert AZ Jersey City NJ Glendale AZ Buffalo NY Winston-Salem NC Chesapeake VA Fremont CA Norfolk VA Irving TX Garland TX Paradise NV Arlington VA Richmond VA Hialeah FL Boise ID Spokane WA Frisco TX Moreno Valley CA Tacoma WA Fontana CA Modesto CA Baton Rouge LA Port St. Lucie FL San Bernardino CA McKinney TX Fayetteville NC Santa Clarita CA Des Moines IA Oxnard CA Birmingham AL Spring Valley NV Huntsville AL Rochester NY Cape Coral FL Tempe AZ Grand Rapids MI Yonkers NY Overland Park KS Salt Lake City UT Amarillo TX Augusta GA Columbus GA Tallahassee FL Montgomery AL Huntington Beach CA Akron OH Little Rock AR Glendale CA Grand Prairie TX Aurora IL Sunrise Manor NV Ontario CA Sioux Falls SD Knoxville TN Vancouver WA Mobile AL Worcester MA Chattanooga TN Brownsville TX Peoria AZ Fort Lauderdale FL Shreveport LA Newport News VA Providence RI Elk Grove CA Rancho Cucamonga CA Salem OR Pembroke Pines FL Santa Rosa CA Eugene OR Oceanside CA Cary NC Fort Collins CO Corona CA Enterprise NV Garden Grove CA Springfield MO Clarksville TN Bayamon PR Lakewood CO Alexandria VA Hayward CA Murfreesboro TN Killeen TX Hollywood FL Lancaster CA Salinas CA Jackson MS Midland TX Macon County GA Kansas City KS Palmdale CA Sunnyvale CA Springfield MA Escondido CA Pomona CA Bellevue WA Surprise AZ Naperville IL Pasadena TX Denton TX Roseville CA Joliet IL Thornton CO McAllen TX Paterson NJ Rockford IL Carrollton TX Bridgeport CT Miramar FL Round Rock TX Metairie LA Olathe KS Waco TX

The Commercial Building Inventories provide modeled data on commercial building type, vintage, and area for each U.S. city and county. Please note this data is modeled and more precise data may be available through county assessors or other sources. Commercial building stock data is estimated using CoStar Realty Information, Inc. building stock data.

This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and builds on Cities-LEAP energy modeling, available at the "EERE Cities-LEAP Page" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

Abstract

We share data, codes and questionnaires for the replication of the paper "Estimating the Demand for Business Training: Evidence from Jamaica." The study conducted two experiments in Jamaica using the Becker-DeGroot-Marschak mechanism and take-it-or-leave-it offers to estimate the demand for training. We found that most entrepreneurs have positive willingness to pay for training, but demand falls sharply as price increases. Offering the chance to pay in installments does not increase demand. Higher prices screen out poorer, less educated entrepreneurs with smaller firms. However, charging a higher price does increase attendance among those who pay. Finally, the paper points to the limitations of using a BDM mechanism in a context of low contract enforcement, and when payments for purchasing an intangible service do not occur immediately.

Geographic coverage

Around the capital city of Kingston; Western regions of Jamaica, including the second-largest city in Jamaica, Montego Bay

Analysis unit

Entrepreneurs Firms

Kind of data

Sample survey data [ssd]

Sampling procedure

We selected firms to take part in the BDM elicitation method from the Western regions of Jamaica, which includes the second-largest city in Jamaica, Montego Bay. While we recruited firms for the TIOLI approach, mainly around the capital city of Kingston (72%), with the remainder from Montego Bay and surrounding parishes. A variety of communication methods were used to contact entrepreneurs. These included emails to the client database of the organization providing the training; advertisements via social media, newspaper and radio; and messages from other firm support organizations. Entrepreneurs were asked to take a short baseline survey by phone, giving 1,782 eligible entrepreneurs who were then invited to come to demonstration sessions. 457 entrepreneurs came to demonstration sessions and completed the BDM elicitation method (BDM sample), and 374 entrepreneurs came to demonstration sessions and received take-it-or-leave-it offers (TIOLI sample). The dataset includes this final set of 831 observations.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The following instruments were used for data collection, and they are provided for download as related materials: Baseline Survey Follow-up Survey Willingness to Pay Example (for TIOLI and BDM)

What information do you include in your sales and marketing database?

