The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

As of March 2025, there were a reported 5,426 data centers in the United States, the most of any country worldwide. A further 529 were located in Germany, while 523 were located in the United Kingdom. What is a data center? A data center is a network of computing and storage resources that enables the delivery of shared software applications and data. These facilities can house large amounts of critical and important data, and therefore are vital to the daily functions of companies and consumers alike. As a result, whether it is a cloud, colocation, or managed service, data center real estate will have increasing importance worldwide. Hyperscale data centers In the past, data centers were highly controlled physical infrastructures, but the cloud has since changed that model. A cloud data service is a remote version of a data center – located somewhere away from a company's physical premises. Cloud IT infrastructure spending has grown and is forecast to rise further in the coming years. The evolution of technology, along with the rapid growth in demand for data across the globe, is largely driven by the leading hyperscale data center providers.

Snapshot img

Big Data Infrastructure Market Size 2024-2028

The big data infrastructure market size is forecast to increase by USD 1.12 billion, at a CAGR of 5.72% between 2023 and 2028. The growth of the market depends on several factors, including increasing data generation, increasing demand for data-driven decision-making across organizations, and rapid expansion in the deployment of big data infrastructure by SMEs. The market is referred to as the systems and technologies used to collect, process, analyze, and store large amounts of data. Big data infrastructure is important because it helps organizations capture and use insights from large datasets that would otherwise be inaccessible.

What will be the Size of the Market During the Forecast Period?

To learn more about this report, View Report Sample

Market Dynamics

In the dynamic landscape of big data infrastructure, cluster design, and concurrent processing are pivotal for handling vast amounts of data created daily. Organizations rely on technology roadmaps to navigate through the evolving landscape, leveraging data processing engines and cloud-native technologies. Specialized tools and user-friendly interfaces enhance accessibility and efficiency, while integrated analytics and business intelligence solutions unlock valuable insights. The market landscape depends on the Organization Size, Data creation, and Technology roadmap. Emerging technologies like quantum computing and blockchain are driving innovation, while augmented reality and virtual reality offer great experiences. However, assumptions and fragmented data landscapes can lead to bottlenecks, performance degradation, and operational inefficiencies, highlighting the need for infrastructure solutions to overcome these challenges and ensure seamless data management and processing. Also, the market is driven by solutions like IBM Db2 Big SQL and the Internet of Things (IoT). Key elements include component (solution and services), decentralized solutions, and data storage policies, aligning with client requirements and resource allocation strategies.

Key Market Driver

Increasing data generation is notably driving market growth. The market plays a pivotal role in enabling businesses and organizations to manage and derive insights from the massive volumes of structured and unstructured data generated daily. This data, characterized by its high volume, velocity, and variety, is collected from diverse sources, including transactions, social media activities, and Machine-to-Machine (M2M) data. The data can be of various types, such as texts, images, audio, and structured data. Big Data Infrastructure solutions facilitate advanced analytics, business intelligence, and customer insights, powering digital transformation initiatives across industries. Solutions like Azure Databricks and SAP Analytics Cloud offer real-time processing capabilities, advanced machine learning algorithms, and data visualization tools.

Digital Solutions, including telecommunications, social media platforms, and e-commerce, are major contributors to the data generation. Large Enterprises and Small & Medium Enterprises (SMEs) alike are adopting these solutions to gain a competitive edge, improve operational efficiency, and make data-driven decisions. The implementation of these technologies also addresses security concerns and cybersecurity risks, ensuring data privacy and protection. Advanced analytics, risk management, precision farming, virtual assistants, and smart city development are some of the industry sectors that significantly benefit from Big Data Infrastructure. Blockchain technology and decentralized solutions are emerging trends in the market, offering decentralized data storage and secure data sharing. The financial sector, IT, and the digital revolution are also major contributors to the growth of the market. Scalability, query languages, and data valuation are essential factors in selecting the right Big Data Infrastructure solution. Use cases include fraud detection, real-time processing, and industry-specific applications. The market is expected to continue growing as businesses increasingly rely on data for decision-making and digital strategies. Thus, such factors are driving the growth of the market during the forecast period.

Significant Market Trends

Increasing use of data analytics in various sectors is the key trend in the market. In today's digital transformation era, Big Data Infrastructure plays a pivotal role in enabling businesses to derive valuable insights from vast amounts of data. Large Enterprises and Small & Medium Enterprises alike are adopting advanced analytical tools, including Azure Databricks, SAP Analytics Cloud, and others, to gain customer insights, improve operational efficiency, and enhance business intelligence. These tools facilitate the use of Artificial Intelligence (AI) and Machine Learning (ML) algorithms for predictive analysis, r

Big Data Infrastructure Market Outlook

The global Big Data Infrastructure market size was valued at approximately $98 billion in 2023 and is projected to grow to around $235 billion by 2032, exhibiting a compound annual growth rate (CAGR) of about 10.1% during the forecast period. This impressive growth can be attributed to the increasing demand for big data analytics across various sectors, which necessitates robust infrastructure capable of handling vast volumes of data effectively. The need for real-time data processing has also been a significant driver, as organizations seek to harness data to gain competitive advantages, improve operational efficiencies, and enhance customer experiences.

One of the primary growth factors driving the Big Data Infrastructure market is the exponential increase in data generation from digital sources. With the proliferation of connected devices, social media, and e-commerce, the volume of data generated daily is staggering. Organizations are realizing the value of this data in gaining insights and making informed decisions. Consequently, there is a growing demand for infrastructure solutions that can store, process, and analyze this data effectively. Additionally, developments in cloud computing have made big data technology more accessible and affordable, further fueling market growth. The ability to scale resources on-demand without significant upfront capital investment is particularly appealing to businesses.

Another critical factor contributing to the growth of the Big Data Infrastructure market is the advent of advanced technologies such as artificial intelligence, machine learning, and the Internet of Things (IoT). These technologies require sophisticated data management solutions capable of handling complex and large-scale data sets. As industries across the spectrum from healthcare to manufacturing integrate these technologies into their operations, the demand for capable infrastructure is scaling correspondingly. Moreover, regulatory requirements around data management and security are prompting organizations to invest in reliable infrastructure solutions to ensure compliance and safeguard sensitive information.

The role of data analytics in shaping business strategies and operations has never been more pertinent, driving organizations to invest in Big Data Infrastructure. Businesses are keenly focusing on customer-centric approaches, understanding market trends, and innovating based on data-driven insights. The ability to predict trends, consumer behavior, and potential challenges offers a significant strategic advantage, further pushing the demand for robust data infrastructure. Additionally, strategic partnerships between technology providers and enterprises are fostering an ecosystem conducive to big data initiatives.

From a regional perspective, North America currently holds the largest share in the Big Data Infrastructure market, driven by the early adoption of advanced technologies and the presence of major technology companies. The region's strong digital economy and a high degree of IT infrastructure sophistication are further bolstering its market position. Europe is expected to follow suit, with significant investments in data infrastructure to meet regulatory standards and drive digital transformation. The Asia Pacific region, however, is anticipated to witness the highest growth rate, attributed to rapid digitalization, the proliferation of IoT devices, and increasing awareness of the benefits of big data analytics among businesses. Other regions like Latin America and the Middle East & Africa are also poised for growth, albeit at a relatively moderate pace, as they continue to embrace digital technologies.

