{"id":4964,"date":"2025-07-17T12:28:03","date_gmt":"2025-07-17T09:28:03","guid":{"rendered":"https:\/\/www.certbolt.com\/certification\/?p=4964"},"modified":"2026-05-13T09:58:59","modified_gmt":"2026-05-13T06:58:59","slug":"pioneering-data-management-the-preeminent-role-of-cloudera-in-the-hadoop-landscape","status":"publish","type":"post","link":"https:\/\/www.certbolt.com\/certification\/pioneering-data-management-the-preeminent-role-of-cloudera-in-the-hadoop-landscape\/","title":{"rendered":"Pioneering Data Management: The Preeminent Role of Cloudera in the Hadoop Landscape"},"content":{"rendered":"

The first decade of the twenty-first century brought with it a data explosion of proportions that existing enterprise technology architectures were fundamentally unprepared to handle. Traditional relational database management systems, which had served as the reliable backbone of corporate data infrastructure for three decades, began to buckle under the weight of data volumes, velocities, and varieties that the digital economy was generating at an accelerating pace. Transactional databases designed for structured data in predefined schemas could not economically store petabytes of unstructured web logs, sensor readings, social media streams, and clickstream data that organizations were suddenly finding themselves responsible for managing. The tools that had served the enterprise world so dependably were reaching the edges of their architectural limitations precisely at the moment when data was becoming the most strategically valuable asset an organization could possess.<\/span><\/p>\n

This mismatch between data reality and available technology created the conditions for a fundamental rethinking of how large-scale data management should work. The answer that emerged from Google’s engineering teams, and that subsequently inspired the open-source community to build Apache Hadoop, was a distributed computing paradigm that stored and processed data across clusters of commodity hardware rather than expensive specialized machines. This approach promised to bring the economics of data storage and processing into alignment with the volumes that modern organizations needed to handle, democratizing large-scale analytics in ways that had previously been accessible only to organizations with the resources to invest in proprietary supercomputing infrastructure. Into this environment of technological transformation and enormous commercial opportunity, Cloudera arrived with a mission and a vision that would make it one of the most consequential companies in the history of enterprise data management.<\/span><\/p>\n

The Founding Vision That Positioned Cloudera as the Hadoop Industry’s Standard Bearer<\/b><\/h3>\n

Cloudera was founded in 2008 by a group of individuals whose collective credentials and institutional connections gave the company an immediate credibility that most enterprise software startups spend years trying to establish. Among the founders were engineers from Google, Yahoo, Facebook, and Oracle who had been at the center of the distributed computing revolution, combined with academic researchers whose theoretical contributions to the field had helped define its intellectual foundations. Doug Cutting, the co-creator of Hadoop itself, joined Cloudera as chief architect, a move that simultaneously gave the company direct stewardship of the core technology and sent a powerful signal to the market about the seriousness and depth of Cloudera’s technical ambitions. The founding team understood not only the technology but the organizational challenges that enterprises would face in adopting it, positioning the company to address both dimensions from the outset.<\/span><\/p>\n

The founding vision was built on the conviction that Hadoop represented a genuinely transformative technology platform with the potential to reshape how every large organization in the world managed and derived value from its data, but that realizing this potential required more than raw open-source software. Enterprise customers needed a hardened, tested, supported, and integrated distribution of Hadoop ecosystem components that they could deploy with confidence, along with the professional services, training, and expert guidance necessary to build internal capability. Cloudera would provide the enterprise-grade packaging, the tooling, the support, and the expertise that transformed Hadoop from a promising but complex open-source project into a production-ready data platform that corporate IT organizations could responsibly adopt and operate. This value proposition proved extraordinarily compelling to a market hungry for exactly what Cloudera was offering.<\/span><\/p>\n

Apache Hadoop Architecture and the Technical Foundation Cloudera Built Upon<\/b><\/h3>\n

