OpenWeb Ninja's Google Images Data (Google SERP Data) API provides real-time image search capabilities for images sourced from all public sources on the web.

The API enables you to search and access more than 100 billion images from across the web including advanced filtering capabilities as supported by Google Advanced Image Search. The API provides Google Images Data (Google SERP Data) including details such as image URL, title, size information, thumbnail, source information, and more data points. The API supports advanced filtering and options such as file type, image color, usage rights, creation time, and more. In addition, any Advanced Google Search operators can be used with the API.

OpenWeb Ninja's Google Images Data & Google SERP Data API common use cases:

• Creative Media Production: Enhance digital content with a vast array of real-time images, ensuring engaging and brand-aligned visuals for blogs, social media, and advertising.

• AI Model Enhancement: Train and refine AI models with diverse, annotated images, improving object recognition and image classification accuracy.

• Trend Analysis: Identify emerging market trends and consumer preferences through real-time visual data, enabling proactive business decisions.

• Innovative Product Design: Inspire product innovation by exploring current design trends and competitor products, ensuring market-relevant offerings.

• Advanced Search Optimization: Improve search engines and applications with enriched image datasets, providing users with accurate, relevant, and visually appealing search results.

OpenWeb Ninja's Annotated Imagery Data & Google SERP Data Stats & Capabilities:

• 100B+ Images: Access an extensive database of over 100 billion images.

• Images Data from all Public Sources (Google SERP Data): Benefit from a comprehensive aggregation of image data from various public websites, ensuring a wide range of sources and perspectives.

• Extensive Search and Filtering Capabilities: Utilize advanced search operators and filters to refine image searches by file type, color, usage rights, creation time, and more, making it easy to find exactly what you need.

• Rich Data Points: Each image comes with more than 10 data points, including URL, title (annotation), size information, thumbnail, and source information, providing a detailed context for each image.

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Real-Time Location Systems (RTLS) Market Size 2025-2029

The real-time location systems (rtls) market size is forecast to increase by USD 45.5 billion, at a CAGR of 42.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasingly low cost of Radio Frequency Identification (RFID) tags and the adoption of Ultra-Wideband (UWB) technology. RFID tags, a key component of RTLS, have seen a notable decrease in price, making them more accessible and cost-effective for businesses seeking to implement location tracking systems. UWB RTLS technology, known for its high accuracy and ability to provide real-time location data, is gaining traction in various industries, including healthcare, manufacturing, and logistics. However, the market faces challenges as well.
One major obstacle is the high implementation costs associated with deploying RTLS solutions. This includes the expense of hardware, software, and installation services. Additionally, ensuring interoperability between different RTLS systems and integrating them with existing IT infrastructure can add to the financial burden. Companies must carefully weigh the benefits of implementing RTLS against these costs to make informed strategic decisions.

What will be the Size of the Real-Time Location Systems (RTLS) Market during the forecast period?

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Real-Time Location Systems (RTLS) continue to evolve and unfold in diverse applications across various sectors, driving market dynamics with unrelenting momentum. Healthcare monitoring and patient tracking systems utilize RTLS for improved care delivery and enhanced safety. In emergency response situations, RTLS enables quick identification and location of individuals in need. Energy efficiency gains are achieved through RTLS-enabled power consumption monitoring and optimization. Support services benefit from RTLS for streamlined workflow automation and improved productivity. Network infrastructure and cost reduction are enhanced through wireless communication and deployment services. Data visualization tools offer valuable insights through data aggregation and real-time alerts.

RTLS technology is integrated with RFID tags, ultrasonic sensor fusion, and positioning algorithms for proximity detection and outdoor positioning. Security protocols ensure data encryption and error reduction, while API integrations facilitate seamless system integration. The ongoing development of RTLS technology encompasses the deployment of mobile apps, cloud platforms, and web applications for real-time data access. Indoor positioning systems, such as those utilizing Ultra-Wideband (UWB) technology, expand the capabilities of RTLS to previously uncharted territories. Continuous innovation in RTLS technology is shaping the future of industries, from healthcare and emergency response to logistics and security management. The integration of real-time location tracking, Wi-Fi positioning, and data analytics is revolutionizing the way businesses operate, offering unprecedented levels of efficiency, productivity, and safety.