Component Analysis

In the realm of Big Data Infrastructure, the component segment is categorized into hardware, software, and services. The hardware segment consists of the physical pieces needed to store and process big data, such as servers, storage devices, and networking equipment. This segment is crucial because the efficiency of data processing depends significantly on the capabilities of these physical components. With the rise in data volumes, there’s an increased demand for scalable and high-performance hardware solutions. Organizations are investing heavily in upgrading their existing hardware to ensure they can handle the data influx effectively. Furthermore, the development of advanced processors and storage systems is enabling faster data processing and retrieval, which is critical for real-time analytics.

The software segment of Big Data Infrastructure encompasses analytics soft

This Level-2G daily global gridded product OMCLDRRG is based on the pixel level OMI Level-2 CLDRR product OMCLDRR. This level-2G global cloud product (OMCLDRRG) provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The algorithm lead for the products OMCLDRR and OMCLDRRG is NASA OMI scientist Dr. Joanna Joinner. OMCLDRRG data product is a special Level-2G Gridded Global Product where pixel level data (OMCLDRR)are binned into 0.25x0.25 degree global grids. It contains the OMCLDRR data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without Averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products.The OMCLDRRG data products are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each daily file contains data from the day lit portion of the orbits (~14 orbits). The average file size for the OMCLDRRG data product is about 75 Mbytes.

Online Cloud Backup Market Outlook

The global online cloud backup market size was estimated at USD 6.5 billion in 2023 and is projected to reach USD 15.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.3% during the forecast period. This remarkable growth is driven by the increasing need for data security, the convenience of cloud-based solutions, and the growing adoption of digital transformation across various industries. The reliance on cloud backup solutions is expanding as more businesses recognize the critical importance of safeguarding their data against loss, breaches, and other threats.

One of the primary factors contributing to the growth of the online cloud backup market is the ever-increasing volume of data generated by individuals and organizations. Today's digital age sees an unprecedented amount of data being created daily, encompassing everything from personal photos and videos to business-critical information. The need to securely store and easily retrieve this data is a significant driver for cloud backup solutions. Companies are increasingly leveraging these solutions to ensure business continuity, data recovery, and compliance with regulatory requirements, thereby fueling market growth.

Another significant growth factor is the rising awareness and concern regarding cybersecurity threats. With the growing sophistication of cyber-attacks, businesses are prioritizing robust backup solutions to protect their data from ransomware, malware, and other cyber threats. Cloud backup solutions offer advanced security features, such as encryption, multi-factor authentication, and regular updates, which help mitigate these risks. As a result, the demand for cloud backup services is escalating among enterprises of all sizes, especially those in sectors with stringent data protection regulations such as BFSI and healthcare.

The trend of remote working and the adoption of hybrid work models also significantly bolster the online cloud backup market. The COVID-19 pandemic has accelerated the shift towards remote work, leading to an increased reliance on cloud-based technologies to ensure seamless business operations. Cloud backup solutions facilitate remote access to data, enabling employees to work from any location while ensuring data integrity and security. This flexibility and resilience provided by cloud backups are crucial for businesses striving to maintain productivity and operational efficiency in an increasingly distributed work environment.

Online Backup Software plays a pivotal role in the current digital landscape by providing businesses and individuals with the tools necessary to safeguard their data efficiently. As the volume of digital data continues to grow exponentially, the need for reliable and secure backup solutions becomes increasingly critical. Online Backup Software offers automated backup features, ensuring that data is consistently protected without manual intervention. This software not only facilitates data recovery in the event of accidental deletion or hardware failure but also provides peace of mind by securing data against potential cyber threats. With the rise of remote work and digital transformation, Online Backup Software is becoming an indispensable component of modern IT infrastructure, enabling seamless data management and protection.

Regionally, North America dominates the online cloud backup market due to the early adoption of advanced technologies and the presence of key market players. The region's well-established IT infrastructure and significant investments in cloud computing technologies contribute to its leading position. Meanwhile, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid digitalization, increasing internet penetration, and growing awareness of data security. The burgeoning SME sector in countries like India and China further propels the demand for cost-effective cloud backup solutions.

Component Analysis

The online cloud backup market is segmented by components into software and services. The software segment encompasses solutions that enable the backup, storage, and recovery of data on the cloud. These software solutions are equipped with features such as automated backups, data compression, and encryption, which enhance data security and efficiency. The rise in data generation and the need for scalable storage solutions drive the demand for cloud backup software. Enterprises are

The MODIS/Terra Clouds 5-Min L2 Swath 1km and 5km (MOD06_L2) product consists of cloud optical and physical parameters. These parameters are derived using remotely sensed infrared, visible and near infrared solar reflected radiances. MODIS infrared channel radiances are used to derive cloud top temperature, cloud top height, effective emissivity, cloud phase (ice vs. water, opaque vs. non-opaque), and cloud fraction under both daytime and nighttime conditions. MODIS visible radiances are used to derive cloud optical thickness and effective particle radius and cloud shadow effects. Near infrared solar reflected radiance provides additional information in the retrieval of cloud particle phase (ice vs. water, clouds vs. snow). The Shortname for this level-2 MODIS cloud product is MOD06_L2.

MOD06_L2 consists of parameters at a spatial resolution of either 1- km or 5-km (at nadir). Each MOD06_L2 product file covers a five-minute time interval. This means that for 5-km resolution parameters, the output grid is 270 pixels in width by 406 pixels in length. Every tenth granule has an output grid size of 270 by 408 pixels. For 1-km resolution parameters, the output grid is 1354 pixels in width by 2030 pixels in length and every tenth granule has an output grid size of 1354 by 2040 pixels.

MOD06_L2 product files are stored in Hierarchical Data Format (HDF-EOS). All gridded cloud parameters are stored as Scientific Data Sets (SDS) within the file, except two (band number and statistics). These are stored as Vdata(table arrays). Approximately 288 files are produced daily. Nighttime files are smaller than their daytime counterparts since only cloud top properties are retrieved at night.

The Collection 6 (C6) cloud-top property product changes include new 1 km SDSs in addition to the legacy 5 km SDSs, and a new physical cloud height dataset. New 1 km products include cloud top pressure, IR cloud emissivity, cloud height, overshooting cloud top flag, and cloud phase. The 1 km IR phase product is generated using an updated algorithm while the other 1 km products were designed to be as similar as possible to the 5 km version.

