High rate data processed to single-look complex SAR images for each antenna. Gridded tile (approx 64x64 km2); half swath (left or right side of full swath). Available in netCDF-4 file format.

High rate data processed to single-look complex SAR images for each antenna. Gridded tile (approx 64x64 km2); half swath (left or right side of full swath). Available in netCDF-4 file format.Please note that this collection contains SWOT Version C science data products.

Data Product Platform Market Outlook

According to our latest research, the global data product platform market size reached USD 6.1 billion in 2024, driven by the rapid digital transformation and the increasing emphasis on data-driven decision-making across industries. The market is expected to grow at a robust CAGR of 23.7% from 2025 to 2033, projecting a value of USD 49.8 billion by 2033. This remarkable growth is primarily fueled by the integration of advanced analytics, artificial intelligence, and cloud computing into enterprise data strategies, enabling organizations to extract actionable insights and monetize their data assets more efficiently. As per the latest research, the market’s expansion is underpinned by the proliferation of big data, the rise of regulatory compliance requirements, and the need for seamless data integration across complex digital ecosystems.

One of the most significant growth factors for the data product platform market is the surge in enterprise data volumes, stemming from ubiquitous digitalization, IoT adoption, and the proliferation of connected devices. Enterprises across sectors such as BFSI, healthcare, retail, and manufacturing are generating and collecting unprecedented amounts of data, necessitating robust platforms capable of integrating, governing, and analyzing this information. The demand for platforms that can deliver end-to-end data management, from ingestion to monetization, is further amplified by the need for real-time insights to drive operational efficiency, customer experience, and competitive advantage. As organizations increasingly recognize data as a strategic asset, investment in comprehensive data product platforms is becoming a critical priority.

Another pivotal driver is the growing complexity of regulatory environments, particularly in sectors like finance, healthcare, and government. Regulations such as GDPR, CCPA, and HIPAA have heightened the focus on data governance, privacy, and security, compelling organizations to adopt platforms that offer robust compliance capabilities. Data product platforms that provide granular access controls, audit trails, and automated policy enforcement are witnessing increased adoption, as enterprises seek to mitigate regulatory risks while still unlocking the value of their data. This regulatory impetus not only drives platform adoption but also fosters innovation in data governance and security features, making compliance a catalyst for market growth.

The accelerated adoption of cloud computing and the evolution of hybrid and multi-cloud strategies are also propelling the data product platform market forward. Cloud-based deployment models offer scalability, flexibility, and cost-effectiveness, enabling organizations to manage and analyze vast datasets without the constraints of traditional on-premises infrastructure. Furthermore, the integration of AI and machine learning capabilities within data product platforms is enhancing predictive analytics, automation, and data monetization opportunities. These technological advancements are empowering enterprises to unlock new revenue streams, improve decision-making, and maintain agility in rapidly changing business environments, thus fueling sustained market expansion.

Regionally, North America continues to dominate the data product platform market, accounting for the largest share due to its mature technology landscape, high digital adoption, and presence of leading solution providers. However, the Asia Pacific region is emerging as the fastest-growing market, driven by rapid digital transformation, increasing investments in cloud infrastructure, and the proliferation of data-centric business models across China, India, and Southeast Asia. Europe’s market growth is supported by stringent data protection regulations and robust enterprise IT spending, while Latin America and the Middle East & Africa are witnessing steady adoption as organizations in these regions embark on their digitalization journeys. This global expansion underscores the universal imperative for effective data management and analytics solutions.

Company Datasets for valuable business insights!

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

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

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

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Product Analytics AI Market Outlook

According to our latest research, the global Product Analytics AI market size reached USD 5.2 billion in 2024, reflecting robust adoption across multiple industries. The market is expected to grow at a CAGR of 21.8% during the forecast period, reaching an estimated USD 40.3 billion by 2033. Key growth factors fueling this expansion include the increasing need for data-driven decision-making, rapid digital transformation, and the growing complexity of product ecosystems that demand advanced analytical tools. As per the latest research, organizations worldwide are leveraging AI-powered product analytics to enhance user experiences, optimize product features, and drive business growth.

One of the primary growth drivers for the Product Analytics AI market is the escalating demand for actionable insights into user behavior and product performance. Enterprises are increasingly recognizing the value of AI-driven analytics in deciphering complex data patterns, which enables them to make informed decisions about product development and marketing strategies. The proliferation of digital products and services, coupled with the exponential rise in data volumes generated by user interactions, has necessitated the adoption of sophisticated analytics platforms. These platforms utilize artificial intelligence to deliver real-time insights, automate reporting, and uncover hidden trends, thereby empowering organizations to stay ahead of the competition and meet evolving customer needs.

