A dataset containing the full genomic sequence of 1,700 individuals, freely available for research use. The 1000 Genomes Project is an international research effort coordinated by a consortium of 75 companies and organizations to establish the most detailed catalogue of human genetic variation. The project has grown to 200 terabytes of genomic data including DNA sequenced from more than 1,700 individuals that researchers can now access on AWS for use in disease research free of charge. The dataset containing the full genomic sequence of 1,700 individuals is now available to all via Amazon S3. The data can be found at: http://s3.amazonaws.com/1000genomes The 1000 Genomes Project aims to include the genomes of more than 2,662 individuals from 26 populations around the world, and the NIH will continue to add the remaining genome samples to the data collection this year. Public Data Sets on AWS provide a centralized repository of public data hosted on Amazon Simple Storage Service (Amazon S3). The data can be seamlessly accessed from AWS services such Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Elastic MapReduce (Amazon EMR), which provide organizations with the highly scalable compute resources needed to take advantage of these large data collections. AWS is storing the public data sets at no charge to the community. Researchers pay only for the additional AWS resources they need for further processing or analysis of the data. All 200 TB of the latest 1000 Genomes Project data is available in a publicly available Amazon S3 bucket. You can access the data via simple HTTP requests, or take advantage of the AWS SDKs in languages such as Ruby, Java, Python, .NET and PHP. Researchers can use the Amazon EC2 utility computing service to dive into this data without the usual capital investment required to work with data at this scale. AWS also provides a number of orchestration and automation services to help teams make their research available to others to remix and reuse. Making the data available via a bucket in Amazon S3 also means that customers can crunch the information using Hadoop via Amazon Elastic MapReduce, and take advantage of the growing collection of tools for running bioinformatics job flows, such as CloudBurst and Crossbow.

Non-Relational SQL Market Outlook

The Non-Relational SQL market size is projected to grow from USD 4.7 billion in 2023 to USD 15.8 billion by 2032, at a compound annual growth rate (CAGR) of 14.5% during the forecast period. This significant growth can be attributed to the rising demand for scalable and flexible database management solutions that efficiently handle large volumes of unstructured data.

One of the primary growth factors driving the Non-Relational SQL market is the exponential increase in data generation from various sources such as social media, IoT devices, and enterprise applications. As businesses seek to leverage this data for gaining insights and making informed decisions, the need for databases that can manage and process unstructured data efficiently has become paramount. Non-Relational SQL databases, such as document stores and graph databases, provide the required flexibility and scalability, making them an ideal choice for modern data-driven enterprises.

Another significant growth factor is the increasing adoption of cloud-based solutions. Cloud deployment offers numerous advantages, including reduced infrastructure costs, scalability, and easier management. These benefits have led to a surge in the adoption of Non-Relational SQL databases hosted on cloud platforms. Major cloud service providers like Amazon Web Services, Microsoft Azure, and Google Cloud offer robust Non-Relational SQL database services, further fueling market growth. Additionally, the integration of AI and machine learning with Non-Relational SQL databases is expected to enhance their capabilities, driving further adoption.

The rapid advancement in technology and the growing need for real-time data processing and analytics are also propelling the market's growth. Non-Relational SQL databases are designed to handle high-velocity data and provide quick query responses, making them suitable for real-time applications such as fraud detection, recommendation engines, and personalized marketing. As organizations increasingly rely on real-time data to enhance customer experiences and optimize operations, the demand for Non-Relational SQL databases is set to rise.

Regional outlook indicates that North America holds the largest share of the Non-Relational SQL market, driven by the presence of major technology companies and early adoption of advanced database technologies. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by the rapid digital transformation initiatives and increasing investments in cloud infrastructure. Europe and Latin America also present significant growth opportunities due to the rising adoption of big data and analytics solutions.

Database Type Analysis

When analyzing the Non-Relational SQL market by database type, we observe that document stores hold a significant share of the market. Document stores, such as MongoDB and Couchbase, are particularly favored for their ability to store, retrieve, and manage document-oriented information. These databases are highly flexible, allowing for the storage of complex data structures and providing an intuitive query language. The increasing adoption of document stores can be ascribed to their ease of use and adaptability to various application requirements, making them a popular choice among developers and businesses.

Key-Value stores represent another crucial segment of the Non-Relational SQL market. These databases are known for their simplicity and high performance, making them ideal for caching, session management, and real-time data processing applications. Redis and Amazon DynamoDB are prominent examples of key-value stores that have gained widespread acceptance. The growing need for low-latency data access and the ability to handle massive volumes of data efficiently are key drivers for the adoption of key-value stores in various industries.

The market for column stores is also expanding as businesses require databases that can handle large-scale analytical queries efficiently. Columnar storage formats, such as Apache Cassandra and HBase, optimize read and write performance for analytical processing, making them suitable for big data analytics and business intelligence applications. The ability to perform complex queries on large datasets quickly is a significant advantage of column stores, driving their adoption in industries that rely heavily on data analytics.