The C6 Cloud optical/microphysical product changes include expanded/improved pixel-level retrieval uncertainty calculations that are intended to be used in lieu of retrieval QA assignments that are no longer assigned (legacy QA bits are now set to a value of 3 for all successful retrievals). Retrievals are now attempted on a pixel flagged as likely to have a partly cloudy field of view by the so-called clear sky restoral algorithm introduced in C5 processing. Successful retrievals from this pixel population have similar SDS names as the non-flagged pixel population but with "_PCL" (for Partly CLoudy) appended to the end of the parameter name; an exception is the uncertainty SDSs where both PCL and non-PCL retrieval uncertainties are kept. Metrics for unsuccessful retrievals (those outside the look-up table solution space) are now provided as well (Retrieval_Failure_Metric_*). Finally, absolute retrievals are provided for the other visible and near-infrared (VIS/NIR) and shortwave infrared (SWIR) band pairs (VIS/NIR at 1.6 microns, VIS/NIR at 3.7 microns) instead of as differences relative to the VIS/NIR at 2.1 microns retrieval. For more details regarding dataset changes read the document at:

https://modis-atmos.gsfc.nasa.gov/products_C006update.html.

The MODIS Cloud Product will be used to investigate seasonal and inter-annual changes in cirrus (semi-transparent) global cloud cover and cloud phase with multispectral observations at high spatial (1 kilometer) resolution.

For more information about the MYD06_L2 product, visit the MODIS-Atmosphere site at: https://modis-atmos.gsfc.nasa.gov/products/cloud or See the MODIS Science Team homepage for more data set information at:

https://modis.gsfc.nasa.gov/data/dataprod/

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. This catalogue entry provides post-processed ERA5 hourly single-level data aggregated to daily time steps. In addition to the data selection options found on the hourly page, the following options can be selected for the daily statistic calculation:

The daily aggregation statistic (daily mean, daily max, daily min, daily sum*) The sub-daily frequency sampling of the original data (1 hour, 3 hours, 6 hours) The option to shift to any local time zone in UTC (no shift means the statistic is computed from UTC+00:00)

*The daily sum is only available for the accumulated variables (see ERA5 documentation for more details). Users should be aware that the daily aggregation is calculated during the retrieval process and is not part of a permanently archived dataset. For more details on how the daily statistics are calculated, including demonstrative code, please see the documentation. For more details on the hourly data used to calculate the daily statistics, please refer to the ERA5 hourly single-level data catalogue entry and the documentation found therein.

Big Data Platform Market Outlook

The global Big Data Platform market size is projected to grow from USD 75.0 billion in 2023 to USD 156.5 billion by 2032, expanding at a CAGR of 8.5% during the forecast period. This impressive growth is driven by the increasing demand for data-driven decision-making, the proliferation of IoT devices, and the rising importance of Big Data analytics in various industries. As enterprises strive to gain a competitive edge, the adoption of Big Data platforms has become essential in harnessing the power of data for strategic insights and operational efficiency.

One of the primary growth factors in the Big Data Platform market is the exponential increase in the volume of data generated globally. With the advent of advanced technologies such as IoT, AI, and machine learning, the amount of data produced daily has skyrocketed. Organizations are increasingly relying on Big Data platforms to store, process, and analyze this vast amount of data, thereby driving market growth. Furthermore, the integration of advanced analytics and real-time data processing capabilities in these platforms is enabling businesses to derive actionable insights, enhance customer experiences, and optimize operational processes, thereby fueling market expansion.

Another significant factor contributing to the growth of the Big Data Platform market is the rising adoption of cloud-based solutions. Cloud computing offers scalable, flexible, and cost-effective infrastructure for Big Data analytics, making it an attractive option for organizations of all sizes. The ability to access and analyze data from anywhere, coupled with the advantages of reduced IT costs and increased operational efficiency, is propelling the demand for cloud-based Big Data platforms. Additionally, advancements in cloud data security and privacy measures are alleviating concerns related to data breaches, further encouraging the shift towards cloud-based solutions.

The increasing need for regulatory compliance and risk management is also driving the growth of the Big Data Platform market. In industries such as BFSI, healthcare, and government, stringent regulations mandate the accurate and secure handling of large volumes of data. Big Data platforms provide the necessary tools and frameworks to ensure compliance with regulatory requirements, thereby mitigating risks associated with data breaches and non-compliance. As organizations strive to adhere to these regulations while managing their data efficiently, the demand for robust Big Data platforms continues to rise.

The emergence of Big Data Technology and Service has revolutionized the way organizations approach data management and analytics. These technologies provide a comprehensive suite of tools and services that enable businesses to collect, store, and analyze vast amounts of data with unprecedented efficiency. By leveraging Big Data Technology and Service, companies can gain deeper insights into their operations, customer behavior, and market trends, leading to more informed decision-making and strategic planning. The integration of these technologies into existing IT infrastructures allows for seamless data processing and real-time analytics, empowering organizations to respond swiftly to changing business dynamics and maintain a competitive edge in the market.

From a regional perspective, North America is expected to dominate the Big Data Platform market during the forecast period, owing to the high concentration of tech-savvy enterprises and the presence of major market players in the region. Additionally, the early adoption of advanced technologies and the significant investments in R&D activities contribute to the region's market leadership. However, the Asia Pacific region is anticipated to witness the highest growth rate, driven by the increasing digital transformation initiatives, rapid adoption of cloud services, and expanding IT infrastructure in emerging economies such as China and India.

Component Analysis

The Big Data Platform market is segmented by components into software, hardware, and services. The software segment holds the largest market share, driven by the increasing adoption of Big Data analytics tools and solutions. These software solutions encompass a wide range of functionalities, including data management, data integration, and advanced analytics, which are essential for processing and analyzing large datasets. The demand for sophisticated analytics software is further fueled by th

