Northwind Database

La base de datos Northwind es una base de datos de muestra creada originalmente por Microsoft y utilizada como base para sus tutoriales en una variedad de productos de bases de datos durante décadas. La base de datos de Northwind contiene datos de ventas de una empresa ficticia llamada "Northwind Traders", que importa y exporta alimentos especiales de todo el mundo. La base de datos Northwind es un excelente esquema tutorial para un ERP de pequeñas empresas, con clientes, pedidos, inventario, compras, proveedores, envíos, empleados y contabilidad de entrada única. Desde entonces, la base de datos Northwind ha sido trasladada a una variedad de bases de datos que no son de Microsoft, incluido PostgreSQL.

El conjunto de datos de Northwind incluye datos de muestra para lo siguiente.

• Proveedores: Proveedores y vendedores de Northwind
• Clientes: Clientes que compran productos de Northwind
• Empleados: detalles de los empleados de los comerciantes de Northwind
• Productos: Información del producto
• Transportistas: los detalles de los transportistas que envían los productos desde los comerciantes a los clientes finales.
• Órdenes y detalles de la orden: transacciones de órdenes de venta que tienen lugar entre los clientes y la empresa.

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Chinook DataBase

Chinook es una base de datos de muestra disponible para SQL Server, Oracle, MySQL, etc. Se puede crear ejecutando un único script SQL. La base de datos Chinook es una alternativa a la base de datos Northwind, siendo ideal para demostraciones y pruebas de herramientas ORM dirigidas a servidores de bases de datos únicos o múltiples.

El modelo de datos Chinook representa una tienda de medios digitales, que incluye tablas para artistas, álbumes, pistas multimedia, facturas y clientes.

Los datos relacionados con los medios se crearon utilizando datos reales de una biblioteca de iTunes. La información de clientes y empleados se creó manualmente utilizando nombres ficticios, direcciones que se pueden ubicar en mapas de Google y otros datos bien formateados (teléfono, fax, correo electrónico, etc.). La información de ventas se genera automáticamente utilizando datos aleatorios durante un período de cuatro años.

¿Por qué el nombre Chinook? El nombre de esta base de datos de ejemplo se basó en la base de datos Northwind. Los chinooks son vientos en el interior oeste de América del Norte, donde las praderas canadienses y las grandes llanuras se encuentran con varias cadenas montañosas. Los chinooks son más frecuentes en el sur de Alberta en Canadá. Chinook es una buena opción de nombre para una base de datos que pretende ser una alternativa a Northwind.

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This dataset provides information about the number of properties, residents, and average property values for Northwind Road cross streets in Las Cruces, NM.

Comprehensive performance analysis and data for Clyde North wind farm, including capacity factors, generation totals, curtailment losses, and predictive modeling based on weather data from the GB balancing mechanism.

Comprehensive performance analysis and data for Strathy North wind farm, including capacity factors, generation totals, curtailment losses, and predictive modeling based on weather data from the GB balancing mechanism.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

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Electrical Energy Matrix: Number Individually Simulated: North: Wind: 2028 data was reported at 426.000 MW in May 2025. This stayed constant from the previous number of 426.000 MW for Apr 2025. Electrical Energy Matrix: Number Individually Simulated: North: Wind: 2028 data is updated monthly, averaging 426.000 MW from Jan 2024 (Median) to May 2025, with 17 observations. The data reached an all-time high of 426.000 MW in May 2025 and a record low of 426.000 MW in May 2025. Electrical Energy Matrix: Number Individually Simulated: North: Wind: 2028 data remains active status in CEIC and is reported by National Electric System Operator. The data is categorized under Brazil Premium Database’s Energy Sector – Table BR.RBA011: Electrical Energy: Matrix.

The 2023 National Offshore Wind data set (NOW-23) is the latest wind resource data set for offshore regions in the United States, which supersedes, for its offshore component, the Wind Integration National Dataset (WIND) Toolkit, which was published about a decade ago and is currently one of the primary resources for stakeholders conducting wind resource assessments in the continental United States. The NOW-23 data set was produced using the Weather Research and Forecasting Model (WRF) version 4.2.1. A regional approach was used: for each offshore region, the WRF setup was selected based on validation against available observations. The WRF model was initialized with the European Centre for Medium Range Weather Forecasts 5 Reanalysis (ERA-5) data set, using a 6-hour refresh rate. The model is configured with an initial horizontal grid spacing of 6 km and an internal nested domain that refined the spatial resolution to 2 km. The model is run with 61 vertical levels, with 12 levels in the lower 300m of the atmosphere, stretching from 5 m to 45 m in height. The MYNN planetary boundary layer and surface layer schemes were used the North Atlantic, Mid Atlantic, Great Lakes, Hawaii, and North Pacific regions. On the other hand, using the YSU planetary boundary layer and MM5 surface layer schemes resulted in a better skill in the South Atlantic, Gulf of Mexico, and South Pacific regions. A more detailed description of the WRF model setup can be found in the WRF namelist files linked at the bottom of this page. For all regions, the NOW-23 data set coverage starts on January 1, 2000. For Hawaii and the North Pacific regions, NOW-23 goes until December 31, 2019. For the South Pacific region, the model goes until 31 December, 2022. For all other regions, the model covers until December 31, 2020. Outputs are available at 5 minute resolution, and for all regions we have also included output files at hourly resolution. The NOW-23 data are provided here as HDF5 files. Examples of how to use the HSDS Service to Access the NOW-23 files are linked below. A list of the variables included in the NOW-23 files is also linked below. No filters have been applied to the raw WRF output.