Graph databases, such as Neo4j and Amazon Neptune, are gaining traction due to their ability to model

S-111 is a data and metadata encoding specification that is part of the S-100 Universal Hydrographic Data Model, an international standard for hydrographic data. This collection of data contains surface water currents forecast guidance from NOAA/NOS Operational Forecast Systems, a set of operational hydrodynamic nowcast and forecast modeling systems, for various U.S. coastal waters and the great lakes. The collection also contains surface current forecast guidance output from the NCEP Global Real-Time Ocean Forecast System (GRTOFS) for some offshore areas. These datasets are encoded as HDF-5 files conforming to the S-111 specification, and are geospatially subset into individual tiles conforming to the NOAA/OCS Nautical Product Tiling Scheme, with filenames indicating the corresponding NOAA Electronic Navigational Chart (ENC) Cell Identifier. A full set of S-111 tiles is created for each new model run cycle, which occurs four times per day for all models except for RTOFS, which updates only once per day. Files are organized using a path naming convention that includes the OFS identifier (e.g. 'cbofs' corresponding with output from the Chesapeake Bay Operational Forecast System) as well as the year, month, day, and hour corresponding with each model run initialization time. Each individual S-111 (HDF-5) file contains all forecast projections from a single model run for that geographic area. In other words, a single S-111 file will contain multiple gridded arrays each containing a forecast valid at a distinct time in the future, out to the forecast horizon of the underlying modeling system. All surface currents forecasts in this collection are computed at a depth of 4.5 meters below water surface, or half the water column depth, whichever is shallower.

The global Big Data in E-commerce market is experiencing robust growth, driven by the increasing volume of e-commerce transactions and the need for businesses to leverage data for improved decision-making, personalized marketing, and enhanced customer experiences. Let's assume, for illustrative purposes, a 2025 market size of $50 billion and a Compound Annual Growth Rate (CAGR) of 15% for the forecast period 2025-2033. This implies significant expansion, reaching an estimated market value of approximately $150 billion by 2033. Key drivers include the proliferation of mobile commerce, the rise of omnichannel strategies, and the increasing adoption of advanced analytics technologies like AI and machine learning to extract actionable insights from vast datasets. Furthermore, the growing demand for real-time data processing and predictive analytics for inventory management, fraud detection, and personalized recommendations fuels this expansion. While data security concerns and the complexity of implementing Big Data solutions present challenges, the overall market trajectory indicates a promising future for Big Data applications in the e-commerce sector. The competitive landscape comprises established technology giants like Amazon Web Services, Microsoft, and IBM, alongside specialized Big Data analytics providers, creating a dynamic market with opportunities for innovation and consolidation. The segment analysis (specific segments not provided) is crucial for identifying high-growth areas within this market. For example, segments focused on real-time analytics for customer experience or AI-powered predictive modeling for marketing campaigns are likely to witness particularly strong growth. Regional variations in e-commerce adoption and technological infrastructure also influence market dynamics. North America and Europe currently hold substantial market share but regions like Asia-Pacific are showing rapid growth potential, due to the expanding e-commerce ecosystem and increasing digital literacy. The continued development and refinement of Big Data technologies, coupled with the growing sophistication of e-commerce businesses in utilizing data-driven strategies, will ensure a sustained expansion of this market in the coming years.

This dataset includes hourly sea surface temperature and current data collected by satellite-tracked surface drifting buoys ("drifters") of the NOAA Global Drifter Program. The Drifter Data Assembly Center (DAC) at NOAA’s Atlantic Oceanographic and Meteorological Laboratory (AOML) has applied quality control procedures and processing to edit these observational data and obtain estimates at regular hourly intervals. The data include positions (latitude and longitude), sea surface temperatures (total, diurnal, and non-diurnal components) and velocities (eastward, northward) with accompanying uncertainty estimates. Metadata include identification numbers, experiment number, start location and time, end location and time, drogue loss date, death code, manufacturer, and drifter type.

Please note that data from the Global Drifter Program are also available at 6-hourly intervals but derived via alternative methods. The 6-hourly dataset goes back further in time (1979) and may be more appropriate for studies of long-term, low frequency patterns of the oceanic circulation. Yet, the 6-hourly dataset does not resolve fully high-frequency processes such as tides and inertial oscillations as well as sea surface temperature diurnal variability.

[CITING NOAA - hourly position, current, and sea surface temperature from drifters data. Citation for this dataset should include the following information below.]
Elipot, Shane; Sykulski, Adam; Lumpkin, Rick; Centurioni, Luca; Pazos, Mayra (2022). Hourly location, current velocity, and temperature collected from Global Drifter Program drifters world-wide. [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/x46c-3620.

NEW GOES-19 Data!! On April 4, 2025 at 1500 UTC, the GOES-19 satellite will be declared the Operational GOES-East satellite. All products and services, including NODD, for GOES-East will transition to GOES-19 data at that time. GOES-19 will operate out of the GOES-East location of 75.2°W starting on April 1, 2025 and through the operational transition. Until the transition time and during the final stretch of Post Launch Product Testing (PLPT), GOES-19 products are considered non-operational regardless of their validation maturity level. Shortly following the transition of GOES-19 to GOES-East, all data distribution from GOES-16 will be turned off. GOES-16 will drift to the storage location at 104.7°W. GOES-19 data should begin flowing again on April 4th once this maneuver is complete.

NEW GOES 16 Reprocess Data!! The reprocessed GOES-16 ABI L1b data mitigates systematic data issues (including data gaps and image artifacts) seen in the Operational products, and improves the stability of both the radiometric and geometric calibration over the course of the entire mission life. These data were produced by recomputing the L1b radiance products from input raw L0 data using improved calibration algorithms and look-up tables, derived from data analysis of the NIST-traceable, on-board sources. In addition, the reprocessed data products contain enhancements to the L1b file format, including limb pixels and pixel timestamps, while maintaining compatibility with the operational products. The datasets currently available span the operational life of GOES-16 ABI, from early 2018 through the end of 2024. The Reprocessed L1b dataset shows improvement over the Operational L1b products but may still contain data gaps or discrepancies. Please provide feedback to Dan Lindsey (dan.lindsey@noaa.gov) and Gary Lin (guoqing.lin-1@nasa.gov). More information can be found in the GOES-R ABI Reprocess User Guide.