The level-2 MODIS cloud product consists of cloud optical and physical parameters. These parameters are derived using remotely sensed infrared, visible and near infrared solar reflected radiances. MODIS infrared channel radiances are used to derive cloud top temperature, cloud top height, effective emissivity, cloud phase (ice vs. water, opaque vs. non-opaque), and cloud fraction under both daytime and nighttime conditions. MODIS visible radiances are used to derive cloud optical thickness and effective particle radius and cloud shadow effects. Near infrared solar reflected radiance provides additional information in the retrieval of cloud particle phase (ice vs. water, clouds vs. snow). The shortname for this level-2 MODIS cloud product is MYD06_L2.MYD06_L2 consists of parameters at a spatial resolution of either 1- km or 5-km (at nadir). Each MYD06_L2 product file covers a five-minute time interval. This means that for 5-km resolution parameters, the output grid is 270 pixels in width by 406 pixels in length. Every tenth granule has an output grid size of 270 by 408 pixels. For 1-km resolution parameters, the output grid is 1354 pixels in width by 2030 pixels in length and every tenth granule has an output grid size of 1354 by 2040 pixels.MYD06_L2 product files are stored in Hierarchical Data Format (HDF-EOS). All gridded cloud parameters are stored as Scientific Data Sets (SDS) within the file, except two (band number and statistics). These are stored as Vdata(table arrays). Approximately 288 files are produced daily. Nighttime files are smaller than their daytime counterparts since only cloud top properties are retrieved at night.The Collection 6 cloud-top property product changes include new 1 km SDSs in addition to the legacy 5 km SDSs, and a new physical cloud height dataset. New 1 km products include cloud top pressure, IR cloud emissivity, cloud height, overshooting cloud top flag, and cloud phase. The 1 km IR phase product is generated using an updated algorithm while the other 1 km products were designed to be as similar as possible to the 5 km version.And, the Collection 6 Cloud optical/microphysical product changes include expanded/improved pixel-level retrieval uncertainty calculations that are intended to be used in lieu of retrieval QA assignments that are no longer assigned (legacy QA bits are now set to a value of 3 for all successful retrievals). Retrievals are now attempted on a pixel flagged as likely to have a partly cloudy field of view by the so-called clear sky restoral algorithm introduced in C5 processing. Successful retrievals from this pixel population have similar SDS names as the non-flagged pixel population but with ???PCL??? (for Partly CLoudy) appended to the end of the parameter name; an exception is the uncertainty SDSs where both PCL and non-PCL retrieval uncertainties are kept. Metrics for unsuccessful retrievals (those outside the look-up table solution space) are now provided as well (Retrieval_Failure_Metric*). Finally, absolute retrievals are provided for the other visible and near-infrared (VNIR) and shortwave infrared band pairs (VNIR and 1.6 ??m, VNIR and 3.7 ??m) instead of as differences relative to the VNIR/2.1 ??m retrieval. For more details regarding dataset changes read the document at http://modis-atmos.gsfc.nasa.gov/products_C006update.html.MYD06_L2 Data Category & ParametersSpatial and Temporal Resolution:latitude,longitudescan start timeSolar and Sensor Viewing Geometry:Solar_Azimuth(daytime,nighttime,all), Solar_Zenith(daytime,nighttime,all), Sensor_Azimuth(daytime,nighttime,all), Sensor_Zenith(daytime,nighttime,all)Cloud Top Parameters (5km):Cloud_Effective_Emissivity_(Day, Night, Nadir, Nadir_Day,Nadir_Night, All), Cloud_Fraction_(Day, Night, Nadir, Nadir_Day,Nadir_Night, All), Cloud_Height_Method, Cloud_Phase_Infrared_(day, night, all), Cloud_Top_Height_(Day, Night, Nadir, Nadir_Day,Nadir_Night, All), cloud_top_method_1km, Cloud_Top_Pressure_From_Ratios, Cloud_Top_Pressure_Infrared, Cloud_Top_Pressure_(Day, Night, Nadir, Nadir_Day,Nadir_Night, All), Cloud_Top_Temperature_(Day, Night, Nadir, Nadir_Day,Nadir_Night, All)Cloud Top Parameters (1km):cloud_top_height_1km, cloud_top_pressure_1km, cloud_top_temperature_1km, , cloud_emiss(11, 12, 13, 85, All)1km, IRW_Low_Cloud_Temperature_From_COPCloud Optical Properties (1km):Above_Cloud_Water_Vapor_094, Cloud_Effective_Radius(PCL, 16, 16_PCL,1621, 1621_PCL,37,37_PCL, All) , Cloud_Optical_Thickness_(PCL, 16, 16_PCL,1621, 1621_PCL,37,37_PCL, All), Cloud_Phase_Infrared_1km, Cloud_Phase_Optical_Properties, Cloud_Water_Path_(PCL, 16, 16_PCL,1621, 1621_PCL,37,37_PCL, All)Radiation Parameters:Radiance_Variance, Brightness_Temperature, Spectral_Cloud_Forcing, Surface_Pressure, Surface_Temperature, surface_temperature_1km, Atm_Corr_Refl, Cirrus_ReflectanceQuality Assurance Parameters:Processing flag for 5 km resolution, Cloud_Multi_Layer_Flag, cirrus reflectance flag for 1 km resolution, quality assurance flag for 1 and 5 km resolution, Retrieval_Failure_Metric_(16,1621,37, All), Cloud_Optical_Thickness_Uncertainty_(16,1621,37, All), Cloud_Water_Path_Uncertainty_(16,1621,37, All), Cloud_Effective_Radius_Uncertainty_(16,1621,37, All), cloud mask for 1km, 5km, and SPI, radiance variance for thermal bands, statistics for parameters at 1km resolutionThe MODIS Cloud Product will be used to investigate seasonal and inter-annual changes in cirrus (semi-transparent) global cloud cover and cloud phase with multispectral observations at high spatial (1 kilometer) resolution.For more information about the MYD06_L2 product, visit the MODIS-Atmosphere site at:http://modis-atmos.gsfc.nasa.gov/MOD06_L2/

This de-duped dataset is used by our customers for many purposes, primarily to understand which countries the people who visit specific locations (more accurately, the mobile devices carried by those people) - perhaps the locations that they own/operate, perhaps those owned/operated by their competitors, or visited by their customers - originated.

If, for instance, you operate a hotel brand and want to understand the top ten countries that visitors to your City came from; if/how that changes seasonally over time, and by type of location (perhaps higher end visitors are more likely to come from the UK or Germany versus France or Italy) - to help you build out your data models or marketing in those countries and/or to help tailor your product offers towards their needs.

This data can be useful as a way to understand, for instance, whether there are specific geographical areas you might consider putting a new location; where you might buy billboard ads, advertising the ‘local’ store; to build your own mobility data models to help better understand visitation into your own/your competitors premises, or test hypotheses around changes in visitation patterns over time.

The Intuizi Country Origin Dataset comprises fully-consented mobile device data, de-identified at source by the entity which has legal consent to own/process such data, and on who’s behalf we work to create a de-identified dataset of Encrypted ID visitation/mobility data.

MODIS was launched aboard the Terra satellite on December 18, 1999 (10:30 am equator crossing time) as part of NASA's Earth Observing System (EOS) mission. MODIS with its 2330 km viewing swath width provides almost daily global coverage. It acquires data in 36 high spectral resolution bands between 0.415 to 14.235 micron with spatial resolutions of 250m(2 bands), 500m(5 bands), and 1000m (29 bands). MODIS sensor counts, calibrated radiances, geolocation products and all derived geophysical atmospheric and ocean products are archived at NASA Goddard DAAC and has been made available to public since April 2000.

       This is a level-3 MODIS gridded atmosphere daily global joint product
       'MOD08_D3'. It contains daily 1 x 1 degree grid average values of
       atmospheric parameters related to atmospheric aerosol particle
       properties, total ozone burden, atmospheric water vapor, cloud optical
       and physical properties, and atmospheric stability indices. This
       product also provides standard deviations, quality assurance weighted
       means and other statistically derived quantities for each parameter.
       The shortname for this level-3 MODIS atmosphere daily global product
       is MOD08_D3 and the principal investigator for this product are MODIS
       scientists :

       Dr. Yoram Kaufman for aerosol retrieval over land;
       Dr. Didier Tanre ( didier.tanre@univ-lille1.fr) for aerosol retrieval
       over ocean;
       Dr. Bo-Cai Gao (gao@rsd.nrl.navy.mil) for cirrus cloud detection;
       Dr. Paul Menzel (paulm@ssec.wisc.edu) for cloud top properties ; and
       Dr. Michael King(king@climate.gsfc.nasa.gov) for cloud optical
       properties.