How is this Real-Time Location Systems (RTLS) Industry segmented?

The real-time location systems (rtls) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Healthcare
  Transportation and logistics
  Retail
  Government
  Others


Solution

  Systems
  Tags


Technology

  Active RFID
  Passive RFID
  Others


Management

  Inventory/asset tracking and management
  Access control and security
  Environmental monitoring
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    The Netherlands
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Application Insights

The healthcare segment is estimated to witness significant growth during the forecast period.

The market is experiencing notable growth across various industries, with a particular focus on healthcare. In this sector, RTLS solutions are revolutionizing patient care and asset management. The advantages of real-time tracking and cost savings have led to increased adoption in hospitals. Indoor Location Based Services (LBS) are a key application, integrating RTLS with clinical systems for enhanced analytics. RTLS technology facilitates improved operational efficiency, security, and safety. Positioning algorithms, such as Wi-Fi positioning and Ultra-Wideband (UWB), enable accurate indoor positioning.

Bluetooth beacons and RFID tags are essential components, supporting proximity detection and asset tracking. Integration wi

The oceanographic time series data collected by U.S. Geological Survey scientists and collaborators are served in an online database at http://stellwagen.er.usgs.gov/index.html. These data were collected as part of research experiments investigating circulation and sediment transport in the coastal ocean. The experiments (projects, research programs) are typically one month to several years long and have been carried out since 1975. New experiments will be conducted, and the data from them will be added to the collection. As of 2016, all but one of the experiments were conducted in waters abutting the U.S. coast; the exception was conducted in the Adriatic Sea. Measurements acquired vary by site and experiment; they usually include current velocity, wave statistics, water temperature, salinity, pressure, turbidity, and light transmission from one or more depths over a time period. The measurements are concentrated near the sea floor but may also include data from the water column. The user interface provides an interactive map, a tabular summary of the experiments, and a separate page for each experiment. Each experiment page has documentation and maps that provide details of what data were collected at each site. Links to related publications with additional information about the research are also provided. The data are stored in Network Common Data Format (netCDF) files using the Equatorial Pacific Information Collection (EPIC) conventions defined by the National Oceanic and Atmospheric Administration (NOAA) Pacific Marine Environmental Laboratory. NetCDF is a general, self-documenting, machine-independent, open source data format created and supported by the University Corporation for Atmospheric Research (UCAR). EPIC is an early set of standards designed to allow researchers from different organizations to share oceanographic data. The files may be downloaded or accessed online using the Open-source Project for a Network Data Access Protocol (OPeNDAP). The OPeNDAP framework allows users to access data from anywhere on the Internet using a variety of Web services including Thematic Realtime Environmental Distributed Data Services (THREDDS). A subset of the data compliant with the Climate and Forecast convention (CF, currently version 1.6) is also available.

This record is for Approval for Access product AfA104 Real-time Flood Data River Levels via our API. This dataset covers monitoring data that is only updated on our systems during a routine daily update cycle, some sites will update more frequently as we update the field technology used over the next few years till 2025. During times of flooding, or other water level related incidents our update frequency can be increased if the technology at site allows. Readings are transferred via telemetry to internal and external systems in or close to real-time. This data is transferred to other systems, including the API. Data for sites in Wales is included in the Open Data feed, but is owned by Natural Resources Wales (NRW). NRW also class the data as Open Data, and you may use it under the same terms as the England data (the standard Open Government Licence, available on The National Archives website). Measurements of the height (m) of water in a river lake or coastal site are taken using automatic field instruments and typically log the value every 15 minutes. Information is available for 1400 river gauging stations (where flow is also measured) and 1800 river level only monitoring sites throughout England as well as some reservoirs and coastal sites. Attribution statement: © Environment Agency copyright and/or database right 2021. All rights reserved.