NOTICE: As of January 10th 2023, GOES-18 assumed the GOES-West position and all data files are deemed both operational and provisional, so no ‘preliminary, non-operational’ caveat is needed. GOES-17 is now offline, shifted approximately 105 degree West, where it will be in on-orbit storage. GOES-17 data will no longer flow into the GOES-17 bucket. Operational GOES-West products can be found in the GOES-18 bucket.

GOES satellites (GOES-16, GOES-17, GOES-18 & GOES-19) provide continuous weather imagery and monitoring of meteorological and space environment data across North America. GOES satellites provide the kind of continuous monitoring necessary for intensive data analysis. They hover continuously over one position on the surface. The satellites orbit high enough to allow for a full-disc view of the Earth. Because they stay above a fixed spot on the surface, they provide a constant vigil for the atmospheric "triggers" for severe weather conditions such as tornadoes, flash floods, hailstorms, and hurricanes. When these conditions develop, the GOES satellites are able to monitor storm development and track their movements. SUVI products available in both NetCDF and FITS.

The Deep Learning Systems market is experiencing explosive growth, projected to reach $24.73 billion in 2025 and exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 41.10%. This expansion is fueled by several key factors. Firstly, the increasing availability and affordability of high-performance computing resources, including GPUs and specialized hardware accelerators, are significantly lowering the barrier to entry for both developers and businesses. Secondly, the proliferation of big data and the advancements in algorithms are enabling the development of increasingly sophisticated and accurate deep learning models across a wide array of applications. This includes image and signal recognition, data processing, and more. The BFSI, retail, manufacturing, healthcare, automotive, and telecom sectors are leading adopters, leveraging deep learning for tasks ranging from fraud detection and personalized recommendations to predictive maintenance and advanced driver-assistance systems. While data privacy concerns and the need for skilled professionals represent challenges, the overall market trajectory remains strongly positive, driven by continuous innovation and expanding application areas. Looking ahead to 2033, the market's robust growth is expected to continue, though the CAGR might naturally moderate slightly as the market matures. However, the consistent advancements in deep learning methodologies, combined with the expanding adoption across new industries and emerging applications (such as the Internet of Things and edge computing), will sustain significant market expansion. The competitive landscape, characterized by technology giants like Google, Amazon, and Microsoft, alongside specialized players like NVIDIA and AMD, indicates a dynamic market with ongoing innovation and competition. Regional growth will likely see continued strong performance in North America and Asia Pacific, fueled by high technological adoption and substantial investment in research and development. Europe will also contribute significantly, driven by governmental initiatives and a focus on data-driven innovation. Recent developments include: September 2023: Amazon and Anthropic announced a strategic partnership that would bring together their respective technology and expertise in safer generative artificial intelligence (AI) to accelerate the development of Anthropic’s future foundation models and make them widely accessible to AWS consumers., May 2022: Intel launched its second-generation Habana AI deep learning processors in order to deliver high efficiency and high performance. The launch of Habana's new deep learning processors is a key example of Intel executing on its AI strategy to give customers a wide array of solution choices from cloud to the edge, addressing the growing number and complex nature of AI workloads., August 2022: Amazon launched a new Machine Learning (ML) software through which medical records of patients can be analyzed for better treatment of patients and reduce overall expenses.. Key drivers for this market are: Increasing Computing Power, coupled with the Presence of Large Unstructured Data, Ongoing Efforts toward the Integration of DL in Consumer-based Solutions; Growing Use of Deep Learning in Retail Sector is Driving the Market. Potential restraints include: Increasing Computing Power, coupled with the Presence of Large Unstructured Data, Ongoing Efforts toward the Integration of DL in Consumer-based Solutions; Growing Use of Deep Learning in Retail Sector is Driving the Market. Notable trends are: Growing Use of Deep Learning in Retail Sector is Driving the Market.

As of March 2021, 212 companies had joined GAIA-X, of which 40 percent are cloud and technical vendor companies. 36 percent are user companies associations, academics, or NPO, while 25 percent are start-ups. 92 percent of these member companies are European companies, and the other companies come from Asia and North America. What is GAIA-X? GAIA-X is a project initiated by France and Germany for the whole of Europe to advocate for and develop a common European data infrastructure. First presented to the public at the Digital Summit in 2019, its aims are the development of a competitive, yet efficient and secure federation of service providers for Europe. Specifically, the project seeks to build a digital ecosystem so that companies can share data and potentially build new services as use cases arise. In addition, this ecosystem will also serve as a repository for companies to find domestic solutions rather than relying on tech giants from abroad. Why does Europe want to encourage the use of domestic providers and services? In general, the GAIA-X Association strives towards more cloud independence and reduces reliance on big cloud companies such as Amazon Web Services and Microsoft Azure. This approach concerns the fact that data is subjected to the laws of the countries in which the companies that host and process the data are located. For example, data by a German company that stores it on an AWS server in the United States is subjected to the U.S. CLOUD Act. In other words, companies from abroad do not operate under the same data policies as Europe. In encouraging companies to use domestic services, GAIA-X seeks to regain data sovereignty by ensuring that data that is generated in Europe remains under European data laws.