When you buy one of our sales and marketing databases, all records we supply to you hold the below information (where it is available):

-Company name -Address (including town, county and postcode) -Phone number (including area code) -Website address -Company registration number and date of incorporation -Financial information (for example, net worth, turnover band, profit and loss, profit percentage change, sales increase or decrease) -Senior decision maker name -Senior decision maker email address (if we do not have this information, we will provide the next best email for example, a departmental email address) -Top-level market sector and granular industry classification -Standard Industrial Classification (SIC) code -Number of employees (both onsite and nationally) -Premise type and description -Number of branches -Legal status (for example, sole trader, partnership, private limited company)

Why use Metric Central?

There are several marketing database providers out there, but these are the reasons why you should choose us for your prospecting requirements.

The most up to date information on the market: We refresh our database every day, meaning when you buy from us, you will have the most up to date and accurate details, without having to worry about database decay, duplicated records

Reach out directly to the key decision-maker: Unlike some lists which will only give you a generic company email address, we will provide you with the name and details of the company’s key decision-maker, meaning you can directly reach out to the person with purchasing power, without being held back by the company gatekeeper 100% GDPR compliance: All of our records come with a full General Data Protection Regulation (GDPR) compliance audit trail, meaning you can reach out to prospects with the peace of mind you are compliant with the latest legislation

Buy online and use straightaway: With a lot of companies providing a similar product to ours, you have to reach out to them for a quote before you receive your product. Our sales and marketing database can be bought online and downloaded straight away, meaning you can start reaching out to decision-makers the very same day you buy. We even tell you how many companies your data will contain before you commit to purchasing!

A wide range of contacts: We have contact details for prospective customers across a wide range of different industries meaning no matter what your product or service is, we have your needs covered. Check out our website for a full list of databases we can provide CRM ready data: Our list can be uploaded straight into your CRM system of choice, meaning your sales team can start reaching out to prospective customers immediately.

Competitive pricing: We’re one of the most cost-effective companies on the market, combining a high-quality product with an accessible price point. Decades of experience: With several years of experience in the sales agency, our team at Metric Central is well-equipped to understand your pain points and provide you with the data you need to help you sell your product or service

If you would like to know more about the data services we offer and how we can help you grow your business, please don’t hesitate to get in touch with us today, and one of our experienced team reach out to you with the information you need.

Please note: All data is supplied under legitimate interest. The term of supply is as standard of a 12-month multi licence agreement which allows unlimited postal and telephone communication with 12 cold email sends.

Company Datasets for valuable business insights!

Discover new business prospects, identify investment opportunities, track competitor performance, and streamline your sales efforts with comprehensive Company Datasets.

These datasets are sourced from top industry providers, ensuring you have access to high-quality information:

• Owler: Gain valuable business insights and competitive intelligence. -AngelList: Receive fresh startup data transformed into actionable insights. -CrunchBase: Access clean, parsed, and ready-to-use business data from private and public companies. -Craft.co: Make data-informed business decisions with Craft.co's company datasets. -Product Hunt: Harness the Product Hunt dataset, a leader in curating the best new products.

We provide fresh and ready-to-use company data, eliminating the need for complex scraping and parsing. Our data includes crucial details such as:

• Company name;
• Size;
• Founding date;
• Location;
• Industry;
• Revenue;
• Employee count;
• Competitors.

You can choose your preferred data delivery method, including various storage options, delivery frequency, and input/output formats.

Receive datasets in CSV, JSON, and other formats, with storage options like AWS S3 and Google Cloud Storage. Opt for one-time, monthly, quarterly, or bi-annual data delivery.