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Web Real Time Communication (WebRTC) Market Size 2025-2029

The web real time communication (WebRTC) market size is forecast to increase by USD 247.7 billion, at a CAGR of 62.6% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing demand for easy-to-use real-time communication solutions. This trend is further fueled by the integration of WebRTC with internet of things (IoT) sensors, enabling seamless communication between devices and users. However, the market faces challenges, primarily the lack of high-end video conferencing features, which may hinder its adoption in corporate environments. Companies seeking to capitalize on this market's opportunities should focus on enhancing the user experience and addressing the need for advanced video conferencing features.
By doing so, they can effectively navigate the competitive landscape and establish a strong presence in the rapidly evolving WebRTC market.

What will be the Size of the Web Real Time Communication (WebRTC) Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The Web Real-Time Communication (WebRTC) market continues to evolve, with dynamic applications across various sectors. Real-time video streaming, online gaming, and distance learning are key areas where WebRTC shines. Packet loss concealment, video conferencing, and WebRTC gateways ensure seamless communication. Adaptive bitrate streaming and interoperability testing maintain quality and compatibility. Signaling protocols and media negotiation facilitate session establishment. Jitter buffer and error correction optimize performance. Noise suppression and echo cancellation enhance audio processing. WebRTC SDKs and APIs simplify integration. Browser compatibility and live streaming expand reach.

Interactive broadcasting and peer-to-peer communication foster engagement. Network congestion control and session management ensure reliability. Media codecs and chat applications enrich user experience. WebRTC's continuous evolution includes advancements in signaling servers, performance benchmarking, and firewall traversal. The market's unfolding patterns reflect the ongoing integration of these features into innovative applications.

How is this Web Real Time Communication (WebRTC) Industry segmented?

The web real time communication (WebRTC) industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Video
  Voice
  Data sharing


Platform

  Mobile
  Browser
  UC


End-User

  Retail
  BFSI
  IT & Telecom
  Media & Entertainment
  Third-Party Logistics (3PL)
  Retail
  BFSI
  IT & Telecom
  Media & Entertainment
  Third-Party Logistics (3PL)


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Application Insights

The video segment is estimated to witness significant growth during the forecast period.

Web Real Time Communication (WebRTC) technology is revolutionizing business communication by enabling real-time, high-quality video streaming and conferencing directly through web and mobile applications. This innovative solution eliminates the need for additional software or plugins, providing a seamless user experience. The technology's reliability is ensured through features like jitter buffer, packet loss concealment, and error correction. WebRTC's versatility extends beyond video conferencing. It's used extensively in online gaming, distance learning, and interactive broadcasting, offering a more immersive and harmonious communication experience. The technology's media negotiation capabilities allow for adaptive bitrate streaming, ensuring optimal performance even in network congestion.

WebRTC's interoperability is crucial, as it allows for peer-to-peer communication and firewall traversal, making it a preferred choice for remote collaboration and real-time chat applications. Signaling protocols facilitate session establishment and management, while media codecs support various audio and video formats. WebRTC's SDKs and APIs, such as getUserMedia, RTCPeerConnection, and RTCDataChannel, are built into modern browsers, making implementation easy and efficient. WebRTC gateways further enhance its functionality by enabling interoperability between WebRTC and non-WebRTC endpoints. Performance benchmarking and network congestion control are essential for maintaining a high-quality user experience. WebRTC solutions address these challenges through advanced techniques like echo cancel

description: Approximately 5,000 of the 6,900 U.S. Geological Survey sampling stations are equipped with telemetry to transmit data on streamflow, temperature, and other parameters back to a data base for real-time viewing via the World Wide Web. A map of the realtime stations is produced every day.; abstract: Approximately 5,000 of the 6,900 U.S. Geological Survey sampling stations are equipped with telemetry to transmit data on streamflow, temperature, and other parameters back to a data base for real-time viewing via the World Wide Web. A map of the realtime stations is produced every day.