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 16.0px 'Andale Mono'; color: #29f914; background-color: #000000} span.s1 {font-variant-ligatures: no-common-ligatures} These files are intended for use with the Data Carpentry Genomics curriculum (https://datacarpentry.org/genomics-workshop/). Files will be useful for instructors teaching this curriculum in a workshop setting, as well as individuals working through these materials on their own.

This curriculum is normally taught using Amazon Web Services (AWS). Data Carpentry maintains an AWS image that includes all of the data files needed to use these lesson materials. For information on how to set up an AWS instance from that image, see https://datacarpentry.org/genomics-workshop/setup.html. Learners and instructors who would prefer to teach on a different remote computing system can access all required files from this FigShare dataset.

This curriculum uses data from a long term evolution experiment published in 2016: Tempo and mode of genome evolution in a 50,000-generation experiment (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4988878/) by Tenaillon O, Barrick JE, Ribeck N, Deatherage DE, Blanchard JL, Dasgupta A, Wu GC, Wielgoss S, Cruveiller S, Médigue C, Schneider D, and Lenski RE. (doi: 10.1038/nature18959). All sequencing data sets are available in the NCBI BioProject database under accession number PRJNA294072 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA294072).

backup.tar.gz: contains original fastq files, reference genome, and subsampled fastq files. Directions for obtaining these files from public databases are given during the lesson https://datacarpentry.org/wrangling-genomics/02-quality-control/index.html). On the AWS image, these files are stored in ~/.backup directory. 1.3Gb in size.

Ecoli_metadata.xlsx: an example Excel file to be loaded during the R lesson.

shell_data.tar.gz: contains the files used as input to the Introduction to the Command Line for Genomics lesson (https://datacarpentry.org/shell-genomics/).

sub.tar.gz: contains subsampled fastq files that are used as input to the Data Wrangling and Processing for Genomics lesson (https://datacarpentry.org/wrangling-genomics/). 109Mb in size.

solutions: contains the output files of the Shell Genomics and Wrangling Genomics lessons, including fastqc output, sam, bam, bcf, and vcf files.

vcf_clean_script.R: converts vcf output in .solutions/wrangling_solutions/variant_calling_auto to single tidy data frame.

combined_tidy_vcf.csv: output of vcf_clean_script.R

Cloud Infrastructure Service Market Outlook

As of 2023, the global cloud infrastructure service market size stands at approximately USD 150 billion, with a projected growth to over USD 550 billion by 2032, reflecting a robust CAGR of 15.5%. Key growth factors driving this market include the increasing adoption of cloud services across various industries, the need for scalable and efficient IT infrastructure, and advancements in cloud technologies.

One of the primary growth factors for the cloud infrastructure service market is the rising demand for digital transformation across industries. Organizations are increasingly leveraging cloud services to enhance their operational efficiency, improve customer experiences, and stay competitive. The flexibility and scalability offered by cloud infrastructure allow businesses to quickly adapt to changing market conditions and customer needs. Furthermore, the COVID-19 pandemic has accelerated the adoption of cloud solutions as companies sought to support remote work and ensure business continuity.

Another significant driver is the cost-efficiency associated with cloud infrastructure services. By migrating to the cloud, organizations can reduce their capital expenditure on IT infrastructure and only pay for the services they use. This shift from a CapEx to an OpEx model provides businesses with greater financial flexibility and the ability to allocate resources more effectively. Additionally, the economies of scale achieved by cloud service providers result in lower costs for end-users, further boosting the adoption of cloud infrastructure services.

The rapid advancements in cloud technologies, such as edge computing, artificial intelligence, and machine learning, are also contributing to the market's growth. These technologies enable organizations to process and analyze data more efficiently, leading to better decision-making and innovation. For example, AI-powered cloud services can help businesses automate routine tasks, enhance cybersecurity, and gain insights from large datasets. The integration of these advanced technologies into cloud infrastructure is expected to drive further market growth in the coming years.

Geographically, the North American region is expected to dominate the cloud infrastructure service market, with significant contributions from the United States and Canada. The presence of major cloud service providers, such as Amazon Web Services, Microsoft Azure, and Google Cloud, along with the region's strong focus on technological innovation, is driving market growth. Additionally, the Asia Pacific region is anticipated to witness the highest growth rate due to the increasing adoption of cloud services in countries like China, India, and Japan. The growing number of SMEs and the expansion of the IT sector in this region are key factors contributing to this growth.

The Middle East and Africa (MEA) region is emerging as a significant player in the cloud infrastructure services market. With the increasing demand for digital transformation and the adoption of cloud technologies, MEA Cloud Infrastructure Services are gaining traction. The region's unique challenges, such as diverse economic conditions and varying levels of technological advancement, are being addressed by tailored cloud solutions that cater to local needs. Companies in the MEA region are leveraging cloud infrastructure to enhance operational efficiency, improve service delivery, and foster innovation. The growing investments in data centers and cloud infrastructure by global and regional players are further propelling the market growth. As businesses in the MEA region continue to embrace digitalization, the demand for scalable and efficient cloud services is expected to rise, creating new opportunities for cloud service providers.

Service Type Analysis

Within the cloud infrastructure service market, service type is a critical segment, encompassing Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Each of these services plays a unique role in the cloud ecosystem, offering varying levels of control, flexibility, and management to organizations.