With Oxylabs Datasets, you can count on:

• Fresh and accurate data collected and parsed by our expert web scraping team.
• Time and resource savings, allowing you to focus on data analysis and achieving your business goals.
• A customized approach tailored to your specific business needs.
• Legal compliance in line with GDPR and CCPA standards, thanks to our membership in the Ethical Web Data Collection Initiative.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Unlock the power of data with Oxylabs' Company Datasets and supercharge your business insights today!

These Commercial Victimization Surveys were collected as part of the National Crime Surveys. They document burglary and robbery incidents for all types of commercial establishments, as well as political, cultural, and religious organizations. Business characteristics gathered include form of ownership and operation, size and type of business, and security measures. Information regarding the reported incidents includes time and place, weapon involvement, offender and entry characteristics, injuries and deaths, and type and value of stolen property. Data were collected by calendar quarter for four quarters in 1973-1976 and for two quarters in 1977, while the actual incidents reported in the files reflect those occurring six months prior to the interview date.

Abstract copyright UK Data Service and data collection copyright owner.

The Business Structure Database (BSD) contains a small number of variables for almost all business organisations in the UK. The BSD is derived primarily from the Inter-Departmental Business Register (IDBR), which is a live register of data collected by HM Revenue and Customs via VAT and Pay As You Earn (PAYE) records. The IDBR data are complimented with data from ONS business surveys. If a business is liable for VAT (turnover exceeds the VAT threshold) and/or has at least one member of staff registered for the PAYE tax collection system, then the business will appear on the IDBR (and hence in the BSD). In 2004 it was estimated that the businesses listed on the IDBR accounted for almost 99 per cent of economic activity in the UK. Only very small businesses, such as the self-employed were not found on the IDBR.

The IDBR is frequently updated, and contains confidential information that cannot be accessed by non-civil servants without special permission. However, the ONS Virtual Micro-data Laboratory (VML) created and developed the BSD, which is a 'snapshot' in time of the IDBR, in order to provide a version of the IDBR for research use, taking full account of changes in ownership and restructuring of businesses. The 'snapshot' is taken around April, and the captured point-in-time data are supplied to the VML by the following September. The reporting period is generally the financial year. For example, the 2000 BSD file is produced in September 2000, using data captured from the IDBR in April 2000. The data will reflect the financial year of April 1999 to March 2000. However, the ONS may, during this time, update the IDBR with data on companies from its own business surveys, such as the Annual Business Survey (SN 7451).

The data are divided into 'enterprises' and 'local units'. An enterprise is the overall business organisation. A local unit is a 'plant', such as a factory, shop, branch, etc. In some cases, an enterprise will only have one local unit, and in other cases (such as a bank or supermarket), an enterprise will own many local units.

For each company, data are available on employment, turnover, foreign ownership, and industrial activity based on Standard Industrial Classification (SIC)92, SIC 2003 or SIC 2007. Year of 'birth' (company start-up date) and 'death' (termination date) are also included, as well as postcodes for both enterprises and their local units. Previously only pseudo-anonymised postcodes were available but now all postcodes are real.

The ONS is continually developing the BSD, and so researchers are strongly recommended to read all documentation pertaining to this dataset before using the data.

Linking to Other Business Studies
These data contain IDBR reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

Latest Edition Information
For the sixteenth edition (March 2024), data files and a variable catalogue document for 2023 have been added.

Main Topics:

The following variables are available for enterprises and local units:

• employment (and employees)
• turnover
• Standard Industrial Classification (1992, 2003 and 2007 classifications are available)
• legal status (e.g. sole proprietor, partnership, public corporation, non-profit organisation etc)
• foreign ownership
• birth (company start date)
• death (termination date of trading)
• various geographical variables
  

'Employment' includes business owners, whereas 'employees' measures the number of staff, excluding owners.

Observations for enterprises also include a variable for ownership if the enterprise is part of a large group of companies.

Local units have an additional ‘death code’ variable, which serves as an indicator as to why the plant closed (e.g. as a result of a merger). It should also be noted that there is no turnover information for individual plants. This is because the ONS does not collect financial information at the plant level, which is notoriously difficult, especially for manufacturing plants where often no financial transactions are processed.