This dataset provides comprehensive real-time data from Amazon's global marketplaces. It includes detailed product information, reviews, seller profiles, best sellers, deals, influencers, and more across all Amazon domains worldwide. The data covers product attributes like pricing, availability, specifications, reviews and ratings, as well as seller information including profiles, contact details, and performance metrics. Users can leverage this dataset for price monitoring, competitive analysis, market research, and building e-commerce applications. The API enables real-time access to Amazon's vast product catalog and marketplace data, helping businesses make data-driven decisions about pricing, inventory, and market positioning. Whether you're conducting market analysis, tracking competitors, or building e-commerce tools, this dataset provides current and reliable Amazon marketplace data. The dataset is delivered in a JSON format via REST API.

GapMaps Live is an easy-to-use location intelligence platform available across 25 countries globally that allows you to visualise your own store data, combined with the latest demographic, economic and population movement intel right down to the micro level so you can make faster, smarter and surer decisions when planning your network growth strategy.

With one single login, you can access the latest estimates on resident and worker populations, census metrics (eg. age, income, ethnicity), consuming class, retail spend insights and point-of-interest data across a range of categories including fast food, cafe, fitness, supermarket/grocery and more.

Some of the world's biggest brands including McDonalds, Subway, Burger King, Anytime Fitness and Dominos use GapMaps Live Map Data as a vital strategic tool where business success relies on up-to-date, easy to understand, location intel that can power business case validation and drive rapid decision making.

Primary Use Cases for GapMaps Live Map Data include:

1. Retail Site Selection - Identify optimal locations for future expansion and benchmark performance across existing locations.
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers and where to find more of them.
3. Analyse your catchment areas at a granular grid levels using all the key metrics
4. Target Marketing: Develop effective marketing strategies to acquire more customers.
5. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)
6. Customer Profiling
7. Target Marketing
8. Market Share Analysis

Some of features our clients love about GapMaps Live Map Data include: - View business locations, competitor locations, demographic, economic and social data around your business or selected location - Understand consumer visitation patterns (“where from” and “where to”), frequency of visits, dwell time of visits, profiles of consumers and much more. - Save searched locations and drop pins - Turn on/off all location listings by category - View and filter data by metadata tags, for example hours of operation, contact details, services provided - Combine public data in GapMaps with views of private data Layers - View data in layers to understand impact of different data Sources - Share maps with teams - Generate demographic reports and comparative analyses on different locations based on drive time, walk time or radius. - Access multiple countries and brands with a single logon - Access multiple brands under a parent login - Capture field data such as photos, notes and documents using GapMaps Connect and integrate with GapMaps Live to get detailed insights on existing and proposed store locations.

This record is for Approval for Access product AfA501 for approximately 1000 automatic rainfall data from the Environment Agency rainfall API.

The data is available on an update cycle which varies across the country, typically updated daily but updated faster is rainfall is detected. This is update frequency is usually increased during times of flooding, etc.

Readings are transferred via telemetry to internal and external systems in or close to real-time.

Measurement of the rainfall is taken in millimetres (mm) accumulated over 15 minutes. Note that rainfall data is recorded in GMT, so during British Summer Time (BST) data may appear to be an hour old. Data comes from a network of over 1000 gauges across England. Data shown is raw data collected from the gauges and is subject to quality control procedures. As a result, values may change after publication on this website.

Continuous rainfall information is also stored on our hydrometric archive, Wiski, and can be provided in non real-time on request through our customer contact centre. This raw rainfall data is provided to the Met Office for quality control along with all the data from our registered daily storage gauges (c.1400). The quality controlled dataset is covered in AfA148 Quality Controlled Daily and Monthly Raingauge Data from Environment Agency Gauges.

Data from a small selection of Met Office raingauges are included in our open data feed. This data is also available from the Met Office as open data.

