Data Lakes Market size was valued at USD 17.21 Billion in 2024 and is projected to reach USD 79.09 Billion by 2031, growing at a CAGR of 21.00% during the forecasted period 2024 to 2031.

The data lakes market is driven by the growing need for organizations to manage and analyze vast amounts of unstructured and structured data for better decision-making and insights. As businesses increasingly rely on big data analytics, machine learning, and artificial intelligence to gain competitive advantages, data lakes provide a scalable and cost-effective solution to store raw data from diverse sources. The rising adoption of cloud-based solutions further fuels the market, as cloud data lakes offer flexibility, agility, and seamless integration with analytics tools. Additionally, the growing emphasis on digital transformation, real-time data processing, and enhanced data governance are key factors pushing the demand for data lakes across industries such as finance, healthcare, retail, and manufacturing.

The United States is embarking on an ambitious transition to a 100% clean energy economy by 2050, which will require improving the flexibility of electric grids. One way to achieve grid flexibility is to shed or shift demand to align with changing grid needs. To facilitate this, it is critical to understand how and when energy is used. High quality end-use load profiles (EULPs) provide this information, and can help cities, states, and utilities understand the time-sensitive value of energy efficiency, demand response, and distributed energy resources. Publicly available EULPs have traditionally had limited application because of age and incomplete geographic representation. To help fill this gap, the U.S. Department of Energy (DOE) funded a three-year project, End-Use Load Profiles for the U.S. Building Stock, that culminated in this publicly available dataset of calibrated and validated 15-minute resolution load profiles for all major residential and commercial building types and end uses, across all climate regions in the United States. These EULPs were created by calibrating the ResStock and ComStock physics-based building stock models using many different measured datasets, as described in the "Technical Report Documenting Methodology" linked in the submission.

The BuildingsBench datasets consist of: Buildings-900K: A large-scale dataset of 900K buildings for pretraining models on the task of short-term load forecasting (STLF). Buildings-900K is statistically representative of the entire U.S. building stock. 7 real residential and commercial building datasets for benchmarking two downstream tasks evaluating generalization: zero-shot STLF and transfer learning for STLF. Buildings-900K can be used for pretraining models on day-ahead STLF for residential and commercial buildings. The specific gap it fills is the lack of large-scale and diverse time series datasets of sufficient size for studying pretraining and finetuning with scalable machine learning models. Buildings-900K consists of synthetically generated energy consumption time series. It is derived from the NREL End-Use Load Profiles (EULP) dataset (see link to this database in the links further below). However, the EULP was not originally developed for the purpose of STLF. Rather, it was developed to "...help electric utilities, grid operators, manufacturers, government entities, and research organizations make critical decisions about prioritizing research and development, utility resource and distribution system planning, and state and local energy planning and regulation." Similar to the EULP, Buildings-900K is a collection of Parquet files and it follows nearly the same Parquet dataset organization as the EULP. As it only contains a single energy consumption time series per building, it is much smaller (~110 GB). BuildingsBench also provides an evaluation benchmark that is a collection of various open source residential and commercial real building energy consumption datasets. The evaluation datasets, which are provided alongside Buildings-900K below, are collections of CSV files which contain annual energy consumption. The size of the evaluation datasets altogether is less than 1GB, and they are listed out below: ElectricityLoadDiagrams20112014 Building Data Genome Project-2 Individual household electric power consumption (Sceaux) Borealis SMART IDEAL Low Carbon London A README file providing details about how the data is stored and describing the organization of the datasets can be found within each data lake version under BuildingsBench.

Historical water temperature and over-water meteorogical data are available on websites of the National Data Buoy Center and NCEP/NCAR reanalysis. These data were used as inputs for the calculation of surface energy fluxes through the surfaces of lakes.

The size and share of this market is categorized based on Deployment Model (Public Cloud, Private Cloud, Hybrid Cloud) and Component (Solutions, Services) and End-User Industry (BFSI, Healthcare, Retail, IT & Telecom, Manufacturing, Government, Energy & Utilities) and Organization Size (Small and Medium Enterprises, Large Enterprises) and Functionality (Data Ingestion, Data Processing, Data Storage, Data Analytics, Data Governance) and geographical regions (North America, Europe, Asia-Pacific, South America, Middle-East and Africa).

Eddy covariance measurements were conducted from 23 April to 16 August 2014 on a thermokarst lake in the Siberian Lena River Delta, yielding direct measurements of sensible (H) and latent (LE) heat flux on half-hourly basis. Ancillary measurements including meteorological variables and water temperature measurements were gathered during the campaign. We derived bulk aerodynamic transfer coefficients in order to parameterize the heat fluxes and compare this in-situ model with independent heat flux parameterization schemes, which are also based on the common bulk transfer algorithm. We further investigated the components of a simple energy balance including measured and modelled H and LE. The dataset was created within the framework of a publication of the study results in Journal of Geophysical Research - Atmospheres (Lake-atmosphere heat flux dynamics of a thermokarst lake in arctic Siberia, by Franz et al.)

