Metadata form template for Tempe Open Data.

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Data Catalog Market Size 2025-2029

The data catalog market size is forecast to increase by USD 5.03 billion, at a CAGR of 29.5% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the increasing demand for self-service analytics. With the proliferation of big data and the need for organizations to derive valuable insights from their data, there is a growing emphasis on having easily accessible and searchable catalogs. Another key trend in the market is the emergence of data mesh architecture, which aims to distribute data ownership and management across the organization. However, maintaining catalog accuracy over time poses a significant challenge. As data volumes continue to grow and change rapidly, ensuring that catalogs remain up-to-date and accurate becomes increasingly difficult.
Companies seeking to capitalize on the opportunities presented by the market must invest in robust catalog management solutions and adopt best practices for data governance. At the same time, they must also address the challenge of maintaining catalog accuracy by implementing automated data discovery and catalog update processes. By doing so, they can ensure that their catalogs remain a valuable asset, enabling efficient data access and driving better business outcomes.

What will be the Size of the Data Catalog Market during the forecast period?

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The market continues to evolve, driven by the increasing importance of data-driven decision making across various sectors. Data standardization methods, such as the Data Mesh framework, play a crucial role in ensuring consistency and interoperability in data management. A notable example is a financial services company that achieved a 25% increase in sales by implementing a standardized data asset inventory using master data management and reference data management techniques. Industry growth in data cataloging is expected to reach 20% annually, fueled by the adoption of data lake architecture, data model optimization, and metadata schema design. Data version control, data access control, semantic enrichment, and data lineage tracking are essential components of data cataloging software, enabling effective data governance policies and metadata management.
Data anonymization methods, data cleansing processes, and data observability tools are integral to maintaining data quality. Data integration platforms employ data quality rules, entity resolution techniques, and data usage monitoring to ensure data accuracy and consistency. Data profiling techniques and data transformation pipelines facilitate the conversion of raw data into valuable insights. Data virtualization, data warehouse design, and data mapping tools enable seamless access to data, while knowledge graph creation and data governance policies foster collaboration and data sharing.

How is this Data Catalog Industry segmented?

The data catalog 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.

Component

  Solutions
  Services


Deployment

  Cloud
  On-premises


Type

  Technical metadata
  Business metadata
  Operational metadata


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    Russia
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Component Insights

The Solutions segment is estimated to witness significant growth during the forecast period. Data catalog solutions have gained significant traction in the US business landscape, addressing the pressing needs of data discovery, governance, collaboration, and data lifecycle management. According to recent studies, over 35% of organizations have adopted data catalog solutions, a testament to their value in streamlining data management processes. Looking ahead, industry experts anticipate that the demand for data catalog solutions will continue to grow, with expectations of a 30% increase in market penetration in the coming years. These solutions enable users to efficiently search and discover relevant datasets for their analytical and reporting requirements, reducing the time spent locating data and encouraging data reuse. Metadata plays a crucial role in understanding unstructured data, which is increasingly prevalent in sectors like healthcare and e-commerce.

Centralized metadata storage offers detailed information about datasets, including source, schema, data quality, and lineage, enhancing data understanding, facilitating governance, and ensuring context for effective data utilization. Data catalog solutions are a crucial component of modern data management and analytics ecosystems, continually evolving to meet the dynamic needs of

Analyst Viewpoint

The data catalog market is expanding rapidly, driven by the ever-increasing need for efficient data management, metadata management, and secure deployment solutions. The dominance of enterprise applications and the rapid growth of on-premise deployments reflect enterprises' push toward localized, secure, and scalable solutions.

Additionally, the increasing emphasis on technical metadata to enhance data governance and analytics will fuel continued growth. The IT and telecom sectors, with their vast datasets and complex architectures, are likely to remain major drivers of demand for data catalog solutions, presenting numerous opportunities for market players to innovate and provide tailored solutions.

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An excel template with data elements and conventions corresponding to the openLCA unit process data model. Includes LCA Commons data and metadata guidelines and definitions Resources in this dataset:Resource Title: READ ME - data dictionary. File Name: lcaCommonsSubmissionGuidelines_FINAL_2014-09-22.pdfResource Title: US Federal LCA Commons Life Cycle Inventory Unit Process Template. File Name: FedLCA_LCI_template_blank EK 7-30-2015.xlsxResource Description: Instructions: This template should be used for life cycle inventory (LCI) unit process development and is associated with an openLCA plugin to import these data into an openLCA database. See www.openLCA.org to download the latest release of openLCA for free, and to access available plugins.