Understanding Cloudera’s role in the Hadoop ecosystem requires a clear picture of the underlying technical architecture that Apache Hadoop provides and the ways in which that architecture addressed the data management challenges of the big data era. At the core of Hadoop sits the Hadoop Distributed File System, a storage layer designed to store very large files reliably across clusters of commodity servers by dividing data into blocks and replicating those blocks across multiple nodes in the cluster. This replication strategy ensures that the failure of individual machines, which in large commodity clusters is an expected and routine occurrence rather than an exceptional event, does not result in data loss and does not interrupt processing jobs that are in progress. The architecture assumes hardware failure rather than treating it as an edge case, a philosophically different approach from the high-availability strategies employed by traditional enterprise storage systems.<\/span><\/p>\n

The MapReduce programming model that originally accompanied the Hadoop Distributed File System provided a framework for processing the data stored within it by moving computation to the data rather than moving data to computation, a principle that proved enormously efficient at scale by minimizing the network transfers that would otherwise represent a significant performance bottleneck in large distributed processing jobs. Subsequent development of the Hadoop ecosystem added YARN as a more flexible resource management layer, Hive for SQL-like query interfaces over Hadoop data, HBase for low-latency random access to Hadoop-stored data, Pig for data flow programming, Spark for in-memory processing that dramatically accelerated iterative analytics, and dozens of other components that together formed a rich and powerful technology stack. Cloudera’s particular contribution was to take this sprawling ecosystem of interdependent components, test their interactions exhaustively, resolve compatibility issues, provide coherent versioning, and deliver the whole as an integrated platform that enterprises could trust.<\/span><\/p>\n

Cloudera Distribution Including Apache Hadoop and the Enterprise Platform Strategy<\/b><\/h3>\n

The Cloudera Distribution Including Apache Hadoop, universally known in the industry as CDH, represented Cloudera’s flagship product offering for the first decade of the company’s existence and the primary vehicle through which it delivered enterprise-grade Hadoop capabilities to its growing customer base. CDH was not merely a collection of open-source Hadoop ecosystem components assembled for convenient download but a carefully engineered integration of tested, compatible, and hardened versions of each component in the stack, packaged with installation tooling, configuration management, security integrations, and monitoring capabilities that made the difference between a technology experiment and a production-ready enterprise platform. The engineering investment required to maintain CDH across the rapid evolution of the Hadoop ecosystem was substantial, as new versions of Hadoop, Spark, Hive, and dozens of other components were released continuously by their respective open-source communities, each requiring testing and validation within the CDH integration context.<\/span><\/p>\n

Alongside CDH, Cloudera developed Cloudera Manager, a management and monitoring platform that addressed one of the most significant operational challenges associated with running Hadoop clusters in enterprise environments. Managing a Hadoop cluster without adequate tooling required deep expertise and was prone to configuration errors that could compromise performance, reliability, or security. Cloudera Manager provided a graphical interface for deploying and configuring cluster components, monitoring the health and performance of the cluster in real time, managing rolling upgrades of software components, and diagnosing problems when they arose. The combination of CDH and Cloudera Manager created a user experience that was meaningfully more accessible and operationally manageable than working with raw upstream Hadoop components directly, justifying the commercial relationship that Cloudera offered enterprise customers alongside the subscription support services that provided the primary revenue stream for the business.<\/span><\/p>\n

Security and Governance Capabilities That Made Cloudera Enterprise-Ready<\/b><\/h3>\n

One of the most significant barriers to enterprise adoption of early Hadoop distributions was the platform’s limited native security and governance capabilities. The open-source Hadoop community had initially focused its engineering energy on storage, processing performance, and ecosystem expansion, while security was treated as a secondary concern that could be addressed through network-level controls and organizational access restrictions. This approach was acceptable for the academic and internet-scale web company environments where Hadoop had its origins, but it was entirely incompatible with the regulatory requirements, compliance obligations, and risk management standards that governed data handling in financial services, healthcare, insurance, telecommunications, and government sectors where much of the most compelling enterprise Hadoop opportunity resided.<\/span><\/p>\n