Another significant growth factor is the surge in digital transformation initiatives across various sectors, such as e-commerce, SaaS, BFSI, and healthcare. As organizations digitize their operations and customer engagement channels, the need for advanced analytics solutions that can handle large-scale, multi-channel data becomes paramount. AI-powered product analytics tools offer the scalability, flexibility, and intelligence required to analyze complex datasets, track feature adoption, and optimize user journeys. The integration of AI with product analytics not only enhances the accuracy of predictions but also enables proactive identification of issues and opportunities, leading to improved product outcomes and higher customer satisfaction.

Moreover, the increasing complexity of product ecosystems, driven by the rapid pace of innovation and the proliferation of connected devices, is pushing organizations to invest in AI-based analytics solutions. Modern products often span multiple platforms and interact with diverse user segments, making traditional analytics insufficient for comprehensive analysis. AI-powered product analytics bridges this gap by providing deep, granular insights into user interactions, feature usage, and retention patterns. This capability is particularly valuable for companies operating in competitive markets, where the ability to swiftly adapt products based on real-time feedback can be a key differentiator. As a result, the adoption of product analytics AI is becoming a strategic imperative for organizations aiming to deliver superior user experiences and drive sustainable growth.

Regionally, North America continues to dominate the Product Analytics AI market, driven by the presence of major technology companies, high digital adoption rates, and significant investments in AI research and development. However, the Asia Pacific region is emerging as a key growth engine, fueled by rapid economic development, increasing internet penetration, and the rise of digital-native businesses. Europe also remains a significant market, characterized by strong regulatory frameworks and a growing emphasis on data privacy and security. Latin America and the Middle East & Africa are witnessing steady growth, supported by expanding digital infrastructure and rising awareness of the benefits of AI-powered analytics. Together, these regional trends underscore the global momentum behind the adoption of Product Analytics AI solutions.

As the demand for more sophisticated analytical tools grows, the role of a Product Analytics Platform becomes increasingly vital. These platforms are designed to provide businesses with comprehensive insights into product performance and user interactions. By integrating advanced analytics capabilities, they enable organizations to track and analyze data across various

Data Prep Market size was valued at USD 4.02 Billion in 2024 and is projected to reach USD 16.12 Billion by 2031, growing at a CAGR of 19% from 2024 to 2031.

Global Data Prep Market Drivers

Increasing Demand for Data Analytics: Businesses across all industries are increasingly relying on data-driven decision-making, necessitating the need for clean, reliable, and useful information. This rising reliance on data increases the demand for better data preparation technologies, which are required to transform raw data into meaningful insights. Growing Volume and Complexity of Data: The increase in data generation continues unabated, with information streaming in from a variety of sources. This data frequently lacks consistency or organization, therefore effective data preparation is critical for accurate analysis. To assure quality and coherence while dealing with such a large and complicated data landscape, powerful technologies are required. Increased Use of Self-Service Data Preparation Tools: User-friendly, self-service data preparation solutions are gaining popularity because they enable non-technical users to access, clean, and prepare data. independently. This democratizes data access, decreases reliance on IT departments, and speeds up the data analysis process, making data-driven insights more available to all business units. Integration of AI and ML: Advanced data preparation technologies are progressively using AI and machine learning capabilities to improve their effectiveness. These technologies automate repetitive activities, detect data quality issues, and recommend data transformations, increasing productivity and accuracy. The use of AI and ML streamlines the data preparation process, making it faster and more reliable. Regulatory Compliance Requirements: Many businesses are subject to tight regulations governing data security and privacy. Data preparation technologies play an important role in ensuring that data meets these compliance requirements. By giving functions that help manage and protect sensitive information these technologies help firms negotiate complex regulatory climates. Cloud-based Data Management: The transition to cloud-based data storage and analytics platforms needs data preparation solutions that can work smoothly with cloud-based data sources. These solutions must be able to integrate with a variety of cloud settings to assist effective data administration and preparation while also supporting modern data infrastructure.

Data Product Marketplace with AI Market Outlook

According to our latest research, the global Data Product Marketplace with AI market size reached USD 3.2 billion in 2024, with a robust year-on-year expansion driven by the rising adoption of artificial intelligence in data-driven decision-making. The market is projected to grow at a CAGR of 23.4% between 2025 and 2033, reaching a forecasted value of USD 25.1 billion by 2033. This exceptional growth is primarily fueled by the surging demand for AI-powered data products across diverse industries, as organizations increasingly seek to leverage data assets for competitive advantage, operational efficiency, and innovation.

One of the primary growth factors for the Data Product Marketplace with AI market is the exponential increase in data generation across enterprises, combined with the urgent need for advanced analytics and automation. As organizations accumulate vast volumes of structured, semi-structured, and unstructured data, the complexity of extracting actionable insights has intensified. AI-powered data product marketplaces address this challenge by providing ready-to-use, customizable, and scalable data solutions that accelerate time-to-value for businesses. The proliferation of IoT devices, social media, and digital platforms has further contributed to the data deluge, making AI-driven data products indispensable for organizations aiming to stay ahead in a fast-evolving digital landscape.