HUD's Enterprise Data Listing in JSON machine readable format. (Schema Version 1.1)

This is a template for publishing your dataset with Datahub Cloud.

Data Dictionary template for Tempe Open Data.

Koncile reads your product catalogs (PDF, scans, various formats) and automatically extracts key data: references, prices, descriptions, categories. Excel, JSON, or API export.

This database contains a comprehensive inventory of geologic (coral, coral reef, limestone, and sediment) cores and samples collected, analyzed, published, and/or archived by, or in collaboration with, the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC). The SPCMSC Geologic Core and Sample Database includes geologic cores and samples collected beginning in the 1970s to present day, from study sites across the world. This database captures metadata about samples throughout the USGS Science Data Lifecycle: including field collection, laboratory analysis, publication of research, and archival or deaccession. For more information about the USGS Science Data Lifecycle, see USGS Open-File Report 2013-1265 (https://doi.org/10.3133/ofr20131265). The SPCMSC Geologic Core and Sample Database also includes storage locations for physical samples and cores archived in a repository (USGS SPCMSC or elsewhere, if known). The majority of the samples and cores in this database come from field activities associated with the SPCMSC and have been assigned a field activity number (FAN) in the field activity management and data inventory tool for USGS Coastal and Marine Hazards and Resources Program (CMHRP) Coastal and Marine Geoscience Data System (CMGDS), https://cmgds.marine.usgs.gov/. Some cores and samples were retroactively assigned FANs based on existing metadata and published information. Cores and samples without FANs indicate there is insufficient information regarding collection of the core(s) or sample(s) needed in order to assign a field activity number in CMGDS. Please see the supplemental information section of the metadata for more information about FANs. All samples and cores contained in this database are described in published research. The database contains a link to the FAN page within the CMGDS for each sample or core where associated publications can be accessed. For a complete list of fields used in this database, please refer to the entity and attribute information section of this metadata record.

The open data portal catalogue is a downloadable dataset containing some key metadata for the general datasets available on the Government of Canada's Open Data portal. Resource 1 is generated using the ckanapi tool (external link) Resources 2 - 8 are generated using the Flatterer (external link) utility. ###Description of resources: 1. Dataset is a JSON Lines (external link) file where the metadata of each Dataset/Open Information Record is one line of JSON. The file is compressed with GZip. The file is heavily nested and recommended for users familiar with working with nested JSON. 2. Catalogue is a XLSX workbook where the nested metadata of each Dataset/Open Information Record is flattened into worksheets for each type of metadata. 3. datasets metadata contains metadata at the dataset level. This is also referred to as the package in some CKAN documentation. This is the main table/worksheet in the SQLite database and XLSX output. 4. Resources Metadata contains the metadata for the resources contained within each dataset. 5. resource views metadata contains the metadata for the views applied to each resource, if a resource has a view configured. 6. datastore fields metadata contains the DataStore information for CSV datasets that have been loaded into the DataStore. This information is displayed in the Data Dictionary for DataStore enabled CSVs. 7. Data Package Fields contains a description of the fields available in each of the tables within the Catalogue, as well as the count of the number of records each table contains. 8. data package entity relation diagram Displays the title and format for column, in each table in the Data Package in the form of a ERD Diagram. The Data Package resource offers a text based version. 9. SQLite Database is a .db database, similar in structure to Catalogue. This can be queried with database or analytical software tools for doing analysis.

Data catalog of the Provincial Council of Valladolid in RDF/XML format based on the DCAT vocabulary, developed by the World Wide Web Consortium (W3C) and which allows standardisation in the definition of document catalogs and information resources. A catalog of documents and information resources is represented by instances of the dcat:Catalog class and includes a collection of dcat:Dataset information resource sets. Data catalog of the Provincial Council of Valladolid in RDF/XML format based on the DCAT vocabulary, developed by the World Wide Web Consortium (W3C) and which allows standardisation in the definition of document catalogs and information resources. A catalog of documents and information resources is represented by instances of the dcat:Catalog class and includes a collection of dcat:Dataset information resource sets.