Infrastructure as a Service (IaaS) is one of the foundational components of cloud infrastructure services. It provides virtualized computing resources over the internet, allowing organizations to rent virtual servers, storage, and networking capabilities. IaaS is highly

NASA's goal in Earth science is to observe, understand, and model the Earth system to discover how it is changing, to better predict change, and to understand the consequences for life on Earth. The Applied Sciences Program, within the Earth Science Division of the NASA Science Mission Directorate, serves individuals and organizations around the globe by expanding and accelerating societal and economic benefits derived from Earth science, information, and technology research and development.

The Prediction Of Worldwide Energy Resources (POWER) Project, funded through the Applied Sciences Program at NASA Langley Research Center, gathers NASA Earth observation data and parameters related to the fields of surface solar irradiance and meteorology to serve the public in several free, easy-to-access and easy-to-use methods. POWER helps communities become resilient amid observed climate variability by improving data accessibility, aiding research in energy development, building energy efficiency, and supporting agriculture projects.

The POWER project contains over 380 satellite-derived meteorology and solar energy Analysis Ready Data (ARD) at four temporal levels: hourly, daily, monthly, and climatology. The POWER data archive provides data at the native resolution of the source products. The data is updated nightly to maintain near real time availability (2-3 days for meteorological parameters and 5-7 days for solar). The POWER services catalog consists of a series of RESTful Application Programming Interfaces, geospatial enabled image services, and web mapping Data Access Viewer. These three service offerings support data discovery, access, and distribution to the project’s user base as ARD and as direct application inputs to decision support tools.

The latest data version update includes hourly-based source ARD, in addition to enhanced daily, monthly, annual, and climatology data. The daily time series for meteorology is available from 1981, while solar-based parameters start in 1984. The hourly source data are from Clouds and the Earth's Radiant Energy System (CERES) and Global Modeling and Assimilation Office (GMAO), spanning from 1984 for meteorology and from 2001 for solar-based parameters. The hourly data equips users with the ARD needed to model building system energy performance, providing information directly amenable to decision support tools introducing the industry standard EnergyPlus Weather file format.

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Server Operating System Market Size 2024-2028

The server operating system market size is estimated to increase by USD 12.19 billion and grow at a CAGR of 10.87% between 2023 and 2028. The market is experiencing significant growth, driven by several key factors. Firstly, the increasing investments in the construction of hyper-scale data centers are fueling the demand for advanced server operating systems that can efficiently manage large-scale infrastructure. Secondly, technological advancements in server operating systems, such as containerization and virtualization, are enabling organizations to optimize their IT resources, data center, and improve application performance. However, the market is also facing challenges, including the rising number of security issues, which require server operating systems to provide robust security features to protect against cyber threats. Additionally, the growing complexity of IT environments is necessitating the need for server operating systems that can seamlessly integrate with various applications and tools. Overall, the server operating system market is expected to continue its growth trajectory, driven by these market trends and challenges.

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

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Market Segmentation

By Deployment

The market share growth by the on-premises segment will be significant during the forecast period. On-premises solutions in the global market are popular among companies looking to manage their IT infrastructure. On-premises solutions give companies complete control over the hardware and software, allowing them to adapt the system to their individual needs. One of the main benefits of on-premises solutions is increased security and privacy. Businesses can keep data and applications behind firewalls and other security measures, reducing the risk of cyberattacks and data breaches. This is especially important for software companies that handle sensitive data such as financial or medical information.

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The on-premises segment was valued at USD 7.57 billion in 2018. On-premises solutions allow companies to choose their hardware and software companies, design their systems to meet their specific needs and make changes and upgrades as needed. This gives software companies more control over their IT infrastructure, helping them achieve the best possible performance. On-premises solutions also offer improved performance and reliability compared to cloud-based solutions. However, on-premises solutions can be expensive up-front, as software companies must invest in the hardware, software, and staff required to maintain and manage the operating system.

By Region

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North America is estimated to contribute 40% to the growth of the global market during the forecast period. Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period. One of the reasons why North America has a strong position in the market is the high acceptance of cloud-based services by enterprises. These cloud-based services require advanced systems for optimal performance and security. North America is home to major cloud service providers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud. Another factor contributing to the growth of the North American market is the increasing demand for data centers.

The North American region is home to some of the world's largest data centers. Companies such as Microsoft Corp (Microsoft), IBM Corp (IBM), and Oracle Corp (Oracle) have a strong presence in North America and invest heavily in research and development (R&D) to innovate and gain a competitive advantage over other companies. For example, Google LLC (Google) announced its USD 750 million new data center in Nebraska to meet its goal of spending USD 9.5 billion on new Google data centers and offices in 2022. Such expansion plans drive the growth of the market in North America during the forecast period.

Market Dynamics and Customer Landscape

The market is a significant segment of the IT industry, focusing on software that manages and operates servers in data centers and cloud platforms. Server OS includes various types such as Application Servers, File Servers, Database Servers, Mail Servers, Web Servers, and others. These operating systems are essential for Client Server Infrastructure and Client Machinery to function effectively in Network environments. Cloud computing has been a major driver in the growth of the Server OS market, with hybrid cloud environments and 5G networking technologies playing a pivotal role. Server OS software is integral to enterprise migration and digital transformation, as businesses

The Time Series Intelligence Software market is projected to grow from USD XXX million in 2023 to USD XXX million by 2033, at a CAGR of XX% during the forecast period. Factors driving the growth of this market include the increasing adoption of IoT devices, the growing need for real-time data analysis, and the need for improved forecasting and predictive capabilities. Furthermore, the rising trend of digital transformation and the increasing need to gain insights from large datasets are contributing to the growth of this market. The market for Time Series Intelligence Software is fragmented, with a number of major players. The key players in this market include Google, SAP, Microsoft Azure, Trendalyze, Anodot, Seeq, SensorMesh, Warp 10, AxiBase, Shapelets, TrendMiner, and Datapred. These players offer a variety of solutions to meet the needs of different industries. For example, Google Cloud's Vertex AI Time Series Insight tool provides end-to-end capabilities for understanding and predicting time series data. Amazon Web Services (AWS) offers Amazon Forecast, which provides time series forecasting capabilities. These solutions are being increasingly adopted by businesses to gain insights from their data and make better decisions.