Another significant driver is the growing emphasis on data monetization and data sharing within and across industries. Enterprises are increasingly recognizing data as a strategic asset, leading to the emergence of data marketplaces that facilitate secure, compliant, and efficient exchange of data products. The integration of AI technologies in these marketplaces enhances the discoverability, curation, and personalization of data offerings, enabling buyers to access high-quality, relevant datasets and analytics tools. This trend is particularly pronounced in sectors such as finance, healthcare, and retail, where timely and accurate data insights are critical for decision-making, risk management, and customer engagement.

Furthermore, advancements in cloud computing and the proliferation of as-a-service models are accelerating the adoption of AI-powered data product marketplaces. The availability of scalable, flexible, and cost-effective cloud infrastructure allows organizations of all sizes to participate in data ecosystems without significant upfront investments. Cloud-based deployment models also facilitate seamless integration with existing IT environments, enabling rapid experimentation and innovation. Additionally, regulatory developments around data privacy and security are prompting vendors to incorporate advanced AI-driven compliance and governance features, further boosting market growth.

From a regional perspective, North America currently dominates the Data Product Marketplace with AI market, accounting for the largest share in 2024 due to the strong presence of technology giants, advanced digital infrastructure, and a mature ecosystem for data-driven innovation. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid digital transformation, increasing investments in AI and cloud technologies, and a burgeoning startup landscape. Europe also exhibits significant growth potential, supported by robust regulatory frameworks and government initiatives aimed at fostering data economy and AI adoption. Latin America and the Middle East & Africa are gradually catching up, driven by increasing awareness and investments in digital technologies.

Component Analysis

The Component segment of the Data Product Marketplace with AI market is primarily divided into Platform and Services. The platform sub-segment constitutes the core infrastructure that enables the creation, distribution, and consumption of AI-powered data products. These platforms offer functionalities such as data cataloging, metadata management, data quality assessment, and AI-driven analytics, empowering organizations to seamlessly access and utilize diverse data assets. The growing demand for integrated platforms that support end-to-end data lifecycle management is a key factor driving the adoption of marketplace platforms, especially among large enterprises with complex data ecosystems.

On the

Purpose: Develop an easy-to-use data product to facilitate comparative effectiveness research involving complex patients. Scope: Claims data can be difficult to use, requiring experience to most appropriately aggregate to the patient level and to create meaningful variables such as treatments, covariates, and endpoints. Easy to use data products will accelerate meaningful comparative effectiveness research (CER). Methods: This project used data from the Medicare Chronic Condition Data Warehouse for patients hospitalized with acute myocardial infarction (AMI) or stroke in 2007 with two-year follow-up and one-year pre-admission baseline. The project joined over 100 raw data files per condition to create research-ready person- and service-level analytic files, code templates, and macros while at the same time adding uniformity in measures of comorbid conditions and other covariates. The data product was tested in a project on statin effectiveness in older patients with multiple comorbidities. Results: A programmer/analyst with no administrative claims data experience was able to use the data product to create an analytic dataset with minimal support aside from the documentation provided. Analytic dataset creation used the conditions, procedures, and timeline macros provided. The data structure created for AMI adapted successfully for stroke. Complexity increased and statin treatment decreased with age. The two-year survival benefit of statins post-AMI increased with age. Conclusion: Claims data can be made more user-friendly for CER research on complex conditions. The data product should be expanded by refreshing the cohort and increasing follow-up. Action is warranted to increase the rate of statin use among the oldest patients. Data Access: These data are not available from ICPSR. The data cannot be made publicly available. Data are stored on University of Iowa College of Public Health secure servers, and may be used only for projects covered within the aims of the original research protocol and Centers for Medicare and Medicaid Services (CMS)-approved data use agreement. Data sharing is allowed only for research protocols approved under data re-use requests by the CMS privacy board. The CMS process for data re-use requests is described at Research Data Assistance Center (ResDac). Please note that as of May 2013, the DUA covering this work is set to expire February 1, 2014. Thereafter, per the terms of the DUA, datasets created for this project may not be available. User guides are available from ICPSR for detailed descriptions of the data products, including a user guide for Acute Myocardial Infarction (AMI) Analytic Files and a user guide for Stroke and Transient Ischemic Attack (TIA) Analytic Files. Data dictionaries are available upon request. Please contact Nick Rudzianski (nicholas-rudzianski@uiowa.edu or 319-335-9783) for more information.