This dataset contains basic metadata in the CSV data format about datasets contained in the Czech National Open Data Catalog (NODC-CZ).

Data catalog of the Diputación de Cádiz in RDF/XML format based on the vocabulary DCAT, developed by the World Wide Web Consortium (W3C) and which allows standardisation in the definition of document catalogs and information resources. A catalog of documents and information resources is represented by instances of the dcat:Catalog class and includes a collection of dcat:Dataset information resource sets. Using the download URL http://datosabiertos.dipucadiz.es/dcat you can federate the catalog to [http://datos.gob.es]. It is also presented in RSS format to allow subscriptions to it.

This resource contains a Jupyter notebook that demonstrates how someone can query the I-GUIDE data catalog, retrieve data, and execute a code workflow.

A data catalog site is a portal site that provides a data catalog (a directory or index of data). It allows searching using metadata (data attributes and descriptive information; specifically, title, URL, data format, creator, etc.). Currently, the data published is mainly statistical information and geospatial information, and the number of data is still small, but we plan to expand it sequentially. Translated from Japanese Original Text: データカタログサイトとは、データカタログ（データの目録・索引）を提供するポータルサイトのことです。 メタデータ（データの属性・説明情報。具体的には、タイトル・URL・データ形式・作成者等）による検索が可能です。 現在公開しているデータは、統計情報や地理空間情報などが中心でまだまだデータ数が少ないですが、順次拡大していく予定です。

This database was prepared using a combination of materials that include aerial photographs, topographic maps (1:24,000 and 1:250,000), field notes, and a sample catalog. Our goal was to translate sample collection site locations at Yellowstone National Park and surrounding areas into a GIS database. This was achieved by transferring site locations from aerial photographs and topographic maps into layers in ArcMap. Each field site is located based on field notes describing where a sample was collected. Locations were marked on the photograph or topographic map by a pinhole or dot, respectively, with the corresponding station or site numbers. Station and site numbers were then referenced in the notes to determine the appropriate prefix for the station. Each point on the aerial photograph or topographic map was relocated on the screen in ArcMap, on a digital topographic map, or an aerial photograph. Several samples are present in the field notes and in the catalog but do not corresp ...

Data.public.lu provides all its metadata in the DCAT and DCAT-AP formats, i.e. all data about the data stored or referenced on data.public.lu. DCAT (Data Catalog Vocabulary) is a specification designed to facilitate interoperability between data catalogs published on the Web. This specification has been extended via the DCAT-AP (DCAT Application Profile for data portals in Europe) standard, specifically for data portals in Europe. The serialisation of those vocabularies is mainly done in RDF (Resource Description Framework). The implementation of data.public.lu is based on the one of the open source udata platform. This API enables the federation of multiple Data portals together, for example, all the datasets published on data.public.lu are also published on data.europa.eu. The DCAT API from data.public.lu is used by the european data portal to federate its metadata. The DCAT standard is thus very important to guarantee the interoperability between all data portals in Europe. Usage Full catalog You can find here a few examples using the curl command line tool: To get all the metadata from the whole catalog hosted on data.public.lu curl https://data.public.lu/catalog.rdf Metadata for an organization To get the metadata of a specific organization, you need first to find its ID. The ID of an organization is the last part of its URL. For the organization "Open data Lëtzebuerg" its URL is https://data.public.lu/fr/organizations/open-data-letzebuerg/ and its ID is open-data-letzebuerg. To get all the metadata for a given organization, we need to call the following URL, where {id} has been replaced by the correct ID: https://data.public.lu/api/1/organizations/{id}/catalog.rdf Example: curl https://data.public.lu/api/1/organizations/open-data-letzebuerg/catalog.rdf Metadata for a dataset To get the metadata of a specific dataset, you need first to find its ID. The ID of dataset is the last part of its URL. For the dataset "Digital accessibility monitoring report - 2020-2021" its URL is https://data.public.lu/fr/datasets/digital-accessibility-monitoring-report-2020-2021/ and its ID is digital-accessibility-monitoring-report-2020-2021. To get all the metadata for a given dataset, we need to call the following URL, where {id} has been replaced by the correct ID: https://data.public.lu/api/1/datasets/{id}/rdf Example: curl https://data.public.lu/api/1/datasets/digital-accessibility-monitoring-report-2020-2021/rdf Compatibility with DCAT-AP 2.1.1 The DCAT-AP standard is in constant evolution, so the compatibility of the implementation should be regularly compared with the standard and adapted accordingly. In May 2023, we have done this comparison, and the result is available in the resources below (see document named 'udata 6 dcat-ap implementation status"). In the DCAT-AP model, classes and properties have a priority level which should be respected in every implementation: mandatory, recommended and optional. Our goal is to implement all mandatory classes and properties, and if possible implement all recommended classes and properties which make sense in the context of our open data portal.

