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Last updated on 2025年2月6日

What are some common data metadata tools and platforms that you use or recommend?

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Data metadata standards and interoperability are essential for data processing, as they enable consistent and accurate data sharing, analysis, and reuse. However, different data sources and domains may have different metadata models, formats, and vocabularies, which can pose challenges for data integration and interoperability. In this article, we will explore some common data metadata tools and platforms that you can use or recommend to manage, harmonize, and exchange data metadata across different systems and contexts.

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Allan ROSS ???????

复杂数据处理专家 | 卫星遥感和计算机视觉研发领域的地球物理工程师/HPML???? @ TotalEnergies SE ?????? | 创新者 | 神经多样性 | 生成式人工智能 | 分形主义者 ??
Cláudio Cardoso

Data Processing | Java | Spring Boot | APDADOS? Member | Control-M | Process Automation | ETL | Database: Oracle…
Sahel Eskandar, Ph.D., M.Sc.

Data Expert | FinTech | Machine Learning

1 Data metadata basics

Data metadata is the information that describes the characteristics, quality, provenance, and context of data. It can include information such as data type, format, structure, schema, identifier, location, source, owner, license, date, version, and so on. Data metadata helps data users and processors to understand, find, access, and use data effectively and efficiently. It also supports data governance, quality assurance, and compliance.

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Allan ROSS ???????

复杂数据处理专家 | 卫星遥感和计算机视觉研发领域的地球物理工程师/HPML???? @ TotalEnergies SE ?????? | 创新者 | 神经多样性 | 生成式人工智能 | 分形主义者 ??
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Several data metadata tools and platforms are widely recognized for managing and organizing metadata. General tools include Alation, Collibra, Informatica Metadata Manager, IBM InfoSphere Information Governance Catalog, and SAP PowerDesigner. Open-source tools include Apache Atlas, Amundsen, DataHub, and OpenMetadata. Specialized platforms include CKAN, Catmandu, and Waterline Data. The choice of tool depends on an organization’s specific needs, such as data types, compliance requirements, and operational scale. Each tool offers unique features to streamline metadata management and enhance data governance practices.

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Cláudio Cardoso

Data Processing | Java | Spring Boot | APDADOS? Member | Control-M | Process Automation | ETL | Database: Oracle, Postgree
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Metadados de dados s?o essenciais para gerenciar e utilizar eficazmente grandes volumes de informa??o. Algumas ferramentas e plataformas comuns de metadados incluem: 1. Apache Atlas: Oferece governan?a de dados e metadados para ecossistemas Hadoop. 2. Collibra: Plataforma abrangente de governan?a e catálogo de dados. 3. Alation: Combina catálogo de dados com colabora??o e descoberta. 4. Informatica Enterprise Data Catalog: Solu??o robusta para descoberta e gerenciamento de metadados. 5. AWS Glue Data Catalog: Servi?o de metadados gerenciado para dados na nuvem AWS. Essas ferramentas ajudam a organizar, descobrir e gerenciar metadados, melhorando a compreens?o e utiliza??o dos dados em toda a organiza??o.

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2 Data metadata standards

Data metadata standards are the rules and guidelines that define how data metadata should be created, formatted, documented, and exchanged. They can be general or domain-specific, depending on the purpose and scope of the data. Some examples of general data metadata standards are Dublin Core, ISO 19115, and DataCite. Some examples of domain-specific data metadata standards are Darwin Core for biodiversity data, DDI for social science data, and DICOM for medical imaging data.

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Cláudio Cardoso

Data Processing | Java | Spring Boot | APDADOS? Member | Control-M | Process Automation | ETL | Database: Oracle, Postgree
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Os padr?es de metadados de dados s?o cruciais para garantir consistência e interoperabilidade. Alguns exemplos importantes incluem: 1. Dublin Core: Amplamente usado para descrever recursos digitais, oferecendo 15 elementos básicos. 2. ISO 19115: Padr?o para metadados geoespaciais, essencial em sistemas de informa??o geográfica. 3. DataCite: Focado em facilitar a cita??o e descoberta de dados de pesquisa. 4. Darwin Core: Específico para dados de biodiversidade, facilitando o compartilhamento de informa??es sobre espécies. 5. DDI (Data Documentation Initiative): Projetado para descrever dados nas ciências sociais e comportamentais.

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3 Data metadata interoperability

Data metadata interoperability is the ability of different data systems and applications to exchange and use data metadata without losing or compromising its meaning and quality. Data metadata interoperability can be achieved at different levels, such as syntactic, semantic, and pragmatic. Syntactic interoperability refers to the compatibility of data metadata formats and structures. Semantic interoperability refers to the consistency of data metadata meanings and vocabularies. Pragmatic interoperability refers to the alignment of data metadata purposes and contexts.

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Cláudio Cardoso

Data Processing | Java | Spring Boot | APDADOS? Member | Control-M | Process Automation | ETL | Database: Oracle, Postgree
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A interoperabilidade de metadados de dados é fundamental para o compartilhamento e uso eficiente de informa??es entre diferentes sistemas. Ela opera em três níveis principais: 1. Interoperabilidade sintática: Garante que os formatos e estruturas dos metadados sejam compatíveis entre sistemas. Isso pode envolver o uso de formatos padronizados como XML ou JSON. 2. Interoperabilidade semantica: Assegura que o significado dos metadados seja consistente, frequentemente através do uso de vocabulários controlados ou ontologias comuns. 3. Interoperabilidade pragmática: Alinha os propósitos e contextos dos metadados, considerando como eles ser?o utilizados em diferentes ambientes.