Cloudera responded to this challenge through a combination of internal engineering investment and strategic acquisitions that progressively transformed the security and governance profile of the CDH platform. The development and open-sourcing of Apache Sentry, a role-based access control framework for Hadoop ecosystem components, provided a foundation for fine-grained authorization that enterprises required to implement appropriate data access restrictions. Apache Atlas, which became the standard metadata management and data governance framework for the Hadoop ecosystem, emerged in part through Cloudera’s engineering contributions and provided the data lineage tracking, classification, and audit capabilities that compliance-conscious organizations needed. Apache Ranger, developed initially by a Hortonworks-backed community effort, was subsequently incorporated into Cloudera’s platform following the merger of the two companies, providing comprehensive security policy management across the full Hadoop ecosystem component stack.<\/span><\/p>\n

The Pivotal Merger With Hortonworks and Its Industry-Reshaping Consequences<\/b><\/h3>\n

The announcement in late 2018 that Cloudera and Hortonworks, the two largest commercial Hadoop companies and longtime competitors, would merge in an all-stock transaction valued at approximately 5.2 billion dollars represented one of the most consequential events in the history of the enterprise data management industry. The merger brought together the two organizations that had done more than any others to commercialize and advance the Hadoop ecosystem, combining Cloudera’s strong enterprise sales relationships, extensive professional services capabilities, and technical leadership in certain platform areas with Hortonworks’s complementary strengths including its stewardship of the Hortonworks Data Platform, its deep engagement with the open-source Apache community, and its relationships with a distinct set of enterprise customers and technology partners.<\/span><\/p>\n

The business rationale for the merger was clear in the context of an evolving competitive landscape where both companies faced intensifying pressure from cloud hyperscalers who were offering managed Hadoop and Spark services that challenged the on-premises distribution business model that had sustained both organizations. Neither Cloudera nor Hortonworks alone had the scale, resources, and comprehensive platform vision needed to compete effectively against Amazon Web Services, Microsoft Azure, and Google Cloud Platform as enterprise data workloads increasingly migrated toward cloud-managed services. Together, the merged entity would have the engineering talent, customer base, partner ecosystem, and financial resources to pursue a next-generation platform strategy centered on hybrid and multi-cloud data management that neither company could have executed independently. The integration of the two organizations and their respective technology platforms was a complex undertaking that ultimately produced the Cloudera Data Platform, a unified offering that represented a meaningful evolution beyond what either company had delivered independently.<\/span><\/p>\n

Cloudera Data Platform and the Strategic Pivot Toward Hybrid Multi-Cloud Architecture<\/b><\/h3>\n

The Cloudera Data Platform, introduced following the Hortonworks merger, represented a fundamental strategic repositioning of Cloudera’s product offering in response to the realities of an enterprise technology landscape increasingly characterized by hybrid infrastructure combining on-premises systems with one or more public cloud environments. Rather than offering separate on-premises and cloud products with limited integration between them, the Cloudera Data Platform was designed from the ground up to provide a consistent experience, a unified security and governance model, and workload portability across private data centers and major public cloud providers including Amazon Web Services, Microsoft Azure, and Google Cloud Platform. This architectural vision addressed the reality that most large enterprises were not moving entirely to the cloud but rather managing increasingly complex hybrid environments where data and workloads needed to flow intelligently between different infrastructure tiers.<\/span><\/p>\n

The Cloudera Data Platform introduced the concept of Shared Data Experience, which provided the consistent metadata management, security policies, and data governance controls necessary to treat data as a unified organizational asset regardless of where it was physically stored or processed. This capability addressed one of the most significant pain points of multi-cloud and hybrid environments, where data silos and inconsistent governance frameworks made it difficult to understand what data existed, where it was located, who had access to it, and how it was being used across the full scope of organizational data infrastructure. The platform’s component services, including Cloudera Data Warehouse, Cloudera Machine Learning, Cloudera DataFlow, and Cloudera Data Engineering, provided purpose-built environments for different analytical and data processing workloads while sharing the common governance, security, and metadata foundation that made the overall platform coherent and manageable.<\/span><\/p>\n