The birth and death variables are particularly useful for research, although it should be noted that for businesses that began trading before 1973, their birth date will be set to 1973. This is the year that VAT was introduced in the UK, and hence the first point in time for VAT registration for these companies. Companies that began trading since 1973 have their ‘real’ date of birth listed.

29211 Global import shipment records of Commercial,samples with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

With 6.2 Million Businesses in Spain , Techsalerator has access to the highest B2B count of Data/Business Data in the country.

Thanks to our unique tools and large data specialist team, we are able to select the ideal targeted dataset based on the unique elements such as sales volume of a company, the company's location, no. of employees etc...

Whether you are looking for an entire fill install, access to our API's or if you are just looking for a one-time targeted purchase, get in touch with our company and we will fulfill your international data need.

At Techsalerator, we cover all regions and cities in Spain with Business Data.

A few example of regions in Spain : Andalusia Catalonia Community of Madrid Valencian Community Galicia Castile and León Basque Country Castilla-La Mancha Canary Islands Region of Murcia Aragon Extremadura Balearic Islands Asturias Navarre Cantabria La Rioja

A few examples of cities in Spain : Madrid Barcelona Valencia Sevilla Zaragoza Malaga Murcia Palma Las Palmas de Gran Canaria Bilbao Alicante Cordoba Valladolid Vigo Gijon Eixample L'Hospitalet de Llobregat Latina Carabanchel A Coruna Puente de Vallecas Sant Marti Gasteiz / Vitoria Granada Elche Ciudad Lineal Oviedo Santa Cruz de Tenerife Fuencarral-El Pardo Badalona Cartagena Terrassa Jerez de la Frontera Sabadell Mostoles Alcala de Henares Pamplona Fuenlabrada Almeria Leganes San Sebastian Sants-Montjuic Santander Castello de la Plana Burgos Albacete Horta-Guinardo Alcorcon Getafe Nou Barris Hortaleza San Blas-Canillejas Salamanca Tetuan de las Victorias Logrono La Laguna City Center Huelva Arganzuela Badajoz Sarria-Sant Gervasi Sant Andreu Salamanca Chamberi Usera Tarragona Chamartin Lleida Marbella Leon Villaverde Cadiz Retiro Dos Hermanas Mataro Gracia Santa Coloma de Gramenet Torrejon de Ardoz Jaen Moncloa-Aravaca Algeciras Parla Delicias Ourense Alcobendas Reus Moratalaz Ciutat Vella Torrevieja Telde Barakaldo Lugo San Fernando Girona Santiago de Compostela Caceres Lorca Coslada Talavera de la Reina El Puerto de Santa Maria Cornella de Llobregat Las Rozas de Madrid Orihuela Aviles El Ejido Guadalajara Roquetas de Mar Palencia Algorta Pozuelo de Alarcon Sant Boi de Llobregat Toledo Les Corts Pontevedra Getxo Gandia Sant Cugat del Valles Ceuta Arona Torrent Chiclana de la Frontera Manresa San Sebastian de los Reyes Ferrol Velez-Malaga Ciudad Real Mijas Melilla

Success.ai’s Commercial Real Estate Data and B2B Contact Data for Global Real Estate Professionals is a comprehensive dataset designed to connect businesses with industry leaders in real estate worldwide. With over 170M verified profiles, including work emails and direct phone numbers, this solution ensures precise outreach to agents, brokers, property developers, and key decision-makers in the real estate sector.

Utilizing advanced AI-driven validation, our data is continuously updated to maintain 99% accuracy, offering actionable insights that empower targeted marketing, streamlined sales strategies, and efficient recruitment efforts. Whether you’re engaging with top real estate executives or sourcing local property experts, Success.ai provides reliable and compliant data tailored to your needs.