Wind resource data for North America was produced using the Weather Research and Forecasting Model (WRF). The WRF model was initialized with the European Centre for Medium Range Weather Forecasts Interim Reanalysis (ERA-Interm) data set with an initial grid spacing of 54 km. Three internal nested domains were used to refine the spatial resolution to 18, 6, and finally 2 km. The WRF model was run for years 2007 to 2014. While outputs were extracted from WRF at 5 minute time-steps, due to storage limitations instantaneous hourly time-step are provided for all variables while full 5 min resolution data is provided for wind speed and wind direction only. The following variables were extracted from the WRF model data: - Wind Speed at 10, 40, 60, 80, 100, 120, 140, 160, 200 m - Wind Direction at 10, 40, 60, 80, 100, 120, 140, 160, 200 m - Temperature at 2, 10, 40, 60, 80, 100, 120, 140, 160, 200 m - Pressure at 0, 100, 200 m - Surface Precipitation Rate - Surface Relative Humidity - Inverse Monin Obukhov Length

The data covers the calculated yields of wind farms in German territory and the German exclusive economic zone (EEZ) and the relevant wind conditions, assuming an expansion of offshore wind energy in continuous expansion years. The calculations were carried out with the numerical weather model WRF using a parameterization of wind farms according to Fitch. The data are available in 10-minute temporal and 2 km x 2 km spatial resolution for the North Sea for the meteorological year 2006. The years in the file names refer to the respective year of expansion. The variables of the data set are wind speed (WS) and wind direction (WD) at 9 height levels between 50 m and 350 m, the power (POWER) of the wind turbines from each grid cell and the air density. A detailed description of the variables can be found in the files.

Electric Energy Matrix: Number Individually Simulated: North: Wind: 2026 data was reported at 426.000 MW in May 2025. This stayed constant from the previous number of 426.000 MW for Apr 2025. Electric Energy Matrix: Number Individually Simulated: North: Wind: 2026 data is updated monthly, averaging 426.000 MW from Jan 2022 (Median) to May 2025, with 41 observations. The data reached an all-time high of 426.000 MW in May 2025 and a record low of 426.000 MW in May 2025. Electric Energy Matrix: Number Individually Simulated: North: Wind: 2026 data remains active status in CEIC and is reported by National Electric System Operator. The data is categorized under Brazil Premium Database’s Energy Sector – Table BR.RBA011: Electrical Energy: Matrix.

The data covers the calculated yields of wind farms in German territory and the German exclusive economic zone (EEZ) and the relevant wind conditions under the assumption of an expansion of offshore wind energy defined in different scenarios. The calculations were carried out with the numerical weather model WRF using a parameterization of wind farms according to Fitch. The data are available in 10-minute temporal and 2 km x 2 km spatial resolution for the North Sea for the meteorological year 2006. The variables of the data set are wind speed (WS) and wind direction (WD) at 9 height levels between 50 m and 350 m, the power (POWER) of the wind turbines from each grid cell and the air density. A detailed description of the variables can be found in the files.

Overview Winds. A radar wind profiler measures the Doppler shift of electromagnetic energy scattered back from atmospheric turbulence and hydrometeors along 3-5 vertical and off-vertical point beam directions. Back-scattered signal strength and radial-component velocities are remotely sensed along all beam directions and are combined to derive the horizontal wind field over the radar. These data typically are sampled and averaged hourly and usually have 6-m and/or 100-m vertical resolutions up to 4 km for the 915 MHz and 8 km for the 449 MHz systems. Temperature. To measure atmospheric temperature, a radio acoustic sounding system (RASS) is used in conjunction with the wind profile. These data typically are sampled and averaged for five minutes each hour and have a 60-m vertical resolution up to 1.5 km for the 915 MHz and 60 m up to 3.5 km for the 449 MHz. Moments and Spectra. The raw spectra and moments data are available for all dwells along each beam and are stored in daily files. For each day, there are files labeled "header" and "data." These files are generated by the radar data acquisition system (LAP-XM) and are encoded in a proprietary binary format. Values of spectral density at each Doppler velocity (FFT point), as well as the radial velocity, signal-to-noise ratio, and spectra width for the selected signal peak are included in these files. Attached zip files, 449mhz-spectra-data-extraction.zip and 449mhz-moment-data-extraction.zip, include executables to unpack the spectra, (GetSpectra32.exe) and moments (GetMomSp32.exe), respectively. Documentation on usage and output file formats also are included in the zip files. Data Details Note, the b0 data is identical to 00 data but a netcdf extraction of the b0 data was also created for the duration of the WFIP2 campaign. Data Quality Various quality control (QC) algorithms developed over the years process data in real time on the radar software layer. These algorithms, which run in real time, act on time-series, spectra, moment, and consensus data layers that are persisted in different forms.