       The level-3 atmosphere daily global product (MOD08_D3) consists of
       approximately 500 parameters that includes all statistically derived
       quantities. Essentially all level-3 MODIS atmosphere daily global
       parameters are derived from the four level-2 MODIS atmosphere products
       MOD04_L2, MOD05_L2, MOD06_L2, and MOD07_L2. Statistics are computed
       over a 1 degree equal-angle lat-lon grid that spans a 24-hour (0000 to
       2400 Greenwich Mean Time) interval. Since the grid cells are 1 degree
       by 1 degree, the output grid is always 360 pixels in width and 180
       pixels in length.

       MOD08_D3 product files are stored in Hierarchical Data Format
       (HDF-EOS). Each gridded global parameter is stored as Scientific Data
       Sets (SDS) within the file. For browsing and extracting data from
       these files, some software are made available at the site:
       http://ladsweb.nascom.nasa.gov/tools/ .


       MOD08_D3 PARAMETERS

       Aerosol over Land and Ocean:
       Optical Depth (0.55 micron)

       Aerosol over Land:
       Optical Depth at 0.47 and 0.66 (based on Continental Aerosol Model)
       Corrected Optical Depths at 0.47, 0.55, 0.66 mm (based on Dynamic
       Aerosol Model)
       Corrected Optical Depths at 0.47, 0.55, 0.66 mm for Smoke, Sulfate,
       Dust Aerosols
       Mass Concentration (based on 0.66 mm)
       Angstrom Exponents for Smoke, Sulfate, Dust

       Aerosol over Ocean:
       Effective Optical Depth at 7 bands (0.47, 0.55, 0.66, 0.87, 1.24,
       1.64, 2.13 mm)
       Effective Radius, Mass Concentration, Cloud Condensation Nuclei
       (based on 0.55 mm)
       Ratio: Optical Depth of Small Particles relative to Effective
       Optical Depth (0.55 mm)
       Asymmetry Factor & Back Scattering Ratio at 7 bands
       Angstrom Exponents 1 (based on 0.55 and 0.865 mm) & 2 (based on
       0.865 and 2.13 mm)


       Ozone:
       Total Column Ozone

       Water Vapor:
       Total Column Precipitable Water Vapor
       NIR based (clear, cloudy), IR based (lower, upper levels, total)

       Cloud:
       Cloud Top Parameters
       Cloud Top Pressure, Temperature, and Effective Emissivity(day, night, all)
       Cloud Particle Phase
       Infrared & Visible based retrievals (day, night, all)
       Cloud Optical Thickness and Effective Radius
       (water, ice, water + ice, undetermined, all phases)
       Cloud Water Path
       (liquid, ice, liquid + ice, undetermined, all phases)
       Cloud Fraction
       Visible and SWIR based (cirrus, contrail, water, ice, water + ice,
       undetermined, all phases) & IR based (day, night, all)

       Atmospheric State & Stability Indices:
       Total -Totals, Lifted Index, K Index

       Radiation Parameters:
       Cirrus Cloud & Contrail Reflectance
       Mean Surface Reflectance for Land (5 bands) and Ocean (7 bands)
       Normalized Reflected Flux for Land (0.47 and 0.66 mm) and Ocean (7 bands)
       Normalized Transmitted Flux for Land (0.47 and 0.66 mm) and Ocean (7 bands)
       Atmosphreic Scattering Angle for Land and Ocean

       Quality Assurance (QA) & Statistical Parameters:
       Uncertainty in Derived Parameters, Minimum, Maximum, Number of Pixels,
       Histograms, Correlation Parameters, QA Weighted, and other
       Statistically Derived Parameters

       More information about the atmospheric parameters and the
       statistically derived parameters of MOD08_D3 product listed in the
       table above are available from the MOD08_D3 web site.

       The MODIS Daily Product will be used in the simultaneously study of
       clouds, water vapor, aerosol , trace gases, land surface and oceanic
       properties, as well as the interaction between them and their effect
       on the Earth's energy budget and climate. This product will also be
       used to investigate seasonal and inter-annual changes in cirrus
       (semi-transparent) global cloud cover and cloud phase with
       multispectral observations at high spatial resolution.

       For more information about the MOD08_D3 product, please visit the
       MODIS-Atmosphere site at:
       http://modis-atmos.gsfc.nasa.gov/MOD08_D3/

Object Storage Tool Market Outlook

The global object storage tool market size in 2023 is valued at USD 5.8 billion and is projected to reach USD 23.4 billion by 2032, growing at a CAGR of 16.5% during the forecast period. The market growth is driven by the increasing demand for scalable, high-capacity storage solutions due to the exponential growth of data generated daily. Enterprises across various sectors are rapidly adopting object storage solutions to address the challenges of managing and storing large volumes of unstructured data efficiently.

One significant growth factor in the object storage tool market is the surge in data generation across various industries. With the proliferation of IoT devices, social media, digital transactions, and multimedia content, organizations are witnessing an unprecedented data explosion. Traditional storage methods, such as block and file storage, are struggling to keep up with this data surge, leading to a shift towards object storage solutions that offer scalable and cost-effective options for managing massive datasets. This shift is further fueled by the growing prominence of big data analytics, artificial intelligence, and machine learning, which require robust storage infrastructures to process and analyze vast amounts of information.

Another key driver of market growth is the increasing adoption of cloud-based storage solutions. Cloud object storage offers several advantages, such as cost savings, scalability, and accessibility from anywhere with an internet connection. Enterprises are leveraging cloud storage to store and manage their data more efficiently, reducing the need for on-premises infrastructure and maintenance costs. Additionally, cloud service providers are continuously enhancing their storage offerings with advanced features like data encryption, redundancy, and disaster recovery, making cloud object storage a reliable and secure option for businesses of all sizes.

The rising demand for data security and compliance is also contributing to the growth of the object storage tool market. As data breaches and cyber threats become more sophisticated, organizations are prioritizing the protection of their sensitive information. Object storage solutions provide robust security features, including encryption, access controls, and immutability, ensuring that data remains protected throughout its lifecycle. Moreover, regulatory requirements such as GDPR, HIPAA, and CCPA mandate stringent data protection measures, prompting enterprises to adopt storage solutions that comply with these regulations.

On the regional front, North America dominates the object storage tool market, accounting for the largest market share in 2023. The region's technological advancements, coupled with a high adoption rate of cloud-based solutions, drive the market's growth. Moreover, the presence of major cloud service providers and technology giants in North America further boosts the demand for object storage tools. Europe and the Asia Pacific regions follow closely, with significant market shares attributed to the rapid digital transformation initiatives and increasing investments in cloud infrastructure. The Middle East & Africa and Latin America regions are also witnessing steady growth, fueled by the growing awareness of data storage solutions and the expansion of IT infrastructure.

Component Analysis

The object storage tool market can be segmented by component into software, hardware, and services. The software segment encompasses various object storage solutions and platforms that enable organizations to manage and store their data efficiently. This segment is witnessing significant growth due to the increasing demand for scalable and flexible storage solutions. Software-defined storage (SDS) is gaining traction as it allows enterprises to decouple storage hardware from software, offering greater flexibility and cost savings. Additionally, advancements in software technologies, such as data deduplication, compression, and tiering, are enhancing the performance and efficiency of object storage solutions.