Key Features of Success.ai’s Real Estate Professional Contact Data

• Comprehensive Industry Coverage Gain direct access to verified profiles of real estate professionals across the globe, including:

1. Real Estate Agents: Professionals facilitating property sales and purchases.
2. Brokers: Key intermediaries managing transactions between buyers and sellers.
3. Property Developers: Decision-makers shaping residential, commercial, and industrial projects.
4. Real Estate Executives: Leaders overseeing multi-regional operations and business strategies.
5. Architects & Consultants: Experts driving design and project feasibility.

• Verified and Continuously Updated Data

AI-Powered Validation: All profiles are verified using cutting-edge AI to ensure up-to-date accuracy. Real-Time Updates: Our database is refreshed continuously to reflect the most current information. Global Compliance: Fully aligned with GDPR, CCPA, and other regional regulations for ethical data use.

• Customizable Data Delivery Tailor your data access to align with your operational goals:

API Integration: Directly integrate data into your CRM or project management systems for seamless workflows. Custom Flat Files: Receive detailed datasets customized to your specifications, ready for immediate application.

Why Choose Success.ai for Real Estate Contact Data?

• Best Price Guarantee Enjoy competitive pricing that delivers exceptional value for verified, comprehensive contact data.

• Precision Targeting for Real Estate Professionals Our dataset equips you to connect directly with real estate decision-makers, minimizing misdirected efforts and improving ROI.

• Strategic Use Cases

  Lead Generation: Target qualified real estate agents and brokers to expand your network. Sales Outreach: Engage with property developers and executives to close high-value deals. Marketing Campaigns: Drive targeted campaigns tailored to real estate markets and demographics. Recruitment: Identify and attract top talent in real estate for your growing team. Market Research: Access firmographic and demographic data for in-depth industry analysis.

• Data Highlights 170M+ Verified Professional Profiles 50M Work Emails 30M Company Profiles 700M Global Professional Profiles

• Powerful APIs for Enhanced Functionality

  Enrichment API Ensure your contact database remains relevant and up-to-date with real-time enrichment. Ideal for businesses seeking to maintain competitive agility in dynamic markets.

Lead Generation API Boost your lead generation with verified contact details for real estate professionals, supporting up to 860,000 API calls per day for robust scalability.

• Use Cases for Real Estate Contact Data

1. Targeted Outreach for New Projects Connect with property developers and brokers to pitch your services or collaborate on upcoming projects.

2. Real Estate Marketing Campaigns Execute personalized marketing campaigns targeting agents and clients in residential, commercial, or industrial sectors.

3. Enhanced Sales Strategies Shorten sales cycles by directly engaging with decision-makers and key stakeholders.

4. Recruitment and Talent Acquisition Access profiles of highly skilled professionals to strengthen your real estate team.

5. Market Analysis and Intelligence Leverage firmographic and demographic insights to identify trends and optimize business strategies.

• What Makes Us Stand Out? >> Unmatched Data Accuracy: Our AI-driven validation ensures 99% accuracy for all contact details. >> Comprehensive Global Reach: Covering professionals across diverse real estate markets worldwide. >> Flexible Delivery Options: Access data in formats that seamlessly fit your existing systems. >> Ethical and Compliant Data Practices: Adherence to global standards for secure and responsible data use.

Success.ai’s B2B Contact Data for Global Real Estate Professionals delivers the tools you need to connect with the right people at the right time, driving efficiency and success in your business operations. From agents and brokers to property developers and executiv...

Note: This dataset has been superseded by the dataset found at "End-Use Load Profiles for the U.S. Building Stock" (submission 4520; linked in the submission resources), which is a comprehensive and validated representation of hourly load profiles in the U.S. commercial and residential building stock. The End-Use Load Profiles project website includes links to data viewers for this new dataset. For documentation of dataset validation, model calibration, and uncertainty quantification, see Wilson et al. (2022).

These data were first created around 2012 as a byproduct of various analyses of solar photovoltaics and solar water heating (see references below for are two examples). This dataset contains several errors and limitations. It is recommended that users of this dataset transition to the updated version of the dataset posted in the resources. This dataset contains weather data, commercial load profile data, and residential load profile data.