Unlock the full potential of your brand strategy with VisitIQ's™ comprehensive brand data solutions. Our platform provides real-time access to an extensive database of brand data analytics and insights, empowering businesses to make informed decisions and drive growth in today's fast-paced, consumer-driven marketplace. With VisitIQ™, you gain a competitive edge by accessing the most up-to-date information on connected consumer brands, allowing you to stay ahead of industry trends, refine your brand positioning, and enhance your marketing efforts.

VisitIQ™ offers a robust and meticulously curated brand database, encompassing a wide range of datasets that deliver a 360-degree view of the brand landscape. Our brand data includes critical information on market trends, brand perception, customer sentiment, competitive positioning, and industry benchmarks. By leveraging this wealth of data, you can gain a deeper understanding of how your brand is perceived in the market, identify growth opportunities, and craft strategies that resonate with your target audience.

Our analytics capabilities provide instant access to the latest brand data, enabling you to monitor changes in brand performance and consumer sentiment as they happen. This level of agility allows you to quickly respond to market dynamics, adjust your messaging, and optimize your campaigns for maximum impact. Whether you’re launching a new product, entering a new market, or trying to boost brand loyalty, the VisitIQ™ platform equips you with the insights needed to make proactive, data-driven decisions.

Our comprehensive brand datasets are ideal for businesses of all sizes, from emerging startups looking to build their brand from the ground up, to established enterprises seeking to maintain their market leadership. VisitIQ's™ data is sourced from a variety of reliable channels and continuously updated to ensure accuracy and relevance, giving you a solid foundation for developing winning strategies.

Whether you are looking to benchmark your brand against competitors, explore new market segments, or understand consumer perceptions, VisitIQ™ offers the real-time data analytics and insights you need to navigate the complex brand landscape with confidence. Harness the power of data to elevate your brand strategy and achieve sustained success with VisitIQ™.

Unacast's Location Data API provides actionable intelligence that is immediately ready for analysis. Unacast’s proprietary methodology involves processing raw location data from multiple sources by deduplicating, merging, and removing flawed and unusable data. We append attributes, apply forensics, spatially and temporally merge the data, and then provide various combinations or derivatives of privacy-friendly location intelligence to our customers.

Location data enables users to answer complex questions related to human mobility on a global scale. Enter your polygonal geofence coordinates (geoJSON format) or pseudonymized registration IDs, and select a time range. Based on your query, the API will deliver merged and deduplicated location data that is fully annotated with forensic flags, allowing you to choose only signals that meet your accuracy criteria.

Companies use location data for: - Product development - Advertising and marketing - Audience creation - Fraud detection - Predictive analysis - Path analysis - Business intelligence - Market analysis

Unlike other providers, Unacast does not share polygons or metadata for sensitive locations. This helps to ensure that visits to privacy-sensitive locations remain private and that organizations do not take unnecessary risks in their analysis.

Unacast's API data can be delivered right away by direct response or exported into an AWS S3.

Database Software Market Size and Forecast

Global Database Software Market size was valued at USD 145.69 Billion in 2024 and is projected to reach USD 186.72 Billion by 2031, growing at a CAGR of 3.15% from 2024 to 2031.

Database Software Market Drivers

Data Explosion: The exponential growth of data generated by various sources like IoT devices, social media, and e-commerce platforms fuels the demand for efficient database solutions to store, manage, and analyze this data.

Cloud Computing Adoption: The increasing adoption of cloud computing enables organizations to leverage scalable and cost-effective database solutions without significant upfront investments.

Big Data Analytics: The need to extract valuable insights from large datasets drives the demand for advanced database technologies capable of handling complex analytics workloads.

Database Software Market Restraints

Complex Data Management: Managing diverse and complex data structures, including unstructured and semi-structured data, can be challenging for traditional database systems.

Data Migration and Integration: Migrating existing data to new database systems and integrating data from multiple sources can be time-consuming and complex.

The SAR SLC product is a single look complex acquired in Image Mode. It is a digital image, with slant range and phase preserved, generated from raw SAR data using up-to-date auxiliary parameters. The products are intended for use in SAR quality assessment, calibration and interferometric applications. A minimum number of corrections and interpolations are performed on the data. Absolute calibration parameters (when available) are provided in the product annotation. Product characteristics: Pixel size: 8 m (range - across track) x 4 m (azimuth - along track – varying slightly depending on acquisition Pulse Repetition Frequency) Scene area: 100 km (range) x at least 102.5 km (azimuth) Scene size: 5000 samples (range) x at least 30000 lines (azimuth) Pixel depth: 32 bits signed integer (16 bits I, 16 bits Q) Total product volume: 575 MB Projection: Slant range- Number of looks: 1.