This U.S. Geological Survey data release presents monthly evaporation estimates from Lake Mead, Nevada and Arizona. Data are updated approximately annually. The spreadsheet includes five worksheets: (1) Read_Me worksheet contains information relevant to understanding the data contained in the rest of the worksheets. (2) Monthly_EC_Met worksheet includes data measured at a land-based station (USGS site identification number 360500114465601) using primarily eddy covariance measurement methods: uncorrected evaporation, latent- and sensible-heat fluxes, net radiation, air temperature, wind speed, and relative humidity. Values are monthly averages computed by averaging daily values except as noted. Monthly values are marked as estimated when a significant portion of daily values are estimated. (3) Monthly_Energy-Budget_Data worksheet includes computed data used to correct measured evaporation for energy balance. Computed data include monthly values for change in stored heat, net advection, turbulent flux, available energy, energy balance ratio, energy balance closure, and Bowen ratio. Change in stored heat was calculated based on methods in Earp and Moreo (2021). Net advection was calculated based on data estimated by the Bureau of Reclamation 24-Month Study (2022). Values are monthly averages or computed from monthly averages. (4) Annual_Energy_Balance worksheet includes annual averages of the Monthly_Energy_Balance data and the annual average values for energy-balance corrected sensible and latent heat fluxes. Values are annual averages or computed from annual averages. (5) Monthly_Evaporation_Estimates worksheet includes measured evaporation, corrected (most probable) evaporation, and energy balance ratio (EBR) adjusted evaporation, in feet. Values are monthly averages or computed from monthly averages. Data were processed according to methods described in Moreo and Swancar (2013) and Earp and Moreo (2021). References Cited: Bureau of Reclamation, Lower Colorado Region website: Operation Plan for Colorado River System Reservoirs (24-Month Study), accessed September 1, 2022 at https://www.usbr.gov/lc/region/g4000/24mo/index.html. Earp, K.J., and Moreo, M.T., 2021, Evaporation from Lake Mead and Lake Mohave, Nevada and Arizona, 2010–2019: U.S. Geological Survey Open-File Report 2021–1022, 36 p., https://doi.org/10.3133/ofr20211022. Moreo, M.T., and Swancar, A., 2013, Evaporation from Lake Mead, Nevada and Arizona, March 2010 through February 2012: U.S. Geological Survey Scientific Investigations Report 2013–5229, 40 p., http://dx.doi.org/10.3133/sir20135229.

Graph and download economic data for Per Capita Personal Consumption Expenditures: Nondurable Goods: Gasoline and Other Energy Goods for Great Lakes BEA Region (GLAKPCEPCGAS) from 1997 to 2023 about Great Lakes BEA Region, nondurable goods, energy, gas, PCE, consumption expenditures, per capita, consumption, personal, goods, and USA.

Evaporation rates were measured at Lake Mead from March 2010 through February 2012 for phase 1 of an evaporation study (Moreo and Swancar, 2013). Phase 2 of the study (March 2012 through September 2017) continues evaporation measurements at Lake Mead and begins evaporation measurements at another lower Colorado River Basin reservoir, Lake Mohave. Eddy covariance is the primary measurement method. Data currently (10/6/2015) are being collected for the phase 2 study. This USGS data release represents tabular data in support of the evaporation study. The data release was produced in compliance with the new 'open data' requirements as way to make the scientific products associated with USGS research efforts and publications available to the public. The data release consists of 2 separate items: 1. Lake Mead evaporation data from March 2010 through April 2015 (Microsoft Excel workbook) 2. Lake Mohave evaporation data from May 2013 through April 2015 (Microsoft Excel workbook)

This dataset contains measurements from drifting deployments of the TigerRAY, a two-body wave energy converter, and four SWIFT buoys on Lake Washington and Puget Sound during February 2023. Tests were conducted under varying wave conditions, including natural waves, calm conditions with artificial boat wakes, and periods of power take-off (PTO) engagement and freewheeling.

Each data file includes a MATLAB structure containing motion, pressure, load, electrical, and heading measurements from TigerRAY's nacelle, heave plate, encoders, IMUs, and pressure sensors. Additional wave data were collected by tethered SWIFT buoys. Units are labeled and described in an accompanying data guide, which also details sensor configurations, data processing steps, and deployment notes.

Hourly ambient fine particulate matter (PM2.5) data in micrograms per cubic metre from provincial ambient air quality monitoring stations across Nova Scotia up to the end of 2023.

The datasets - Uganda Lakes, are sourced from the Ugandan Energy Sector GIS Working Group Open Data Site, developed and maintained by the Ugandan Energy Sector GIS Working Group. The Ugandan Energy Sector GIS Working Group’s mission is to develop a high quality GIS for the Energy Sector of Uganda in order to drive informed decision-making. As such, it brings datasets together in one place, organize them, keep them updated, and make public data available to all stakeholders. Link: http://data-energy-gis.opendata.arcgis.com/ The dataset is published on October 23, 2014

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.