GTFS Realtime feeds have been provided by the Victoria Department of Transport and Planning to provide realtime updates about Public Transport services. It contains feeds about: Trip Updates - delays, cancellations, changed routes Service Alerts - stop moved, unforeseen events affecting a station, route or the entire network Vehicle Positions - information about the vehicles including location and congestion level Please note these feeds are provided in the Protocol Buffer format and are not human readable. For more information refer to this GTFS Realtime page () which is maintained by MobilityData () and facilitates the GTFS and GTFS-R specification. API Key - To obtain an API Key please continue to signup using our old Data Exchange Platform (TEMPORARY) Summary of Changes GTFS-R - Trip Updates - Metro Train: Schedule Relationships, Stop ID, and Route ID have been added as additional fields to enhance real-time trip accuracy. GTFS-R - Vehicle Positions - Metro Train: Route ID added as a Conditionally Required field to improve data details. GTFS-R - Service Alerts - Metro Train: Additional information about planned and unplanned disruptions have been added to include more details about delays and bus replacement services. Route ID and Direction ID are two new fields that have been added also. Please note: the information in the current ‘Required’ data fields remains the same and data feeds continue to follow the GTFS Realtime global standards and specifications. These data feeds will also still be hosted via the Data Exchange Platform, so there are no changes to API keys. However, in the next few months, they will be moved to the Transport Victoria Open Data Portal and users will need to register for a new API key. We will provide more information closer to the date. Why are these changes being made? These changes are being made to: Create consistent and accurate real-time transport information for open data users. Improve trip planning and data usability by better aligning static and real-time datasets.

The Real-time Data Transfer Service market is experiencing robust growth, driven by the increasing demand for immediate data access across diverse sectors. The market size in 2025 is estimated at $15 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. This significant expansion is fueled by several key factors. The proliferation of IoT devices and the rise of big data analytics necessitate the swift and reliable transfer of large datasets. Furthermore, industries like healthcare, finance, and manufacturing are increasingly reliant on real-time data for critical decision-making, fueling the demand for high-performance, low-latency solutions. Cloud-based solutions are dominating the market, offering scalability and cost-effectiveness compared to on-premise solutions. However, concerns regarding data security and regulatory compliance pose significant challenges to market growth. The competitive landscape is characterized by both established technology giants and specialized providers, leading to continuous innovation and competitive pricing. Geographical expansion is also a significant driver, with North America currently holding the largest market share due to early adoption and technological advancement, followed by Europe and Asia-Pacific, which are expected to witness substantial growth in the coming years. The segmentation of the market by application reveals a diverse range of industries leveraging real-time data transfer services. Healthcare and Life Sciences represent a major segment, driven by the need for immediate access to patient data and the growing adoption of telehealth. Manufacturing utilizes real-time data for optimized production processes and predictive maintenance. Transportation and Logistics rely on instant data for efficient supply chain management and real-time tracking. The Energy and Utilities sector benefits from real-time data monitoring for grid optimization and enhanced operational efficiency. Finally, the Public Sector utilizes real-time data for improved citizen services and enhanced public safety. This varied application across industries signifies the widespread adoption and critical role of real-time data transfer services in the modern digital economy. The ongoing development of advanced technologies like 5G and edge computing is expected to further accelerate market expansion in the forecast period.

These four NetCDF databases constitute the bulk of the spatial and spatiotemporal environmental covariates used in a latent health factor index (LHFI) model for assessment and prediction of ecosystem health across the MDB. The data formatting and hierarchical statistical modelling were conducted under a CSIRO appropriation project funded by the Water for a Healthy Country Flagship from July 2012 to June 2014. Each database was created by collating and aligning raw data downloaded from the respective state government websites (QLD, NSW, VIC, and SA). (ACT data were unavailable.) There are two primary components in each state-specific database: (1) a temporally static data matrix with axes "Site ID" and "Variable," and (2) a 3D data cube with axes "Site ID", "Variable," and "Date." Temporally static variables in (1) include geospatial metadata (all states), drainage area (VIC and SA only), and stream distance (SA only). Temporal variables in (2) include discharge, water temperature, etc. Missing data (empty cells) are highly abundant in the data cubes. The attached state-specific README.pdf files contain additional details on the contents of these databases, and any computer code that was used for semi-automation of raw data downloads. Lineage: (1) For NSW I created the NetCDF database by (a) downloading CSV raw data from the NSW Office of Water real-time data website (http://realtimedata.water.nsw.gov.au/water.stm) during February-April 2013, then (b) writing computer programs to preprocess such raw data into the current format. (2) The same was done for QLD, except through the Queensland Water Monitoring Data Portal (http://watermonitoring.derm.qld.gov.au/host.htm). (3) The same was also done for SA, except through the SA WaterConnect => Data Systems => Surface Water Data website (https://www.waterconnect.sa.gov.au/Systems/SWD/SitePages/Home.aspx) during April 2013 as well as May 2014. (4) For Victoria I created the NetCDF database by (a) manually downloading XLS raw data during November and December in 2013 from the Victoria DEPI Water Measurement Information System => Download Rivers and Streams sites website (http://data.water.vic.gov.au/monitoring.htm), then (b) writing computer programs to preprocess such raw data into CSV format (intermediate), then into the current final format.