Fine-Grained Consent for Data Products Market Outlook

According to our latest research, the global Fine-Grained Consent for Data Products market size in 2024 stands at USD 1.42 billion, with a robust growth trajectory expected through the forecast period. The market is set to expand at a CAGR of 18.6% from 2025 to 2033, reaching an anticipated value of USD 7.32 billion by the end of 2033. This remarkable growth is primarily driven by the increasing demand for enhanced data privacy and compliance solutions across industries, as organizations worldwide strive to meet stringent regulatory requirements and empower users with more granular control over their personal data.

One of the primary growth factors fueling the Fine-Grained Consent for Data Products market is the rapid evolution of data privacy regulations such as the GDPR in Europe, CCPA in California, and similar legislations emerging globally. These frameworks mandate organizations to obtain explicit, granular consent from data subjects for specific data processing activities, thereby necessitating the adoption of advanced consent management solutions. As enterprises increasingly recognize the reputational and financial risks associated with non-compliance, there is a marked shift towards deploying fine-grained consent systems that allow for detailed user preferences, dynamic consent management, and real-time auditing. This regulatory landscape is further intensified by growing consumer awareness and demands for transparency, compelling businesses to invest in robust consent mechanisms as a competitive differentiator.

Another significant driver for market expansion is the accelerating digital transformation across sectors such as healthcare, BFSI, retail, and telecommunications. The proliferation of data-driven products and services, coupled with the rise of cloud computing and AI-powered analytics, has exponentially increased the volume and sensitivity of personal data being processed. Fine-grained consent solutions enable organizations to not only comply with legal mandates but also foster trust by granting users granular control over how, when, and by whom their data is accessed and utilized. This is particularly critical in industries handling sensitive data, such as patient health records or financial information, where personalized consent preferences can directly impact customer loyalty and operational efficiency.

Moreover, the integration of fine-grained consent frameworks into broader data governance and security architectures is gaining momentum. Organizations are leveraging these solutions to streamline consent capture, automate policy enforcement, and ensure continuous monitoring of consent status across complex data ecosystems. The advent of APIs, microservices, and interoperable platforms has made it easier to embed fine-grained consent capabilities into existing workflows, thus reducing implementation barriers and operational costs. As a result, the market is witnessing increased adoption among both large enterprises seeking to scale compliance efforts and small and medium enterprises aiming to gain a competitive edge through enhanced data stewardship.

Regionally, North America leads the Fine-Grained Consent for Data Products market, driven by early regulatory adoption, high digital maturity, and a strong presence of technology innovators. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid digitization, evolving privacy regulations, and heightened consumer expectations. Europe continues to play a pivotal role due to its stringent data protection laws and proactive industry initiatives. The market landscape is thus characterized by regional nuances in regulatory frameworks, technological readiness, and market maturity, shaping distinct opportunities and challenges for stakeholders across the globe.

Component Analysis

The Fine-Grained Consent for Data Products market is segmented by component into Software and Services, each playing a crucial role in enabling organizations to manage user consent with granularity and agility. The software segment encompasses consent management platforms, APIs, and integration tools that facilitate the capture, storage, and enforcement of user consent across various touchpoints. These platforms are increasingly leveraging advanced technologies such as artificial intelligence and machine learning to automate consent workflows, detect an

DataOps Platform Market size was valued at USD 4.02 Billion in 2023 and is projected to reach USD 16.22 Billion by 2031, growing at a CAGR of 21% from 2024 to 2031.

Key Market Drivers:

Rapid Digital Transformation Across Industries: As organizations undergo digital transformation, there is an increased demand for DataOps platforms. These platforms are integral in enabling businesses to enhance decision-making by automating data management and analytics processes. The seamless integration of digital technologies into business operations improves customer experience through real-time data collection, allowing businesses to refine their products and services based on customer feedback. Additionally, DataOps platforms streamline workflows and automate processes, leading to improved operational efficiency and reduced costs. Rising Demand for Real-Time Data Analytics: In today's fast-paced business environment, real-time data analytics is crucial for timely decision-making. DataOps platforms facilitate the rapid processing and analysis of real-time data streams, enabling businesses to gain immediate insights and respond swiftly to market changes. This capability is essential for maintaining competitive advantage and optimizing business operations. High Complexity of Data Integration: As data ecosystems become more complex, organizations face challenges in integrating and harmonizing diverse data sources, types, and structures. DataOps platforms provide robust solutions for data integration, transformation, and orchestration, making it easier to manage complex data environments. This complexity necessitates efficient tools to streamline data workflows and ensure consistency across systems. Increasing Demand for Data Reliability and Quality Assurance: With the growing emphasis on quick decision-making, organizations require reliable and high-quality data. DataOps platforms address this need by implementing rigorous data quality and assurance practices. This ensures that the data used for analysis is accurate and dependable, supporting effective decision-making processes. Growing Awareness of Data Pipeline Orchestration: There is an increasing recognition of the importance of data pipeline orchestration tools in enhancing organizational agility and operational efficiency. DataOps platforms offer comprehensive solutions for orchestrating data pipelines, which helps businesses manage and streamline their data processes more effectively. Expansion of Hybrid Cloud and Cloud Computing Solutions: The adoption of cloud computing and hybrid cloud environments is on the rise, driven by the need for scalable and flexible data storage and management solutions. DataOps platforms are increasingly being adopted by cloud-centric enterprises due to their ability to provide cloud-native solutions that leverage the scalability, flexibility, and agility of cloud infrastructure. Exponential Growth in Data Volume: The surge in data creation from diverse sources, including social media, sensors, IoT devices, and enterprise applications, is driving demand for DataOps platforms. Organizations need efficient solutions to handle, process, and analyze vast amounts of data effectively, making DataOps platforms essential for managing this data growth. Growing Adoption of Emerging Technologies: DataOps platforms support the integration and utilization of emerging technologies such as AI, machine learning, and IoT. As industries increasingly adopt these technologies, the need for robust DataOps solutions to facilitate data management and integration becomes more critical.