The objective of CAMREX (Carbon in the Amazon River Experiment) project which was conducted from 1982 through 1991, was been to define by mass balances and direct measurements those processes which control the distribution of bioactive elements (C, N, P and O) in the mainstem of the Amazon River in Brazil. The CAMREX dataset represents a time series unique in its length and detail for very large river systems. The central sampling strategy has been to obtain representative flux-weighted water samples for comprehensive chemical analysis and to make rate measurements over 18 different sites within a 2000 km reach of the Brazilian Amazon mainstem, including major intervening tributaries. Samples have now been collected on 13 different cruises (1982-1991) during contrasting hydrographic stages.

Data or images are provided for (1) water chemistry, (2) daily river discharge, (3) monthly estimates for 1989 of some model drivers and structure including NPP, Evapotranspiration, Precipitation, Temperature, and AVHRR data, (4) daily precipitation, and (5) air temperature anomalies.

The processed, quality controlled and integrated data in the documented Pre-LBA Data sets were originally published as a set of three CD_ROMs (Marengo and Victoria, 1998) but are now archived individually.

According to Cognitive Market Research, the global Multichannel Analytics market size is USD 22.5 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 18.3% from 2024 to 2031. Market Dynamics of Multichannel Analytics Market

Key Drivers for Multichannel Analytics Market

Growing social consumer - The increasing need for data-driven decision making in many different companies is the main factor propelling the global market. This is explained by the increasing demand for understanding consumer behavior data in order to make data-driven decisions in real time. Accordingly, the market is being stimulated by the growing use of artificial intelligence (AI)-based analytics solutions to automate routine processes like reporting and analysis. Furthermore, the industry is seeing profitable growth prospects as a result of the paradigm shift toward cloud-based company operations.
Marketing requires predictive analytics is anticipated to drive the Multichannel Analytics market's expansion in the years ahead.

Key Restraints for Multichannel Analytics Market

Insufficient experience poses a serious threat to the Multichannel Analytics industry.
The market also faces significant difficulties related to quickly advancing technologies.

Trends for Multichannel Analytics Market

Technological Trends and Advancements in the Multichannel Analytics Market

The multichannel analytics market is undergoing a transformative phase driven by the integration of advanced technologies, as organizations increasingly prioritize data-driven strategies to enhance customer experiences and optimize business outcomes. One of the most significant advancements in this domain is the incorporation of artificial intelligence (AI) and machine learning (ML). These technologies enable predictive analytics, automate decision-making processes, and provide real-time insights into customer behavior. For example, Google Analytics 4 utilizes AI-powered tools to map customer journeys across multiple platforms, helping businesses predict future trends and tailor their marketing strategies accordingly. This has become particularly critical in the era of personalized marketing, where consumers expect brands to deliver customized experiences. Additionally, AI-driven sentiment analysis tools are being employed to gauge customer feedback from unstructured data sources such as social media, online reviews, and email communications. Another major trend shaping the market is the widespread adoption of cloud-based analytics platforms, which provide the scalability and flexibility required to handle vast amounts of data from diverse sources. Cloud platforms, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud, have emerged as key enablers, facilitating seamless integration of data from social media platforms, mobile apps, e-commerce websites, and traditional marketing channels. These platforms offer organizations cost-effective and agile solutions, allowing them to focus on extracting actionable insights rather than managing complex IT infrastructures. By the end of 2023, cloud-based analytics solutions were estimated to account for nearly 60% of the global market, reflecting a paradigm shift toward more dynamic and accessible data analytics frameworks. This trend is expected to accelerate further as businesses increasingly recognize the value of real-time data processing for staying competitive in a rapidly evolving digital landscape.

Emerging Opportunities in the Multichannel Analytics Market

The unprecedented growth of e-commerce and digital marketing has unlocked substantial opportunities for the multichannel analytics market, as businesses strive to deliver seamless and engaging experiences across multiple touchpoints. The global e-commerce sector, projected to surpass $6 trillion in revenue by 2024, underscores the critical need for robust analytics tools capable of unifying customer data across online and offline platforms. Retailers, for instance, are leveraging multichannel analytics to identify purchase patterns, optimize inventory management, and implement dynamic pricing strategies tailored to consumer preferences. A case in point is Walmart, which uses advanced analytics to streamline its supply chain operations and enhance in-store as well as online shopping experiences. Similarly, digital marketing agencies are harnessing these tools to measure campaign performance, refine targeting...