Weather The Typical Meteorological Year 3 (TMY3) provides one year of hourly data for around 1,000 locations. The TMY weather represents 30-year normals, which are typical weather conditions over a 30-year period.

Commercial The commercial load profiles included are the 16 ASHRAE 90.1-2004 DOE Commercial Prototype Models simulated in all TMY3 locations, with building insulation levels changing based on ASHRAE 90.1-2004 requirements in each climate zone. The folder names within each resource represent the weather station location of the profiles, whereas the file names represent the building type and the representative city for the ASHRAE climate zone that was used to determine code compliance insulation levels. As indicated by the file names, all building models represent construction that complied with the ASHRAE 90.1-2004 building energy code requirements. No older or newer vintages of buildings are represented.

Residential The BASE residential load profiles are five EnergyPlus models (one per climate region) representing 2009 IECC construction single-family detached homes simulated in all TMY3 locations. No older or newer vintages of buildings are represented. Each of the five climate regions include only one heating fuel type; electric heating is only found in the Hot-Humid climate. Air conditioning is not found in the Marine climate region.

One major issue with the residential profiles is that for each of the five climate zones, certain location-specific algorithms from one city were applied to entire climate zones. For example, in the Hot-Humid files, the heating season calculated for Tampa, FL (December 1 - March 31) was unknowingly applied to all other locations in the Hot-Humid zone, which restricts heating operation outside of those days (for example, heating is disabled in Dallas, TX during cold weather in November). This causes the heating energy to be artificially low in colder parts of that climate zone, and conversely the cooling season restriction leads to artificially low cooling energy use in hotter parts of each climate zone. Additionally, the ground temperatures for the representative city were used across the entire climate zone. This affects water heating energy use (because inlet cold water temperature depends on ground temperature) and heating/cooling energy use (because of ground heat transfer through foundation walls and floors). Representative cities were Tampa, FL (Hot-Humid), El Paso, TX (Mixed-Dry/Hot-Dry), Memphis, TN (Mixed-Humid), Arcata, CA (Marine), and Billings, MT (Cold/Very-Cold).

The residential dataset includes a HIGH building load profile that was intended to provide a rough approximation of older home vintages, but it combines poor thermal insulation with larger house size, tighter thermostat setpoints, and less efficient HVAC equipment. Conversely, the LOW building combines excellent thermal insulation with smaller house size, wider thermostat setpoints, and more efficient HVAC equipment. However, it is not known how well these HIGH and LOW permutations represent the range of energy use in the housing stock.

Note that on July 2nd, 2013, the Residential High and Low load files were updated from 366 days in a year for leap years to the more general 365 days in a normal year. The archived residential load data is included from prior to this date.

With 1.5 Million Businesses in Ukraine, Techsalerator has access to the highest B2B count of Data/Business Data in the country.

Thanks to our unique tools and large data specialist team, we can select the ideal targeted dataset based on the unique elements such as sales volume of a company, the company's location, no. of employees etc...

Whether you are looking for an entire fill install, access to our API's or if you are just looking for a one-time targeted purchase, get in touch with our company and we will fulfill your international data need.