The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Coregistered Single-Look Complex (CSLC) from Sentinel-1 validated product consists of Single Look Complex (SLC) images which contain both amplitude and phase information of the complex radar return. The amplitude is primarily determined by ground surface properties (e.g., terrain slope, surface roughness, and physical properties), and phase primarily represents the distance between the radar and ground targets corrected for the geometrical distance between the two based on the knowledge from Digital Elevation Model and platform’s position, i.e., the CSLC phase represents residual geometrical distance between the sensor and target, the atmospheric propagation delay and the target movements. The CSLC-S1 product is derived from Copernicus Sentinel-1A and Sentinel-1B Interferometric Wide (IW) SLC data. The CSLC images are precisely aligned or “coregistered” to a pre-defined UTM/Polar stereographic map projection systems and posted at 5x10 m spacing in east and north direction, respectively. Each CSLC-S1 product corresponds to a single S1 burst and is distributed as a Hierarchical Data Format version 5 (HDF5) file following the CF-1.8 convention containing both data raster layers (e.g., geocoded complex backscatter, low-resolution correction look-up tables) and product metadata. OPERA CSLC-S1 products are available over North America which includes the USA and U.S. Territories, Canada within 200 km of the U.S. border, and all mainland countries from the southern U.S. border down to and including Panama. The OPERA CSLC-S1 product contains modified Copernicus Sentinel data (2016-2025).Due to the S1 mission’s narrow orbital tube, radar-geometry layers vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA CLSLC-S1 product, as they are produced only once or a limited number of times. The static layers are available in the associated OPERA Coregistered Single-Look Complex from Sentinel-1 Static Layers validated product (Version 1). Read our doc on how to get AWS Credentials to retrieve this data: https://cumulus.asf.alaska.edu/s3credentialsREADME

Success.ai’s LinkedIn Data Solutions offer unparalleled access to a vast dataset of 700 million public LinkedIn profiles and 70 million LinkedIn company records, making it one of the most comprehensive and reliable LinkedIn datasets available on the market today. Our employee data and LinkedIn data are ideal for businesses looking to streamline recruitment efforts, build highly targeted lead lists, or develop personalized B2B marketing campaigns.

Whether you’re looking for recruiting data, conducting investment research, or seeking to enrich your CRM systems with accurate and up-to-date LinkedIn profile data, Success.ai provides everything you need with pinpoint precision. By tapping into LinkedIn company data, you’ll have access to over 40 critical data points per profile, including education, professional history, and skills.

Key Benefits of Success.ai’s LinkedIn Data: Our LinkedIn data solution offers more than just a dataset. With GDPR-compliant data, AI-enhanced accuracy, and a price match guarantee, Success.ai ensures you receive the highest-quality data at the best price in the market. Our datasets are delivered in Parquet format for easy integration into your systems, and with millions of profiles updated daily, you can trust that you’re always working with fresh, relevant data.

Global Reach and Industry Coverage: Our LinkedIn data covers professionals across all industries and sectors, providing you with detailed insights into businesses around the world. Our geographic coverage spans 259M profiles in the United States, 22M in the United Kingdom, 27M in India, and thousands of profiles in regions such as Europe, Latin America, and Asia Pacific. With LinkedIn company data, you can access profiles of top companies from the United States (6M+), United Kingdom (2M+), and beyond, helping you scale your outreach globally.

Why Choose Success.ai’s LinkedIn Data: Success.ai stands out for its tailored approach and white-glove service, making it easy for businesses to receive exactly the data they need without managing complex data platforms. Our dedicated Success Managers will curate and deliver your dataset based on your specific requirements, so you can focus on what matters most—reaching the right audience. Whether you’re sourcing employee data, LinkedIn profile data, or recruiting data, our service ensures a seamless experience with 99% data accuracy.