Hardware components, including storage servers, networking equipment, and storage devices, are essential for deploying object storage solutions. The hardware segment is driven by the need for high-performance and reliable storage infrastructure to support the growing data volumes. Enterprises are investing in advanced storage hardware with features like high-capacity drives, NVMe (Non-Volatile Memory Express) technology, and all-flash arrays to optimize data storage and retrieval speeds. Additionally, the integration of h

In 2024, the number of data compromises in the United States stood at 3,158 cases. Meanwhile, over 1.35 billion individuals were affected in the same year by data compromises, including data breaches, leakage, and exposure. While these are three different events, they have one thing in common. As a result of all three incidents, the sensitive data is accessed by an unauthorized threat actor. Industries most vulnerable to data breaches Some industry sectors usually see more significant cases of private data violations than others. This is determined by the type and volume of the personal information organizations of these sectors store. In 2024 the financial services, healthcare, and professional services were the three industry sectors that recorded most data breaches. Overall, the number of healthcare data breaches in some industry sectors in the United States has gradually increased within the past few years. However, some sectors saw decrease. Largest data exposures worldwide In 2020, an adult streaming website, CAM4, experienced a leakage of nearly 11 billion records. This, by far, is the most extensive reported data leakage. This case, though, is unique because cyber security researchers found the vulnerability before the cyber criminals. The second-largest data breach is the Yahoo data breach, dating back to 2013. The company first reported about one billion exposed records, then later, in 2017, came up with an updated number of leaked records, which was three billion. In March 2018, the third biggest data breach happened, involving India’s national identification database Aadhaar. As a result of this incident, over 1.1 billion records were exposed.

This dataset includes daily two-band Enhanced Vegetation Index (EVI2) at 30-m resolution over a Landsat scene (path 26 and row 31) in central Iowa. Fourteen years of daily EVI2 from 2001 to 2015 (except 2012) were generated through fusing and interpolating Landsat-MODIS data.Landsat surface reflectances were order and used in this study. Mostly clear Landsat images from each year were chosen to pair with MODIS images acquired from the same day to generate daily Landsat-MODIS surface reflectance using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). Partially clear Landsat images were also used in generating the smoothed and gap-filled daily VI time-series. All available Landsat data including Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) were used in this study.The MODIS data products were downloaded and processed. These include the daily surface reflectance at both 250m (MOD09GQ) and 500m (MOD09GA) resolution, the MODIS Bidirectional Reflectance Distribution Function (BRDF) parameters at 500m resolution, and the MODIS land cover types at 500m resolution (MCD12Q1). They were used to generated daily nadir BRDF-adjusted reflectance (NBAR) at 250m resolution for fusing with Landsat.The Landsat-MODIS data fusion results for 2001-2014 were generated from a previous study (Gao et al, 2017; doi: 10.1016/j.rse.2016.11.004). Data fusion results for 2015 were generated using Landsat 8 OLI images from day 194, 226, 258 and 338 in this study. Cloud masks were extracted from Landsat and MODIS QA layers and were used to exclude cloud, cloud shadow and snow pixels. Since Landsat 5 TM operational imaging ended in November 2011 and Landsat 8 OLI has not been launched until February 2013, Landsat 7 ETM+ Scan Line Corrector (SLC)-off images are the only available Landsat data. For this reason, 2012 was not included.Due to the cloud contamination in the Landsat and MODIS images, the fused Landsat-MODIS results still have invalid values or gaps. To fill these gaps, a modified Savitzky-Golay (SG) filter approach was built and applied to smooth and gap-fill EVI2. The SG filter is a moving fitting approach. Each point is smoothed using the value computed from the polynomial function fit to the observations within the moving window. The program removes spike points if the fitting errors are larger than the predefined threshold (default 3 standard deviation). The modified SG filter allows us to retain small variations but also fill large gaps in an unevenly distributed time-series EVI2.Daily EVI2 files are saved in one tar file per year. Each tar file contains a binary image file and a text header file that can be displayed in the ENVI software. The binary image file has the dimension of 7201 lines by 8061 samples by 365 days and is saved in BIP (band interleaved by pixel) format. EVI2 data are saved in 4-byte float number. The text header file contains necessary information including projection and geolocation. Daily EVI2 file is named as "flexfit_evi2.026031.yyyy.bin", where "026031" refers to the Landsat path and row, and yyyy represents year and ranges from 2001-2015.Resources in this dataset:Resource Title: Daily EVI2 Data Packages .File Name: Web Page, url: https://app.globus.org/file-manager?origin_id=904c2108-90cf-11e8-9672-0a6d4e044368&origin_path=/LTS/ADCdatastorage/NAL/published/node22870/These Daily EVI2 data packages are grouped by year. Each package includes a plain binary file that saves daily EVI2, and a ENVI header file (in text) that contains metadata and geolocation information. Contents are as follows: dailyVI.026031.2000.tar.gz dailyVI.026031.2001.tar.gz dailyVI.026031.2002.tar.gz dailyVI.026031.2003.tar.gz dailyVI.026031.2004.tar.gz dailyVI.026031.2005.tar.gz dailyVI.026031.2006.tar.gz dailyVI.026031.2007.tar.gz dailyVI.026031.2008.tar.gz dailyVI.026031.2009.tar.gz dailyVI.026031.2010.tar.gz dailyVI.026031.2011.tar.gz dailyVI.026031.2013.tar.gz dailyVI.026031.2014.tar.gz dailyVI.026031.2015.tar.gzSCINet users: The .tar.gz files can be accessed/retrieved with valid SCINet account at this location: /LTS/ADCdatastorage/NAL/published/node22870/See the SCINet File Transfer guide for more information on moving large files: https://scinet.usda.gov/guides/data/datatransferGlobus users: The files can also be accessed through Globus by following this data link. The user will need to log in to Globus in order to retrieve this data. User accounts are free of charge with several options for signing on. Instructions for creating an account are on the login page.

Object Storage Market Outlook

The global object storage market size was valued at approximately USD 6.8 billion in 2023 and is expected to reach around USD 25 billion by 2032, growing at a compound annual growth rate (CAGR) of 15.7% during the forecast period. This remarkable growth can be attributed to the increasing demand for managing unstructured data, the rise in cloud adoption, and the growing use of big data analytics. As companies across various industries generate enormous amounts of data daily, the need for efficient, scalable, and cost-effective storage solutions becomes imperative, driving the object storage market's expansion.

One of the primary factors contributing to the growth of the object storage market is the exponential increase in unstructured data. Unstructured data, which includes emails, videos, social media posts, and IoT data, does not follow a predefined data model or structure. As organizations collect vast amounts of this data, traditional storage solutions like block and file storage become less effective due to scalability and cost constraints. Object storage provides a solution by allowing for limitless data scalability and cost efficiency, making it a preferred choice for managing unstructured data.

The rise in cloud adoption significantly fuels the object storage market's growth. Enterprises are increasingly shifting their data and applications to the cloud to leverage benefits such as reduced IT costs, enhanced scalability, and improved accessibility. Object storage is integral to cloud infrastructure, offering advantages like redundancy, high availability, and simplified management. Public, private, and hybrid cloud deployments extensively utilize object storage to store and manage data, enhancing the market's growth trajectory.