We cover all regions and cities: ( example) Kyiv Київ Kyiv Kharkiv Харків Kharkiv Oblast Odesa Одеса Odesa Oblast Dnipro Дніпро Dnipropetrovsk Oblast Donetsk Донецьк Donetsk Oblast Zaporizhzhia Запоріжжя Zaporizhzhia Oblast Lviv Львів Lviv Oblast Kryvyi Rih Кривий Ріг Dnipropetrovsk Oblast Mykolaiv Миколаїв Mykolaiv Oblast Sevastopol Севастополь Sevastopol Mariupol Маріуполь Donetsk Oblast Luhansk Луганськ Luhansk Oblast Vinnytsia Вінниця Vinnytsia Oblast Makiivka Макіївка Donetsk Oblast Simferopol Сімферополь Crimea Chernihiv Чернігів Chernihiv Oblast Kherson Херсон Kherson Oblast Poltava Полтава Poltava Oblast Khmelnytskyi Хмельницький Khmelnytskyi Oblast Cherkasy Черкаси Cherkasy Oblast Chernivtsi Чернівці Chernivtsi Oblast Zhytomyr Житомир Zhytomyr Oblast Sumy Суми Sumy Oblast Rivne Рівне Rivne Oblast Horlivka Горлівка Donetsk Oblast Ivano-Frankivsk Івано-Франківськ Ivano-Frankivsk Oblast Kamianske Кам'янське Dnipropetrovsk Oblast Ternopil Тернопіль Ternopil Oblast Kropyvnytskyi Кропивницький Kirovohrad Oblast Kremenchuk Кременчук Poltava Oblast Lutsk Луцьк Volyn Oblast Bila Tserkva Біла Церква Kyiv Oblast Kerch Керч Crimea Melitopol Мелітополь Zaporizhzhia Oblast Kramatorsk Краматорськ Donetsk Oblast Uzhhorod Ужгород Zakarpattia Oblast Brovary Бровари Kyiv Oblast Yevpatoria Євпаторія Crimea Berdiansk Бердянськ Zaporizhzhia Oblast Nikopol Нікополь Dnipropetrovsk Oblast Sloviansk Слов'янськ Donetsk Oblast Alchevsk Алчевськ Luhansk Oblast Pavlohrad Павлоград Dnipropetrovsk Oblast Sieverodonetsk Сєверодонецьк Luhansk Oblast Kamianets-Podilskyi Кам'янець-Подільський Khmelnytskyi Oblast Lysychansk Лисичанськ Luhansk Oblast Mukachevo Мукачево Zakarpattia Oblast Konotop Конотоп Sumy Oblast Uman Умань Cherkasy Oblast Khrustalnyi Хрустальний Luhansk Oblast Yalta Ялта Crimea Oleksandriia Олександрія Kirovohrad Oblast Yenakiieve Єнакієве Donetsk Oblast Drohobych Дрогобич Lviv Oblast Berdychiv Бердичів Zhytomyr Oblast Kadiyivka Кадіївка Luhansk Oblast Shostka Шостка Sumy Oblast Bakhmut Бахмут Donetsk Oblast Izmail Ізмаїл Odesa Oblast Novomoskovsk Новомосковськ Dnipropetrovsk Oblast Kostiantynivka Костянтинівка Donetsk Oblast Kovel Ковель Volyn Oblast Feodosiya Феодосія Crimea Nizhyn Ніжин Chernihiv Oblast Smila Сміла Cherkasy Oblast Kalush Калуш Ivano-Frankivsk Oblast Chervonohrad Червоноград Lviv Oblast Boryspil Бориспіль Kyiv Oblast Pervomaisk Первомайськ Mykolaiv Oblast Dovzhansk Довжанськ Luhansk Oblast Irpin Ірпінь Kyiv Oblast Korosten Коростень Zhytomyr Oblast Pokrovsk Покровськ Donetsk Oblast Kolomyia Коломия Ivano-Frankivsk Oblast Stryi Стрий Lviv Oblast Chornomorsk Чорноморськ Odesa Oblast Khartsyzk Харцизьк Donetsk Oblast Rubizhne Рубіжне Luhansk Oblast Novohrad-Volynskyi Новоград-Волинський Zhytomyr Oblast Druzhkivka Дружківка Donetsk Oblast Lozova Лозова Kharkiv Oblast Chystiakove Чистякове Donetsk Oblast Enerhodar Енергодар Zaporizhzhia Oblast Pryluky Прилуки Chernihiv Oblast Antratsyt Антрацит Luhansk Oblast Novovolynsk Нововолинськ Volyn Oblast Horishni Plavni Горішні Плавні Poltava Oblast Shakhtarsk Шахтарськ Donetsk Oblast Bilhorod-Dnistrovskyi Білгород-Дністровський Odesa Oblast Okhtyrka Охтирка Sumy Oblast Myrnohrad Мирноград Donetsk Oblast Snizhne Сніжне Donetsk Oblast Izium Ізюм Kharkiv Oblast Marhanets Марганець Dnipropetrovsk Oblast Rovenky Ровеньки Luhansk Oblast Nova Kakhovka Нова Каховка Kherson Oblast Brianka Брянка Luhansk Oblast Fastiv Фастів Kyiv Oblast Lubny Лубни Poltava Oblast Svitlovodsk Світловодськ Kirovohrad Oblast Zhovti Vody Жовті Води Dnipropetrovsk Oblast Sorokyne Сорокине Luhansk Oblast Vyshneve Вишневе Kyiv Oblast Varash Вараш Rivne Oblast Shepetivka Шепетівка Khmelnytskyi Oblast Podilsk Подільськ Odesa Oblast Yuzhnoukrainsk Южноукраїнськ Mykolaiv Oblast Myrhorod Миргород Poltava Oblast Romny Ромни Sumy Oblast Pokrov Покров Dnipropetrovsk Oblast Volodymyr-Volynskyi Володимир-Волинський Volyn Oblast Dzhankoy Джанкой Crimea Vasylkiv Васильків Kyiv Oblast Dubno Дубно Rivne Oblast Bucha Буча Kyiv Oblast Netishyn Нетішин Khmelnytskyi Oblast Pervomaisk Первомайськ Luhansk Oblast Kakhovka Каховка Kherson Oblast Boiarka Боярка Kyiv Oblast Slavuta Славута Khmelnytskyi Oblast Sambir Самбір Lviv Oblast Yasynuvata Ясинувата Donetsk Oblast Starokostiantyniv Старокостянтинів Khmelnytskyi Oblast Zhmerynka ...