• Best Price Guarantee: We offer unbeatable pricing on LinkedIn data, and we’ll match any competitor.
• Global Scale: Access 700 million LinkedIn profiles and 70 million company records globally.
• AI-Verified Accuracy: Enjoy 99% data accuracy through our advanced AI and manual validation processes.
• Real-Time Data: Profiles are updated daily, ensuring you always have the most relevant insights.
• Tailored Solutions: Get custom-curated LinkedIn data delivered directly, without managing platforms.
• Ethically Sourced Data: Compliant with global privacy laws, ensuring responsible data usage.
• Comprehensive Profiles: Over 40 data points per profile, including job titles, skills, and company details.
• Wide Industry Coverage: Covering sectors from tech to finance across regions like the US, UK, Europe, and Asia.

Key Use Cases:

• Sales Prospecting and Lead Generation: Build targeted lead lists using LinkedIn company data and professional profiles, helping sales teams engage decision-makers at high-value accounts.
• Recruitment and Talent Sourcing: Use LinkedIn profile data to identify and reach top candidates globally. Our employee data includes work history, skills, and education, providing all the details you need for successful recruitment.
• Account-Based Marketing (ABM): Use our LinkedIn company data to tailor marketing campaigns to key accounts, making your outreach efforts more personalized and effective.
• Investment Research & Due Diligence: Identify companies with strong growth potential using LinkedIn company data. Access key data points such as funding history, employee count, and company trends to fuel investment decisions.
• Competitor Analysis: Stay ahead of your competition by tracking hiring trends, employee movement, and company growth through LinkedIn data. Use these insights to adjust your market strategy and improve your competitive positioning.
• CRM Data Enrichment: Enhance your CRM systems with real-time updates from Success.ai’s LinkedIn data, ensuring that your sales and marketing teams are always working with accurate and up-to-date information.
• Comprehensive Data Points for LinkedIn Profiles: Our LinkedIn profile data includes over 40 key data points for every individual and company, ensuring a complete understanding of each contact:

LinkedIn URL: Access direct links to LinkedIn profiles for immediate insights. Full Name: Verified first and last names. Job Title: Current job titles, and prior experience. Company Information: Company name, LinkedIn URL, domain, and location. Work and Per...

The global Data Fabric Solutions market is experiencing robust expansion, projected to reach an estimated USD 2,185.6 million by 2025, with an impressive Compound Annual Growth Rate (CAGR) of 23.7% expected to persist through 2033. This significant surge is primarily propelled by the escalating need for unified data access and management across diverse and complex IT environments. Organizations are increasingly recognizing the limitations of traditional data integration methods, which struggle to cope with the velocity, volume, and variety of modern data. Data fabric solutions offer a compelling answer by abstracting underlying data complexities, enabling seamless data access, integration, and governance from disparate sources. Key drivers include the burgeoning adoption of cloud computing, the proliferation of big data analytics, and the growing demand for real-time data insights to inform strategic business decisions. Sectors like BFSI, Telecommunications and IT, and Healthcare and Life Sciences are at the forefront of this adoption, leveraging data fabrics to enhance customer experiences, optimize operations, and drive innovation. Further fueling this growth are emerging trends such as the increasing focus on data democratization, enabling broader access to data across organizations while maintaining stringent security and compliance. The rise of AI and Machine Learning applications, which are highly dependent on accessible and well-governed data, also acts as a significant catalyst. While the market presents immense opportunities, certain restraints, such as the initial implementation cost and the need for skilled personnel to manage complex data fabric architectures, could pose challenges. However, the ongoing advancements in data virtualization, metadata management, and automated data governance are progressively mitigating these concerns. The market is characterized by a competitive landscape with key players like Denodo Technologies, IBM, Informatica, and Oracle Corp, who are continuously innovating to offer advanced data fabric capabilities, including support for both disk-based and in-memory data fabric types, catering to a wide spectrum of enterprise needs. Here is a unique report description on Data Fabric Solutions, incorporating your specific requirements:

This in-depth report offers a panoramic view of the global Data Fabric Solutions market, meticulously analyzing its trajectory from the historical period of 2019-2024 to a projected forecast period of 2025-2033. The base year for estimation is 2025, providing a contemporary snapshot of market dynamics. With an anticipated market size to exceed $25,000 million by 2033, this study delves into the intricate factors shaping its growth. We will explore the market's concentration, innovative characteristics, the significant impact of evolving regulations, the threat posed by product substitutes, end-user concentration, and the prevalent level of Mergers & Acquisitions (M&A), which is estimated to involve transactions in the range of $100 million to $500 million annually.