Comprehensive dataset containing 2 verified Wind farm businesses in North Carolina, United States with complete contact information, ratings, reviews, and location data.

The data covers the calculated yields of wind farms in German territory and the German exclusive economic zone (EEZ) and the relevant wind conditions under the assumption of an expansion of offshore wind energy defined in different scenarios. The calculations were carried out with the numerical weather model WRF using a parameterization of wind farms according to Fitch. The data are available in 10-minute temporal and 2 km x 2 km spatial resolution for the North Sea for the meteorological year 2006. The variables of the data set are wind speed (WS) and wind direction (WD) at 9 height levels between 50 m and 350 m, the power (POWER) of the wind turbines from each grid cell and the air density. A detailed description of the variables can be found in the files.

This dataset shows the current risk presented to sites across the UK from North easterly winds, based on data collected from a 1981-2010 baseline period. This data is the Met Office UKCP18 data depicting NE storm winds will have more impact on the UK. Historically SW winds dominate the UK.

The KNW (KNMI North Sea Wind) atlas is based on the ERA-Interim reanalyses dataset which captures more than 40 years (January 1979 - August 2019) of meteorological measurements and generates 3D wind (temperature, etc) fields consistent with these measurements and the laws of physics. This dataset is downscaled using the state-of-the-art weather forecasting model, HARMONIE with a horizontal grid of 2.5 km. The vertical profile of wind speed was calibrated against the 200 m tall Cabauw measurement mast to obtain a single wind shear correction coefficient which was applied throughout the whole dataset. The result is a high resolution dataset of more than 40 years: the KNW dataset.

This data release contains eight datasets that represent the entirety of the data collected for a study that examined breeding-bird densities in native mixed-grass prairie from 2003 to 2012 at and adjacent to wind facilities in North Dakota and South Dakota, USA. Data were collected to determine breeding-bird density per 100 hectares (ha) by distance bands from turbines and by excluding habitat that may not be considered suitable as breeding habitat for particular bird species. A subset of the data that included only one year prior to turbine construction and several years post-construction and that lent itself to a before-after-control-impact (BACI) assessment was published as its own data release and paper in 2016 in Conservation Biology by authors J. Shaffer and D. Buhl. The all-inclusive data release described hereafter is of the same basic format but includes all years and all study sites (also referred to as study plots), even those that did not lend themselves to a BACI ass ...

The global wind data logger market is experiencing robust growth, driven by the expanding renewable energy sector and the increasing need for precise wind resource assessment. The market, estimated at $150 million in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching approximately $250 million by 2033. This growth is fueled by several key factors. Firstly, the burgeoning wind energy industry necessitates accurate and reliable wind data for site selection, turbine optimization, and overall project feasibility. Secondly, advancements in data logger technology, such as improved accuracy, longer battery life, and remote data access capabilities, are boosting market adoption. The increasing emphasis on optimizing wind farm performance through advanced analytics and predictive maintenance further contributes to the market's expansion. Active data transfer systems are currently dominating the market due to their real-time data capabilities, crucial for effective wind farm management. However, passive data transfer systems are gaining traction due to their cost-effectiveness and suitability for remote locations with limited infrastructure. The North American market currently holds the largest share, driven by significant investments in wind energy projects and robust technological infrastructure. However, Asia-Pacific is expected to witness the fastest growth in the coming years, fueled by rapidly expanding wind energy capacity in countries like China and India. Competition in the market is intense, with established players like Vaisala and Campbell Scientific alongside specialized firms like WINDLogger, NRG Systems, and Kintech Engineering vying for market share. The market segmentation reveals strong demand across various applications. Wind resource monitoring and assessment are the primary applications, reflecting the core requirement for accurate wind data in wind farm development and operation. The constraints on market growth include the high initial investment associated with deploying data loggers, particularly in remote areas, and the need for specialized expertise in data analysis and interpretation. Despite these challenges, the long-term outlook for the wind data logger market remains positive, driven by the global shift towards cleaner energy sources and technological advancements that promise to enhance affordability and accessibility. The continued focus on improving wind energy efficiency and reducing operational costs will drive demand for sophisticated data loggers capable of providing detailed insights into wind patterns and turbine performance. This will lead to a continued expansion of the market across all geographical regions and application segments.

This dataset shows the future risk presented to sites across the UK from North Easterly winds , based on data projected for a 2060-2080 future period. This data is the Met Office UKCP18 data depicting NE storm winds will have more impact on the UK. Historically SW winds dominate the UK.