Furthermore, the growing use of big data analytics across various industries drives the demand for object storage solutions. Big data analytics involves processing and analyzing vast amounts of data to derive valuable insights, enhance decision-making, and drive business growth. Object storage systems are designed to handle large-scale data sets, making them ideal for big data applications. As more organizations invest in big data initiatives to gain a competitive edge, the need for robust and efficient storage solutions like object storage continues to rise, bolstering market growth.

In the realm of data management, the Object Storage Tool has emerged as a pivotal component for organizations seeking to harness the full potential of their unstructured data. This tool offers a robust framework for storing and retrieving data objects, which are essential for applications that require scalability and efficiency. By utilizing metadata and unique identifiers, the Object Storage Tool simplifies data retrieval processes, making it an indispensable asset for businesses that rely on large-scale data operations. Its integration with cloud environments further enhances its utility, providing seamless access and management capabilities that align with modern data strategies.

The regional outlook for the object storage market is also promising. North America is expected to dominate the market due to the presence of numerous key players, advanced IT infrastructure, and early adoption of emerging technologies. Europe and the Asia Pacific regions are also projected to experience significant growth, driven by the increasing digital transformation initiatives and the rising adoption of cloud services. Latin America and the Middle East & Africa are anticipated to witness moderate growth due to the gradual adoption of cloud technologies and growing investments in IT infrastructure.

Component Analysis

The object storage market by component is segmented into software, hardware, and services. The software segment is witnessing substantial growth due to its crucial role in enabling object storage systems to manage, store, and retrieve large volumes of unstructured data. Advanced software solutions support vital functions such as data deduplication, compression, and encryption, enhancing data management efficiency and security. As enterprises seek to optimize storage infrastructure and reduce costs, demand for robust object storage software solutions continues to rise.

The hardware segment is also e

Cloud Based Data Lake Market Outlook

In 2023, the global market size for Cloud Based Data Lakes is estimated to be valued at approximately USD 6.9 billion, with a projected market size of USD 19.8 billion by 2032, growing at a robust Compound Annual Growth Rate (CAGR) of 12.1% over the forecast period. The market is primarily driven by increasing data generation across various industries, the rising adoption of cloud services, and the growing need for advanced data analytics to gain business insights.

The rapid growth in data volume is one of the primary factors contributing to the expansion of the Cloud Based Data Lake market. Organizations across various sectors generate massive amounts of structured and unstructured data daily. Traditional data storage and management solutions struggle to handle such vast data efficiently, leading businesses to adopt cloud-based data lakes. These platforms provide scalable and flexible storage solutions, enabling organizations to store, process, and analyze large datasets efficiently. Moreover, the integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML) with cloud-based data lakes further enhances data analytics capabilities, driving market growth.

Another significant growth factor is the increasing adoption of cloud services across different industries. Cloud computing offers numerous benefits, including cost savings, scalability, and flexibility, which are particularly advantageous for data management. Companies can leverage cloud-based data lakes to reduce the infrastructure costs associated with on-premises data storage and management. Additionally, cloud service providers offer various tools and services that facilitate seamless data integration, processing, and analysis, simplifying the complexities associated with big data management. This increased adoption of cloud services is expected to fuel the growth of the Cloud Based Data Lake market in the coming years.

The growing emphasis on data-driven decision-making is also propelling the demand for cloud-based data lakes. Organizations are increasingly relying on data analytics to gain valuable insights, enhance operational efficiency, and drive strategic decision-making. Cloud-based data lakes enable businesses to ingest, store, and analyze large volumes of data from diverse sources in real-time, providing a comprehensive view of their operations. This ability to derive actionable insights from data helps organizations stay competitive in a rapidly evolving market landscape, thereby driving the adoption of cloud-based data lakes.

From a regional perspective, North America is expected to dominate the Cloud Based Data Lake market throughout the forecast period, owing to the presence of major technology players and early adoption of advanced data management solutions. Additionally, the Asia Pacific region is anticipated to witness significant growth, driven by the increasing adoption of cloud services and the growing focus on digital transformation initiatives in emerging economies. Europe is also projected to experience substantial growth, supported by the rising demand for data analytics solutions and the implementation of stringent data protection regulations.

Component Analysis

The Cloud Based Data Lake market can be broadly segmented into two major components: Solutions and Services. The Solutions segment includes various software tools and platforms that enable the creation, management, and analysis of data lakes. This segment is further divided into data ingestion, data storage, data processing, and analytics solutions. Data ingestion solutions facilitate the seamless integration of data from multiple sources into the data lake, ensuring that data is ingested in real-time and in the correct format. Data storage solutions provide scalable storage options that can accommodate the growing volumes of data generated by organizations. Data processing solutions enable the transformation and processing of raw data into meaningful insights, while analytics solutions offer advanced tools for data analysis and visualization.

Within the Services segment, the market is classified into professional services and managed services. Professional services include consulting, implementation, and support services provided by vendors to assist organizations in setting up and managing their cloud-based data lakes. These services are crucial for ensuring that the data lake is configured correctly and optimized for performance. Managed services, on the other hand, involve outsourcing the management and maintenance of the data lake to third-party ser

Big Data and Data Engineering Services Market Outlook

The global market size for Big Data and Data Engineering Services was valued at approximately USD 45.6 billion in 2023 and is expected to reach USD 136.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 13.2% during the forecast period. This robust growth is primarily driven by the increasing volume of data being generated across industries, advancements in data analytics technologies, and the rising importance of data-driven decision-making. Enterprises of all sizes are progressively leveraging big data solutions to gain strategic insights and maintain competitive advantage, thereby fueling market growth.

One of the pivotal growth factors for the Big Data and Data Engineering Services market is the exponential rise in data generation. With the advent of the Internet of Things (IoT), social media, and digital interactions, the volume of data generated daily is staggering. This data, if harnessed effectively, can provide invaluable insights into consumer behaviors, market trends, and operational efficiencies. Companies are increasingly investing in data engineering services to streamline and manage this data effectively. Additionally, the adoption of advanced analytics and machine learning techniques is enabling organizations to derive actionable insights, further driving the market's expansion.

Another significant growth driver is the technological advancements in data processing and analytics. The development of sophisticated data engineering tools and platforms has made it easier to collect, store, and analyze large datasets. Cloud computing has played a crucial role in this regard, offering scalable and cost-effective solutions for data management. The integration of artificial intelligence (AI) and machine learning (ML) in data analytics is enhancing the ability to predict trends and make informed decisions, thereby contributing to the market's growth. Furthermore, continuous innovations in data security and privacy measures are instilling confidence among businesses to invest in big data solutions.

The increasing emphasis on regulatory compliance and data governance is also propelling the market forward. Industries such as BFSI, healthcare, and government are subject to stringent regulatory requirements for data management and protection. Big Data and Data Engineering Services are essential in ensuring compliance with these regulations by maintaining data accuracy, integrity, and security. The implementation of data governance frameworks is becoming a top priority for organizations to mitigate risks associated with data breaches and ensure ethical data usage. This regulatory landscape is creating a conducive environment for the adoption of comprehensive data engineering services.