The Contact Center Software Market size was valued at USD 33.38 billion in 2023 and is projected to reach USD 149.62 billion by 2032, exhibiting a CAGR of 23.9 % during the forecasts period. The Contact Center Software Market includes a various solutions used for the purpose of effectively handling the customer’s communication both through the phone, e-mail, instant messaging services or social networks. The said software systems are very useful in improving customer contacts, efficiency of the center, and the means of doing business of a contact center. Examples of uses are call center, tele-selling, and sales operations. Market trends that can be identified include adoption of artificial intelligence (AI) to enhance automation, cloud solutions in the market for the reasons of flexibility, and omnichannel capabilities, which became significant priorities. The market of contact center software is growing as companies make the focus on interaction with the clients and the improvement of services. Recent developments include: In May 2023, BT Group plc and Five9, Inc. announced the expansion of partnership to provide cloud-based contact centers across the globe. Through the partnership end use companies can access Five9 Intelligent CX Platform which is embedded with data and voice services of BT Group plc. , In March 2023, Five9, Inc. introduced an Agent Assist 2.0 solution. It is integrated with AI summary and powered by OpenAI. The solution help end use companies to improve agent performance, processes, and customer experience. , In January 2023, NICE, a New York-based contact center software provider, unveiled a global strategic partnership with Cognizant to expedite customer experience (CX) transformation. This collaboration brings together the extensive consulting and business transformation expertise of Cognizant and the powerful, all-encompassing cloud platform of NICE CXone. By leveraging advanced CX solutions such as digital, analytics, and conversational AI, this partnership aims to drive the widespread adoption of innovative CX offerings. Both NICE and Cognizant anticipate significant growth opportunities within their respective customer bases through this strategic alliance. , In May 2022, Amazon Web Services and IBM signed a strategic collaboration agreement. This agreement enabled IBM to provide a wide range of its software portfolio as Software-as-a-Service (SaaS) on Amazon Web Services. .

The global Managed Services Market, valued at $276.64 billion in 2025, is projected to experience robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 10.75% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing adoption of cloud computing necessitates robust managed services for security, maintenance, and optimization. Businesses are increasingly outsourcing IT operations to focus on core competencies, leading to higher demand for managed services providers (MSPs). Furthermore, the growing complexity of IT infrastructure and the need for round-the-clock support are pushing organizations towards managed service solutions. The market's segmentation across deployment (cloud, on-premises) and service types (MDS, MNS, MSS, MMS, Others) reflects the diverse needs of various industries. While on-premise solutions still hold a significant market share, the rapid shift towards cloud-based managed services is driving substantial growth in this segment. Different service types cater to specialized needs; for example, Managed Database Services (MDS) are gaining traction due to the increasing reliance on data-driven decision-making. The competitive landscape is highly fragmented, with numerous global players such as Accenture, IBM, and Amazon competing fiercely, each leveraging its unique strengths and strategies. While strong competition benefits consumers with diverse choices and cost-effectiveness, managing security risks and ensuring service quality remain key challenges. Geographical distribution reveals a strong presence in North America and Europe, but Asia-Pacific and other regions are showing significant growth potential. The forecast period (2025-2033) anticipates a surge in demand, driven by factors such as digital transformation initiatives, the Internet of Things (IoT) expansion, and the increasing adoption of artificial intelligence (AI) and machine learning (ML) in various business operations. The growth will not be uniform across all segments, with cloud-based managed services and specialized service offerings such as security and compliance services likely to witness the most significant acceleration. Industry players are actively investing in research and development to enhance their service offerings, integrate cutting-edge technologies, and meet the evolving needs of their clientele. This proactive approach, coupled with strategic partnerships and acquisitions, will shape the competitive dynamics and further fuel market expansion throughout the forecast period. However, challenges such as cybersecurity threats and the need for skilled professionals will require careful navigation.

The market for Iaas In Chemical Industry is anticipated to increase from USD 3.81 Billion in 2025 to USD 6.79 Billion by 2033, expanding at a CAGR of 6.13%. The increasing requirement for strong and dependable IT infrastructure to support the chemical industry's data-intensive activities is a major driver of market expansion. The shift toward cloud-based services as a result of improved connectivity and advancements in data security measures is another factor driving market expansion. Leading businesses in the Iaas In Chemical Industry market are concentrating on product innovation and strategic collaborations to strengthen their market position. To increase their market reach and share, vendors are also concentrating on regional growth. The market research includes an analysis of the strategies used by the top companies, including Infinera, Cisco, DigitalOcean, Amazon Web Services, IBM, and Alibaba Cloud. The study offers a thorough insight into the competitive environment, giving stakeholders important knowledge for making wise decisions and capitalizing on market opportunities. Recent developments include: The IaaS in Chemical Industry Market has experienced significant developments recently, particularly with key players such as Amazon Web Services, Microsoft Azure, and IBM expanding their cloud capabilities tailored for chemical sector applications., Reports indicate that various companies are investing heavily in cloud infrastructure to enhance operational efficiency, reduce costs, and improve supply chain management. Cisco and VMware have also been active, focusing on strategic collaborations to enhance cloud-based solutions for chemical production processes., Moreover, mergers and acquisitions are shaping the landscape; for example, Infinera has pursued partnerships to integrate its optical networking solutions into existing cloud infrastructures, while Alibaba Cloud has reported enhancements in its service portfolio aimed at chemical manufacturers., The growth of these companies in market valuation has led to a competitive environment, spurring further innovation and attracting investments in IaaS services. DigitalOcean and Oracle are also seeing increased demand for their services, emphasizing the sector’s shift toward more scalable and reliable cloud solutions., This momentum is fostering a collaborative ecosystem among established players, driving advancements in cloud technology tailored specifically for the chemical industry.. Key drivers for this market are: Enhanced scalability for production, Cost reduction through shared resources; Improved data analytics capabilities; Integration with AI and IoT; Increased sustainability and compliance solutions. Potential restraints include: Cloud adoption in chemical sector, Cost efficiency and scalability; Data security and compliance; Demand for digital transformation; Environmental sustainability initiatives.