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4 Data metadata tools

Data metadata tools are the software applications that help data users and processors to create, edit, manage, validate, and publish data metadata. They can be standalone or integrated with data management systems, such as databases, repositories, or catalogs. Some examples of data metadata tools are Metadata Editor, Catmandu, and CKAN. Data metadata tools can help to automate and streamline the data metadata workflow, as well as to ensure the compliance and quality of data metadata.

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Cláudio Cardoso

Data Processing | Java | Spring Boot | APDADOS? Member | Control-M | Process Automation | ETL | Database: Oracle, Postgree
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As ferramentas de metadados de dados s?o essenciais para gerenciar eficazmente as informa??es sobre os dados. Algumas ferramentas populares incluem: 1. Metadata Editor: Ferramenta versátil para criar e editar metadados em diversos formatos. 2. Catmandu: Framework flexível para processamento e convers?o de metadados, popular em bibliotecas e arquivos. 3. CKAN (Comprehensive Knowledge Archive Network): Plataforma de código aberto para publica??o e gerenciamento de dados, incluindo metadados. 4. Apache Atlas: Oferece recursos abrangentes de governan?a e metadados para ecossistemas de big data. 5. Collibra Catalog: Ferramenta robusta para descoberta, organiza??o e governan?a de metadados empresariais.

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5 Data metadata platforms

Data metadata platforms are the online services that provide data users and processors with access to data metadata from various sources and domains. They can be general or domain-specific, depending on the coverage and scope of the data. Some examples of general data metadata platforms are DataCite, CrossRef, and OpenAIRE. Some examples of domain-specific data metadata platforms are GBIF for biodiversity data, ICPSR for social science data, and BioPortal for biomedical ontologies. Data metadata platforms can help to facilitate and enhance data discovery, integration, and reuse.

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Sahel Eskandar, Ph.D., M.Sc.

Data Expert | FinTech | Machine Learning
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These are just a few examples of the many data metadata tools and platforms available. The choice of tool or platform depends on the specific needs and requirements of an organization. Apache Atlas: A metadata management platform for Hadoop that allows for the definition, management, and discovery of metadata within a Hadoop ecosystem. Apache NiFi: A data integration platform that allows for the collection, transformation, and routing of data from various sources. NiFi also has built-in metadata capabilities. Apache Hudi: A data management framework for streaming and batch processing on Apache Hadoop that includes a metadata management system.

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Cláudio Cardoso

Data Processing | Java | Spring Boot | APDADOS? Member | Control-M | Process Automation | ETL | Database: Oracle, Postgree
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As plataformas de metadados de dados s?o cruciais para o acesso e gerenciamento eficiente de informa??es. Algumas plataformas notáveis incluem: 1. DataCite: Fornece DOIs (Identificadores de Objeto Digital) para conjuntos de dados, facilitando cita??o e descoberta. 2. CrossRef: Oferece servi?os de liga??o de referências e metadados para publica??es acadêmicas. 3. OpenAIRE: Plataforma que agrega metadados de pesquisa de várias fontes europeias. 4. GBIF (Global Biodiversity Information Facility): Específica para dados de biodiversidade global. 5. ICPSR (Inter-university Consortium for Political and Social Research): Focada em dados de ciências sociais e comportamentais.

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Olga Kokhan

CEO at Tinkogroup | Data Entry, Data Annotation, Lead Generation Services
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?? If you’re working with large datasets, a good metadata platform is a lifesaver. It helps keep track of where data comes from, how it's structured, and who’s using it. I’ve tested a few, and here’s what I’d recommend: ? Apache Atlas – great for managing metadata in big data environments. If you're working with Hadoop, this is a solid choice. ? Alation – user-friendly and AI-powered, perfect for teams that need strong collaboration and governance. ? Collibra – a bit enterprise-heavy, but if data governance is a priority, this tool is worth considering. ? DataHub – open-source and flexible, ideal if you need customization. At the end of the day, the best platform depends on your needs. Test them out and see what fits your workflow! ??

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6 Data metadata best practices

Data metadata best practices are the recommendations and principles that guide data users and processors to create, manage, and share data metadata in a consistent and effective way. They can be general or domain-specific, depending on the needs and expectations of the data community. Some examples of general data metadata best practices are FAIR principles, Metadata 2020, and RDA Metadata Standards Directory. Some examples of domain-specific data metadata best practices are Darwin Core Quick Reference Guide, DDI Codebook, and DICOM Conformance Statement. Data metadata best practices can help to improve the quality and interoperability of data metadata.

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Sahel Eskandar, Ph.D., M.Sc.

Data Expert | FinTech | Machine Learning
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More: Alation: provides a centralized repository for all enterprise data assets, including metadata. Collibra: Data governance platform that includes metadata management as one of its core capabilities. Informatica Metadata Manager: Metadata management tool that helps organizations discover, understand, and govern their data assets. IBM InfoSphere Information Governance Catalog: Metadata management that provides a unified view of enterprise metadata across different platforms and applications. Talend Metadata Manager: Metadata management for the management and discovery of metadata across various sources and platforms. Waterline Data: A data catalog uses ML to discover, classify, and manage metadata across an organization

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What are some common data metadata tools and platforms that you use or recommend?

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1 Data metadata basics

2 Data metadata standards

3 Data metadata interoperability

4 Data metadata tools

5 Data metadata platforms

6 Data metadata best practices

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