Airport Data Catalog Market Outlook

According to our latest research, the global airport data catalog market size reached USD 1.47 billion in 2024 and is forecasted to expand to USD 4.36 billion by 2033, reflecting a robust compound annual growth rate (CAGR) of 12.9% during the forecast period. The market’s dynamic expansion is driven by the increasing digital transformation initiatives across the aviation industry, the growing need for real-time data analytics, and the demand for enhanced passenger experiences at airports worldwide. The proliferation of smart airport projects and the integration of advanced technologies such as IoT and AI are further catalyzing the adoption of comprehensive data catalog solutions in airport environments.

One of the primary growth factors for the airport data catalog market is the rising emphasis on operational efficiency and safety within airports. As air travel rebounds post-pandemic and passenger volumes surge, airports are under pressure to optimize resource allocation, streamline flight operations, and minimize turnaround times. Data catalog solutions enable seamless aggregation, indexing, and governance of vast data sets, including flight schedules, baggage handling, and maintenance logs. By providing a unified view of operational data, these systems empower airport operators and airlines to make informed, data-driven decisions that enhance both efficiency and safety. The ability to analyze historical trends and predict potential disruptions also supports proactive planning and risk mitigation.

Another significant driver is the increasing focus on delivering superior passenger experiences. The modern traveler expects personalized, seamless, and efficient journeys, from check-in to boarding. Airport data catalogs play a pivotal role by integrating and harmonizing passenger data across various touchpoints, including ticketing, security, and retail. This data-driven approach allows airports and airlines to offer tailored services, reduce wait times, and improve overall satisfaction. Additionally, the adoption of biometric verification and automated self-service kiosks, powered by real-time data access, is transforming the passenger journey and setting new benchmarks for convenience and security.

The rapid adoption of cloud-based solutions is another catalyst propelling the airport data catalog market. Cloud deployment offers unparalleled scalability, flexibility, and cost-effectiveness, enabling airports of all sizes to implement sophisticated data management tools without heavy upfront investments in IT infrastructure. Cloud-based catalogs facilitate seamless data sharing and collaboration among stakeholders—airports, airlines, ground handlers, and government agencies—across geographies. As cybersecurity threats become more sophisticated, cloud providers are also enhancing security protocols, ensuring that sensitive airport data remains protected while being readily accessible for authorized users.

From a regional perspective, North America currently leads the market, buoyed by significant investments in airport modernization and digital infrastructure. Europe follows closely, driven by stringent regulatory frameworks and a strong focus on passenger-centric services. The Asia Pacific region is poised for the highest growth, supported by rapid airport expansion projects in countries such as China, India, and Southeast Asia. These regions are witnessing unprecedented air traffic growth, necessitating advanced data catalog solutions to manage complex operations and enhance competitiveness on the global stage.

Component Analysis

The component segment of the airport data catalog market is bifurcated into software and services. The software sub-segment dominates the market, as airports increasingly rely on sophisticated platforms to catalog, index, and manage vast amounts of operational, passenger, and asset data. These software solutions are designed to integrate seamlessly with

This dataset contains basic metadata in the CSV data format about distributions of datasets contained in the Czech National Open Data Catalog (NODC-CZ).

USGS Geochron is a database of geochronological and thermochronological dates and data. The USGS Geochron: Data Compilation Templates data release hosts Microsoft Excel-based data compilation templates for the USGS Geochron database. Geochronological and thermochronological methods currently archived in the USGS Geochron database include radiocarbon, cosmogenic (10Be, 26Al, 3He), fission track, (U-Th)/He, U-series, U-Th-Pb, 40Ar/39Ar, K-Ar, Lu-Hf, Rb-Sr, Sm-Nd, and Re-Os dating methods. For questions or to submit data please contact geochron@usgs.gov