Additional details on lineage are available from the attached README.pdf files.

This project develop components of a polar cyberinfrastructure (CI) to support researchers and users for data discovery and access. The main goal is to provide tools that will enable a better access to polar data and information, hence allowing to spend more time on analysis and research, and significantly less time on discovery and searching. A large-scale web crawler, PolarHub, is developed to continuously mine the Internet to discover dispersed polar data. Beside identifying polar data in major data repositories, PolarHub is also able to bring individual hidden resources forward, hence increasing the discoverability of polar data. Quality and assessment of data resources are analyzed inside of PolarHub, providing a key tool for not only identifying issues but also to connect the research community with optimal data resources.

In the current PolarHub system, seven different types of geospatial data and processing services that are compliant with OGC (Open Geospatial Consortium) are supported in the system. They are: -- OGC Web Map Service (WMS): is a standard protocol for serving (over the Internet)georeferenced map images which a map server generates using data from a GIS database. -- OGC Web Feature Service (WFS): provides an interface allowing requests for geographical features across the web using platform-independent calls. -- OGC Web Coverage Service (WCS): Interface Standard defines Web-based retrieval of coverages; that is, digital geospatial information representing space/time-varying phenomena. -- OGC Web Map Tile Service (WMTS): is a standard protocol for serving pre-rendered georeferenced map tiles over the Internet. -- OGC Sensor Observation Service (SOS): is a web service to query real-time sensor data and sensor data time series and is part of theSensor Web. The offered sensor data comprises descriptions of sensors themselves, which are encoded in the Sensor Model Language (SensorML), and the measured values in the Observations and Measurements (O and M) encoding format. -- OGC Web Processing Service (WPS): Interface Standard provides rules for standardizing how inputs and outputs (requests and responses) for invoking geospatial processing services, such as polygon overlay, as a web service. -- OGC Catalog Service for the Web (CSW): is a standard for exposing a catalogue of geospatial records in XML on the Internet (over HTTP). The catalogue is made up of records that describe geospatial data (e.g. KML), geospatial services (e.g. WMS), and related resources.

PolarHub has three main functions: (1) visualization and metadata viewing of geospatial data services; (2) user-guided real-time data crawling; and (3) data filtering and search from PolarHub data repository.

PredictLeads Job Openings Data provides high-quality hiring insights sourced directly from company websites - not job boards. Using advanced web scraping technology, our dataset offers real-time access to job trends, salaries, and skills demand, making it a valuable resource for B2B sales, recruiting, investment analysis, and competitive intelligence.

Key Features:

✅214M+ Job Postings Tracked – Data sourced from 92 Million company websites worldwide. ✅7,1M+ Active Job Openings – Updated in real-time to reflect hiring demand. ✅Salary & Compensation Insights – Extract salary ranges, contract types, and job seniority levels. ✅Technology & Skill Tracking – Identify emerging tech trends and industry demands. ✅Company Data Enrichment – Link job postings to employer domains, firmographics, and growth signals. ✅Web Scraping Precision – Directly sourced from employer websites for unmatched accuracy.