Qualitative content analysis was conducted through three data types: annual reports, earnings calls and shareholder meetings, and terms of service agreements or “user contracts.” In addition to these three categories of data, summary statistics of revenues and assets were derived from financial data and leaked internal and court documents were examined. Data were obtained through the firms, from the Security and Exchange Commission’s (SEC) EDGAR system, Wharton WRDS, ToS agreements from firms and through the Internet Archive “Wayback Machine,” and leaked documents from secondary grey literature and Harvard’s “fbarchive.” I apply qualitative content analysis (QCA) as my method of inquiry using Atlas.ti qualitative data analysis software to help code and analyze study data.The categories of textual data are divided into user-targeted ToS agreements and investor-targeted reports. The latter category is divided into annual reports: 10-Ks, annual meetings termed “annual reports,” and earnings calls: follow up calls, 10-Qs, and other quarterly earnings presentations and reports, termed “earnings reports.” I began with conceptual analysis before moving to a relational analysis. I use a hybrid iterative deductive and inductive method of inquiry. I used an open, adaptive, coding process to inductively investigate the data and build a coding system. Financial data were used to summarize asset holdings, market capitalization, and revenue and earnings before interest, taxes, depreciation, and amortization (EBITDA). Financial statements, such as 8-Ks, and leaked internal documents underwent unstructured analysis to search for anomalous data. The structured content analysis approach outlined here was applied to annual reports, earnings calls, and Terms of Service (ToS) data for both cases. A total of 521 documents were reviewed, 268 in the three document categories revealing 22,652 quotations from three primary theme and three concept codes. I used Atlas.ti qualitative data analysis (QDA) software to apply a non-hierarchical coding structure to the data. Three primary theme concepts from the literature were applied to the data: “user,” “data,” and “value,” with variations of these themes used in search terms. These three primary theme concepts were applied in various combinations and new concepts were used after initial analysis. For example, an inductive analysis found that artificial intelligence (AI) was a frequently used relevant concept in the data and a consultation of theory and the literature links the concept to the “value” theme. The resulting adjusted concepts used were “user engagement,” “user data,” and “AI,” with a multitude of related search terms.

As per Cognitive Market Research's latest published report, the Global MLOps market size was $1.21 Billion in 2022 and it is forecasted to reach $14.16 Billion by 2030. MLOps Industry's Compound Annual Growth Rate will be 39.57% from 2023 to 2030. What is the key driving factor for the MLOps market?

Increasing internet and digital penetration across the world and the adoption of MLOps technology in enterprises to improve productivity & operation is the key factor expected to drive the growth of the MLOps market.

What are the opportunities for the MLOps market?

Increasing investment in the healthcare industry and MLOps help to reduce costs for the whole machine learning lifecycle expected to create growth opportunities for the MLOps market in the forecast period.

Implementation of AutoML in MLOps Models is driving the market to grow.

Automating the whole machine learning pipeline, including data management, to installations, democratized ML brings it to those with limited ML expertise. AutoML has a number of easy and accessible solutions that do not require pre-determined ML expertise. With ML doing the majority of data labelling process, the chances of human mistakes are significantly reduced. It cuts down on human resources costs, allowing businesses to concentrate more on data analysis. AutoML tries to streamline the entire process by reducing certain manually tiresome steps while training an ML model, viz., feature choosing, model picking, model fitting, and evaluating the model. Some cloud solutions, like Amazon Sagemaker, Data Robot AI platform, and Microsoft Power BI, give their proprietary AutoML offerings. For Instance, Amazon revealed the availability of Sagemaker Autopilot directly from within Amazon Sagemaker pipelines to automate MLOps industry seamlessly. It allows the automation of an end-to-end process of building machine learning models with Autopilot and integrating models into subsequent CI/CD phases. The benefits of AutoML integration with machine learning operations facilitate businesses in generating better ML models more effectively, at lesser expenses, and overcome the skillset deficit. Such conditions drive the deployment of AutoML in such solutions, thus furthering the MLOps market growth. (Source: - https://aws.amazon.com/blogs/machine-learning/launch-amazon-sagemaker-autopilot-experiments-directly-from-within-amazon-sagemaker-pipelines-to-easily-automate-mlops-workflows/ )

What is the growth hampering factor for the MLOps market?

The lack of a skilled workforce, rigid business models, data security, and inaccessible data are key factors anticipated to hamper the growth of the MLOps market.

Inability to Ensure Security in MLOps Environment to Restrict Market Growth

Machine learning operates incessantly on sensitive projects with extremely critical data. Therefore, making sure that the environment is secure is paramount for the long-term success of the project. For example, Most of the time, users are not aware that they possess several vulnerabilities that represent a window of opportunity for malicious attacks. Moreover, processing outdated libraries is the most prevalent problem confronted by organizations. Further, the security disadvantage is related to the model endpoints and data pipelines not being adequately secured. They have the risk of exposing publicly accessible, key data to third parties that have an influence over the data security in MLOps setup. Therefore, security for the machine learning operations environment can be a limiting factor. It can inhibit the productivity and efficiency of machine-learning models, affecting enterprises' business.

What is MLOps?

MLOps is a method of adapting DevOps practices to machine learning development processes. This is used in transitioning from running a couple of ML models manually to using ML models in the company operation. MLOps helps to make data science productive, reduce defects, improve delivery time, and reduce defects. Furthermore, MLOps is the missing bridge between data science, data engineering, and machine learning.