This data was collected by the United States Geological Survey (USGS) for the Water, Energy, and Biogeochemical Budget Project. The data set is primarily composed of water chemistry variables, and was collected from four USGS stream gauge stations in the Northern Highland Lake District of Wisconsin, near Trout Lake. The four USGS stream gauge stations are Allequash Creek at County Highway M (USGS-05357215), Stevenson Creek at County Highway M (USGS-05357225), North Creek at Trout Lake (USGS-05357230), and the Trout River at Trout Lake (USGS-05357245), all near Boulder Junction, Wisconsin. The project has collected stream water chemistry data for a maximum of 36 different chemical parameters,. and three different physical stream parameters: temperature, discharge, and gauge height. All water chemistry samples are collected as grab samples and sent to the USGS National Water Quality Lab in Denver, Colorado. There is historic data for Stevenson Creek from 1975-1977, and then beginning again in 1991. The Trout Lake WEBB project began during the summer of 1991 and sampling of all four sites continues to date.

This dataset is comprised of instrumented buoy data collected on Emaiksoun Lake in Barrow, Alaska for the purposes of quantifying the over-lake meteorology and surface energy balance. Hourly mean weather, wave height, water temperature, and energy balance data were collected from a TIDAS-900 data buoy deployed near the center of the lake over a 4-year period (2012-2015). Primary responsible individuals include John Lenters and Brittany Potter, assisted at times by other CALON collaborators.

For many ectothermic animals, the acquisition, storage, and depletion of lipids is integral to successfully coping with reduced metabolic rates and activity levels associated with cold, winter periods. In fish, lipids are crucial for overwinter survival and successful reproduction. The timing and magnitude of seasonal lipid storage should therefore vary predictably among fish with different thermal preferences and spawn times. Small- and large-bodied fish should also face different constraints associated with season that influence lipid cycling. However, much work to date has been species- and location-specific and a general conceptual model for the seasonal energy budgets of freshwater fish is lacking. Here, we conducted a comprehensive literature review of seasonal lipid levels in freshwater fishes. We predicted that warm and cool water species would be more likely to demonstrate peak lipid levels during warm months than cold water species, and expected a larger magnitude of annual lipid cycling in warm and cool water compared to cold water fish. We also expected dampened lipid cycling in larger fish due to their lower mass-specific metabolic rates. Observed patterns in the timing and magnitude of lipid storage contradicted our prediction because lipid cycling was widespread across species, despite thermal guild, with peak lipid levels commonly occurring during warmer months, even in cold water fish. For body size effects, larger bodied fish species had dampened seasonal lipid cycling, as predicted. We developed a conceptual framework describing how the 'scope' for variation in annual lipid cycling changes with body size both among and within species in order to guide future work. Together, our findings suggest that energy acquired during warm months is broadly important for overwinter survival and reproduction in fishes, and provide a new perspective on the differential constraints and physiological responses to seasonality among freshwater fish.

2008-2009 bottom currents, turbidity, wind and waves in Lake Erie. The dataset is used for calculating bottom shear stress and evaluating bottom shear stress parameterization methods. Bottom shear stress is the driving force of sediment entrainment. Understanding bottom shear stress and being able to model it allows for better understanding of erosion and deposition in Lake Erie.

These data provide the 2024 update of the Electricity Annual Technology Baseline (ATB). Starting in 2015 NREL has presented the ATB, consisting of detailed cost and performance data, both current and projected, for electricity generation and storage technologies. The ATB products now include data (Excel workbook, Tableau workbooks, and structured summary csv files), as well as documentation and user engagement via a website, presentation, and webinar. Starting in 2021, the data are cloud optimized and provided in the OEDI data lake. The data for 2015 - 2020 are can be found on the NREL Data Search Page. The website documentation can be found on the ATB Website.

Prey preference of top predators and energy flow across habitat boundaries are of fundamental importance for structure and function of aquatic and terrestrial ecosystems, as they may have strong effects on production, species diversity, and food-web stability. In lakes, littoral and pelagic food-web compartments are typically coupled and controlled by generalist fish top predators. However, the extent and determinants of such coupling remains a topical area of ecological research and is largely unknown in oligotrophic high-latitude lakes. We analyzed food-web structure and resource use by a generalist top predator, the Arctic charr Salvelinus alpinus (L.), in 17 oligotrophic subarctic lakes covering a marked gradient in size (0.5–1084 km2) and fish species richness (2–13 species). We expected top predators to shift from littoral to pelagic energy sources with increasing lake size, as the availability of pelagic prey resources and the competition for littoral prey are both likely to be higher in large lakes with multispecies fish communities. We also expected top predators to occupy a higher trophic position in lakes with greater fish species richness due to potential substitution of intermediate consumers (prey fish) and increased piscivory by top predators. Based on stable carbon and nitrogen isotope analyses, the mean reliance of Arctic charr on littoral energy sources showed a significant negative relationship with lake surface area, whereas the mean trophic position of Arctic charr, reflecting the lake food-chain length, increased with fish species richness. These results were supported by stomach contents data demonstrating a shift of Arctic charr from an invertebrate-dominated diet to piscivory on pelagic fish. Our study highlights that, because they determine the main energy source (littoral vs. pelagic) and the trophic position of generalist top predators, ecosystem size and fish diversity are particularly important factors influencing function and structure of food webs in high-latitude lakes.