Primary Attributes:

• id (string, UUID) – Unique identifier for the job posting.
• type (string, constant: "job_opening") – Object type.
• title (string) – Job title.
• description (string) – Full job description, extracted from the job listing.
• url (string, URL) – Direct link to the job posting.
• first_seen_at – Timestamp when the job was first detected.
• last_seen_at – Timestamp when the job was last detected.
• last_processed_at – Timestamp when the job data was last processed.

Job Metadata:

• contract_types (array of strings) – Type of employment (e.g., "full time", "part time", "contract").
• categories (array of strings) – Job categories (e.g., "engineering", "marketing").
• seniority (string) – Seniority level of the job (e.g., "manager", "non_manager").
• status (string) – Job status (e.g., "open", "closed").
• language (string) – Language of the job posting.
• location (string) – Full location details as listed in the job description.
• Location Data (location_data) (array of objects)
• city (string, nullable) – City where the job is located.
• state (string, nullable) – State or region of the job location.
• zip_code (string, nullable) – Postal/ZIP code.
• country (string, nullable) – Country where the job is located.
• region (string, nullable) – Broader geographical region.
• continent (string, nullable) – Continent name.
• fuzzy_match (boolean) – Indicates whether the location was inferred.

Salary Data (salary_data)

• salary (string) – Salary range extracted from the job listing.
• salary_low (float, nullable) – Minimum salary in original currency.
• salary_high (float, nullable) – Maximum salary in original currency.
• salary_currency (string, nullable) – Currency of the salary (e.g., "USD", "EUR").
• salary_low_usd (float, nullable) – Converted minimum salary in USD.
• salary_high_usd (float, nullable) – Converted maximum salary in USD.
• salary_time_unit (string, nullable) – Time unit for the salary (e.g., "year", "month", "hour").

Occupational Data (onet_data) (object, nullable)

• code (string, nullable) – ONET occupation code.
• family (string, nullable) – Broad occupational family (e.g., "Computer and Mathematical").
• occupation_name (string, nullable) – Official ONET occupation title.

Additional Attributes:

• tags (array of strings, nullable) – Extracted skills and keywords (e.g., "Python", "JavaScript").

📌 Trusted by enterprises, recruiters, and investors for high-precision job market insights.

PredictLeads Dataset: https://docs.predictleads.com/v3/guide/job_openings_dataset

Note:- Only publicly available data can be worked upon

APISCRAPY collects and organizes data from Zillow's massive database, whether it's property characteristics, market trends, pricing histories, or more. Because of APISCRAPY's first-rate data extraction services, tracking property values, examining neighborhood trends, and monitoring housing market variations become a straightforward and efficient process.

APISCRAPY's Zillow real estate data scraping service offers numerous advantages for individuals and businesses seeking valuable insights into the real estate market. Here are key benefits associated with their advanced data extraction technology:

1. Real-time Zillow Real Estate Data: Users can access real-time data from Zillow, providing timely updates on property listings, market dynamics, and other critical factors. This real-time information is invaluable for making informed decisions in a fast-paced real estate environment.

2. Data Customization: APISCRAPY allows users to customize the data extraction process, tailoring it to their specific needs. This flexibility ensures that the extracted Zillow real estate data aligns precisely with the user's requirements.

3. Precision and Accuracy: The advanced algorithms utilized by APISCRAPY enhance the precision and accuracy of the extracted Zillow real estate data. This reliability is crucial for making well-informed decisions related to property investments and market trends.

4. Efficient Data Extraction: APISCRAPY's technology streamlines the data extraction process, saving users time and effort. The efficiency of the extraction workflow ensures that users can access the desired Zillow real estate data without unnecessary delays.

5. User-friendly Interface: APISCRAPY provides a user-friendly interface, making it accessible for individuals and businesses to navigate and utilize the Zillow real estate data scraping service with ease.

APISCRAPY provides real-time real estate market data drawn from Zillow, ensuring that consumers have access to the most up-to-date and comprehensive real estate insights available. Our real-time real estate market data services aren't simply a game changer in today's dynamic real estate landscape; they're an absolute requirement.