This dataset contains UAVSAR Level 1 (L1) Single Look Complex (SLC) stack products for Delta-X flight lines acquired during 2021-03-27 to 2021-04-18 (spring) and 2021-09-03 to 2021-09-13 (fall). The data were collected by Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), a polarimetric L-band synthetic aperture radar flown on the NASA Gulfstream-III (C20) aircraft as part of the Delta-X campaign. The study area includes the Atchafalaya Basin, in Southern Louisiana, USA, within the Mississippi River Delta (MRD) floodplain. Repeat pass interferometric synthetic aperture (InSAR) data are a standard UAVSAR product delivered by the UAVSAR processing team. These repeat pass SLC stack co-registered time series data were used as the underlying data for higher level data products. These higher level products provide a time series of water level changes and address a goal of the Delta-X campaign to measure water-level changes throughout wetlands. Data quality was assessed by comparing water elevation estimates with data from in situ water level gauges throughout the study area. These L1 data contain slant range single look complex (SLC), latitude/longitude/height, look vector, doppler, and metadata files. The data are provided in SLC stack format (.slc) with associated annotation (.ann), latitude-longitude-height (.llh), look vector (.lkv), and Doppler centroid-slant range (*.dop) files. The single look complex (SLC) stacks are in the HH, HV, VH, and VV polarizations. The same area was sampled at approximately 30-minute intervals. The SLCs are not corrected for residual baseline (BU).

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Product Data Management Market Outlook

According to our latest research, the global Product Data Management (PDM) market size reached USD 4.2 billion in 2024, driven by increasing digital transformation initiatives and the growing complexity of product development cycles across industries. The market is poised for robust expansion, exhibiting a CAGR of 10.1% during the forecast period. By 2033, the Product Data Management market is projected to attain a value of USD 10.1 billion. The primary growth factor is the escalating need for centralized product information, which enables organizations to streamline workflows, enhance collaboration, and accelerate time-to-market for new products.

The rapid adoption of digital technologies and Industry 4.0 practices is significantly fueling the growth of the Product Data Management market. Enterprises are increasingly recognizing the value of integrating PDM systems to manage the ever-expanding volume of product-related data, especially in sectors like automotive, aerospace, and industrial machinery. The demand for real-time access to accurate product data is intensifying, as organizations aim to reduce errors, avoid costly rework, and ensure regulatory compliance. The proliferation of connected devices and the Internet of Things (IoT) further adds to the complexity, making robust PDM solutions indispensable for maintaining a competitive edge in highly dynamic markets.

Another pivotal growth driver is the rising emphasis on collaboration across geographically dispersed teams and supply chain partners. As product development becomes more globalized, seamless information sharing and version control are essential to ensure that stakeholders work with the latest data. Product Data Management platforms facilitate this by offering centralized repositories, automated workflows, and advanced security features. This not only enhances productivity but also mitigates the risks associated with data silos and miscommunication. The growing trend of remote work and the need for agile product development processes are further propelling organizations to invest in scalable and flexible PDM solutions.

The integration of artificial intelligence (AI) and machine learning (ML) technologies into Product Data Management systems is also transforming the market landscape. AI-powered PDM tools enable predictive analytics, intelligent search, and automated data classification, which significantly improve decision-making and operational efficiency. As organizations strive for digital maturity, the adoption of advanced PDM solutions that leverage AI and ML capabilities is expected to surge. This technological evolution is particularly pronounced in industries with complex product lifecycles and stringent regulatory requirements, such as healthcare, aerospace, and defense.

From a regional perspective, North America currently dominates the Product Data Management market, accounting for the largest revenue share in 2024. This leadership is attributed to the early adoption of digital solutions, a strong presence of major PDM vendors, and high investments in research and development. Europe follows closely, driven by robust manufacturing and engineering sectors, while the Asia Pacific region is witnessing the fastest growth rate due to rapid industrialization, expanding manufacturing bases, and increasing awareness of the benefits of PDM solutions. The Middle East & Africa and Latin America are also emerging as promising markets, supported by ongoing digital transformation and infrastructure development initiatives.

Component Analysis

The Product Data Management market, segmented by component, is primarily divided into software and services. The software segment constitutes the backbone of the market, encompassing standalone PDM solutions and integrated platforms that facilitate document management, version control, and workflow automation. As organizations increasingly seek to streamline their product development processes, the demand for robust and scalable PDM software has surged. Vendors are responding with feature-rich platforms that offer seamless integration with other enterprise systems such as Product Lifecycle Management (PLM), Enterprise Resource Planning (ERP), and Computer-Aided Design (CAD) tools. This integration capability ensures that product data flows effortlessly across the enterprise, reducing redundancies and improving overall efficiency.