40093 Global import shipment records of Commercial Samples with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This dataset provides a list of the tests undertaken by APHA testing laboratories on avian samples in 2014 paid for by commercial contracts. The dataset includes the following fields: Year; Species class; Species; Test code; test description; Number of tests (the volume of tests performed in the 12 month period).

The National Crime Surveys, of which these Commercial Victimization Surveys are a part, were conducted to obtain current and reliable measures of serious crime in the United States. The Commercial Victimization Surveys are restricted to coverage of burglary and robbery incidents. They include all types of commercial establishments as well as political, cultural, and religious organizations. The survey includes a series of questions about the business, e.g., type and size, form of ownership, insurance, security, and break-in and robbery characteristics. Time and place, weapon, injury, entry evidence, offender characteristics, and stolen property data were collected for each of the incidents. Data on both victimized and nonvictimized establishments in 26 different cities were collected during 1972, 1973, and 1974. In the 1975 survey, data from the 13 cities surveyed during 1972 and 1973 were collected again.

Water use and supply data for 2016 joined to spatial boundaries. GPCD = Gallons Per Capita Day or Gallons Per Person Per Day. Supply and Use numbers are in Acre Feet Per Year (ACFT).

The global big data and business analytics (BDA) market was valued at 168.8 billion U.S. dollars in 2018 and is forecast to grow to 215.7 billion U.S. dollars by 2021. In 2021, more than half of BDA spending will go towards services. IT services is projected to make up around 85 billion U.S. dollars, and business services will account for the remainder. Big data High volume, high velocity and high variety: one or more of these characteristics is used to define big data, the kind of data sets that are too large or too complex for traditional data processing applications. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets. For example, connected IoT devices are projected to generate 79.4 ZBs of data in 2025. Business analytics Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate business insights. The size of the business intelligence and analytics software application market is forecast to reach around 16.5 billion U.S. dollars in 2022. Growth in this market is driven by a focus on digital transformation, a demand for data visualization dashboards, and an increased adoption of cloud.

This dataset contains the owner information for all the accounts listed in the Business License Dataset, and is sorted by Account Number. To identify the owner of a business, you will need the account number or legal name, which may be obtained from theBusiness Licenses dataset: https://data.cityofchicago.org/dataset/Business-Licenses/r5kz-chrr. Data Owner: Business Affairs & Consumer Protection. Time Period: 2002 to present. Frequency: Data is updated daily.