Our dedication to offering high-quality real estate data extraction services is based on the utilization of Zillow Real Estate Data. APISCRAPY's integration of Zillow Real Estate Data sets it different from the competition, whether you're a seasoned real estate professional or a homeowner wanting to sell, buy, or invest.

APISCRAPY's data extraction is a key element, and it is an automated and smooth procedure that is at the heart of the platform's operation. Our platform gathers Zillow real estate data quickly and offers it in an easily consumable format with the click of a button.

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Real-time API access to rich Google Images Data (Google SERP Data) with 100B+ images sourced from Google Images - the largest image index on the web.

DataForSEO Labs API offers three powerful keyword research algorithms and historical keyword data:

• Related Keywords from the “searches related to” element of Google SERP. • Keyword Suggestions that match the specified seed keyword with additional words before, after, or within the seed key phrase. • Keyword Ideas that fall into the same category as specified seed keywords. • Historical Search Volume with current cost-per-click, and competition values.

Based on in-market categories of Google Ads, you can get keyword ideas from the relevant Categories For Domain and discover relevant Keywords For Categories. You can also obtain Top Google Searches with AdWords and Bing Ads metrics, product categories, and Google SERP data.

You will find well-rounded ways to scout the competitors:

• Domain Whois Overview with ranking and traffic info from organic and paid search. • Ranked Keywords that any domain or URL has positions for in SERP. • SERP Competitors and the rankings they hold for the keywords you specify. • Competitors Domain with a full overview of its rankings and traffic from organic and paid search. • Domain Intersection keywords for which both specified domains rank within the same SERPs. • Subdomains for the target domain you specify along with the ranking distribution across organic and paid search. • Relevant Pages of the specified domain with rankings and traffic data. • Domain Rank Overview with ranking and traffic data from organic and paid search. • Historical Rank Overview with historical data on rankings and traffic of the specified domain from organic and paid search. • Page Intersection keywords for which the specified pages rank within the same SERP.

All DataForSEO Labs API endpoints function in the Live mode. This means you will be provided with the results in response right after sending the necessary parameters with a POST request.

The limit is 2000 API calls per minute, however, you can contact our support team if your project requires higher rates.

We offer well-rounded API documentation, GUI for API usage control, comprehensive client libraries for different programming languages, free sandbox API testing, ad hoc integration, and deployment support.

We have a pay-as-you-go pricing model. You simply add funds to your account and use them to get data. The account balance doesn't expire.

Summary information about locations of environmental monitoring sites that have monitoring data publicly available. Types of monitoring sites are air quality, water quality, storm tides, wave heights and direction. Each site provides links to download its data and to its associated webpage if it exists.

Field descriptions
Monitoring type: The type of monitoring being conducted at that location
Site name: The name of the site
Latitude: The latitude in decimal degrees
Longitude: The longitude in decimal degrees
Resource label: The name of the resource (data file) that is available for download
Start date: First date of the monitoring for that resource
End date: Last date of the monitoring for that resource
Near real-time period: If the resource contains near real-time data, this field indicates the numerical length of the period
Period type: If the resource contains near real-time data, this field indicates the type of period, e.g. day, current year, etc
Update frequency: Indicates how often the resource is updated
Resource Url: The location of the resource to download the data
Website Url: The location of the webpage associated with this site, if it exists

N/A

The realtime_down_sites data shows the location of telemetered traffic monitoring sites that are experiencing problems with reporting hourly polling data and speed information. Sites can be down for a number of different reasons. The locations of these down sites are provided to augment the real-time houlry polling data to provide additional information about where sites might not be available. Hourly real-time polling is activated for a hurricane or other emergencies in Florida. This dataset is maintained by the Transportation Data & Analytics office (TDA). This hosted feature layer was updated on: 06-05-2025 11:35:04.Download Data: Enter Guest as Username to download the source shapefile from here: https://ftp.fdot.gov/file/d/FTP/FDOT/co/planning/transtat/gis/special_projects/real_time/realtime_down_sites.zip