Regionally, North America dominates the Big Data and Data Engineering Services market, owing to the presence of major technology companies, high adoption of advanced analytics, and significant investments in R&D. However, the Asia Pacific region is expected to exhibit the highest growth rate due to rapid digital transformation, increasing internet penetration, and growing awareness about the benefits of data-driven decision-making among businesses. Europe also represents a significant market share, driven by the strong presence of industrial and technological sectors that rely heavily on data analytics.

Service Type Analysis

Data Integration is a critical component of Big Data and Data Engineering Services, encompassing the process of combining data from different sources to provide a unified view. This service type is instrumental for organizations aiming to harness data from various departments, applications, and geographies. The increasing complexity of data landscapes, characterized by disparate data sources and formats, necessitates efficient data integration solutions. Companies are investing heavily in data integration technologies to consolidate their data, improve accessibility, and enhance the quality of insights derived from analytical processes. This segment's growth is further fueled by advancements in integration tools that support real-time data processing and seamless connectivity.

Data Quality services ensure the accuracy, completeness, and reliability of data, which is essential for effective decision-making. Poor data quality can lead to misinformed decisions, operational inefficiencies, and regulatory non-compliance. As organizations increasingly recognize the criticality of data quality, there is a growing demand for robust data quality solutions. These services include da

A continuous dataset of Land Surface Temperature (LST) is vital for climatological and environmental studies. LST can be regarded as a combination of seasonal mean temperature (climatology) and daily anomaly, which is attributed mainly to the synoptic-scale atmospheric circulation (weather). To reproduce LST in cloudy pixels, time series (2002-2019) of cloud-free 1km MODIS Aqua LST images were generated and the pixel-based seasonality (climatology) was calculated using temporal Fourier analysis. To add the anomaly, we used the NCEP Climate Forecast System Version 2 (CFSv2) model, which provides air surface temperature under both cloudy and clear sky conditions. The combination of the two sources of data enables the estimation of LST in cloudy pixels.

Data structure

The dataset consists of geo-located continuous LST (Day, Night and Daily) which calculates LST values of cloudy pixels. The spatial domain of the data is the Eastern Mediterranean, at the resolution of the MYD11A1 product (~1 Km). Data are stored in GeoTIFF format as signed 16-bit integers using a scale factor of 0.02, with one file per day, each defined by 4 dimensions (Night LST Cont., Day LST Cont., Daily Average LST Cont., QA). The QA band stores information about the presence of cloud in the original pixel. If in both original files, Day LST and Night LST there was NoData due to clouds, then the QA value is 0. QA value of 1 indicates NoData at original Day LST, 2 indicates NoData at Night LST and 3 indicates valid data at both, day and night. File names follow this naming convention: LST_  .tif, where  represents the year, represents the month and represents the day. Files of each year (2002-2019) are compressed in a ZIP file. The same data is also provided in NetCDF format, each file represents a whole year and is consist of 4 bands (Night LST Cont., Day LST Cont., Daily Average LST Cont., QA) for each day.

The file LSTcont_validation.tif contains the validation dataset in which the MAE, RMSE, and Pearson (r) of the validation with true LST are provided. Data are stored in GeoTIFF format as signed 32-bit floats, with the same spatial extent and resolution as the LSTcont dataset. These data are stored with one file containing three bands (MAE, RMSE, and Perarson_r). The same data with the same structure is also provided in NetCDF format.

How to use

The data can be read in various of program languages such as Python, IDL, Matlab etc.and can be visualize in a GIS program such as ArcGis or Qgis. A short animation demonstrates how to visualize the data using the Qgis open source program is available in the project Github code reposetory.

Web application

The LSTcont web application (https://shilosh.users.earthengine.app/view/continuous-lst) is an Earth Engine app. The interface includes a map and a date picker. The user can select a date (July 2002 – present) and visualize LSTcont for that day anywhere on the globe. The web app calculate LSTcont on the fly based on ready-made global climatological files. The LSTcont can be downloaded as a GeoTiff with 5 bands in that order: Mean daily LSTcont, Night original LST, Night LSTcont, Day original LST, Day LSTcont.

Code availability

Datasets for other regions can be easily produced by the GEE platform with the code provided project Github code reposetory.

Data Protection Software Market Outlook

The global data protection software market size was valued at USD 8.8 billion in 2023 and is projected to reach USD 26.4 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 13% during the forecast period. This impressive growth can be attributed to the escalating concerns over data breaches and cyber-attacks, the increasing adoption of cloud-based services, and stringent regulatory requirements for data protection across various industries.

One of the primary growth factors for the data protection software market is the increasing frequency and sophistication of cyber-attacks. In today's digital age, organizations across all sectors are facing a myriad of cyber threats that can lead to significant financial and reputational damage. The need to safeguard sensitive information against such threats has intensified the demand for robust data protection solutions. Furthermore, the rising awareness about the importance of data security among enterprises is driving the adoption of advanced data protection software.

Another significant growth driver is the surge in data generated by enterprises. With the advent of big data, artificial intelligence, and the Internet of Things (IoT), the amount of data generated daily is growing exponentially. This data comprises valuable information that needs to be protected to ensure business continuity and compliance with regulations. As a result, organizations are investing heavily in data protection software solutions to manage and safeguard their data effectively.

The proliferation of cloud computing is also playing a crucial role in the expansion of the data protection software market. Organizations are increasingly migrating their data to the cloud to leverage benefits such as scalability, cost efficiency, and remote accessibility. However, this shift also introduces new security challenges, necessitating the deployment of advanced data protection solutions designed for cloud environments. The demand for cloud-based data protection software is expected to witness substantial growth as more businesses adopt cloud services.

In this context, Data-Centric Security Software is becoming increasingly vital for organizations aiming to protect their sensitive information. Unlike traditional security measures that focus on securing the perimeter, data-centric security emphasizes the protection of data itself, regardless of its location. This approach is particularly beneficial in today's digital landscape, where data is constantly moving across various platforms and environments. By implementing data-centric security solutions, organizations can ensure that their data remains secure, whether it is stored on-premises, in the cloud, or on mobile devices. This shift towards data-centric security is driven by the need for more granular control over data access and the ability to monitor data usage in real-time, providing a robust defense against unauthorized access and data breaches.

Regionally, North America holds the largest share in the data protection software market, driven by the presence of numerous key market players and the high adoption rate of advanced technologies. However, the Asia Pacific region is anticipated to exhibit the highest growth rate during the forecast period, owing to the rapid digitalization of businesses, increasing cyber threats, and growing awareness about data protection in emerging economies like India and China.

Component Analysis

When we analyze the data protection software market by component, it can be broadly divided into software and services. The software segment includes various data protection solutions such as encryption, data masking, and tokenization tools, while the services segment covers consulting, implementation, and managed services. The software segment is expected to dominate the market throughout the forecast period, driven by the continuous advancements in data protection technologies and the increasing need for comprehensive data security solutions.

Within the software segment, encryption tools hold a significant share as they provide robust security by converting data into a format that can only be read by authorized users. The growing concerns over data breaches and the necessity to comply with stringent data protection regulations are propelling the adoption of encryption solutions across industries. Additionally, data masking and tokenization too