Cloudera’s Contribution to the Apache Open Source Ecosystem<\/b><\/h3>\n

Cloudera’s relationship with the Apache Software Foundation and the broader open-source community has been one of the defining characteristics of the company’s identity and one of the most significant aspects of its contribution to the technology industry beyond its direct commercial activities. From its earliest days, Cloudera positioned itself as a committed participant in and contributor to the open-source ecosystem upon which its products were built, recognizing that the strength of upstream Apache projects was directly connected to the quality and competitiveness of its own commercial offerings. This alignment of commercial interest with open-source contribution created incentives for sustained engineering investment in community projects that benefited the entire ecosystem of organizations and individuals working with Hadoop and related technologies.<\/span><\/p>\n

Cloudera engineers have held committer and project management committee roles across dozens of Apache projects over the company’s history, contributing code, documentation, testing, and community governance that have shaped the direction of technologies including Apache Spark, Apache Impala, Apache Kudu, Apache Hive, Apache Kafka, Apache Sentry, and many others. Apache Impala, an in-memory SQL query engine optimized for interactive analytics on Hadoop-stored data, was originally developed within Cloudera before being contributed to the Apache Software Foundation, where it has continued to evolve as a community project. This pattern of internal development followed by open-source contribution reflects a philosophy that views commercial leadership and open-source engagement not as competing priorities but as mutually reinforcing dimensions of a sustainable technology business strategy. The open-source contributions Cloudera has made represent a lasting legacy that will continue to benefit the data management community regardless of how the commercial landscape evolves.<\/span><\/p>\n

Machine Learning and Artificial Intelligence Integration Within the Cloudera Ecosystem<\/b><\/h3>\n

As machine learning and artificial intelligence transitioned from specialized research activities to mainstream enterprise priorities, Cloudera recognized the necessity of integrating these capabilities into its data platform in ways that allowed organizations to apply machine learning directly to the data assets managed within their Cloudera environments. The acquisition of Cloudera Fast Forward Labs in 2017 brought world-class machine learning research capability into the organization and established a credible intellectual foundation for Cloudera’s AI strategy. Fast Forward Labs had built a reputation for applied machine learning research that focused on emerging techniques with practical organizational applications, producing research reports and prototype implementations that helped enterprise clients understand and evaluate new developments in the field.<\/span><\/p>\n

The Cloudera Machine Learning service, built on the Cloudera Data Platform foundation, provides data scientists with a governed, collaborative workspace for developing, training, and deploying machine learning models using familiar open-source tools including Python, R, TensorFlow, PyTorch, and scikit-learn. The integration with the broader Cloudera Data Platform means that data scientists can access the full scope of organizational data assets subject to the same security and governance controls that apply across all other platform workloads, eliminating the data access challenges that frequently slow machine learning initiatives in organizations where data is fragmented across multiple systems with inconsistent access controls. The ability to deploy trained models as production services within the same platform environment where they were developed reduces the friction of the model operationalization step that has historically been a significant bottleneck in the translation of data science work into tangible organizational value.<\/span><\/p>\n

Customer Success Stories Demonstrating Real-World Impact Across Industries<\/b><\/h3>\n

Cloudera’s impact on the enterprise technology landscape is most concretely demonstrated through the outcomes achieved by the organizations that have deployed its platform to address genuine business challenges across diverse industries. Financial services organizations have used Cloudera’s platform to build fraud detection systems capable of analyzing transaction patterns in real time across billions of events, identifying suspicious activity with accuracy and speed that previous generation systems could not approach. The combination of Hadoop’s ability to store and process massive historical transaction datasets with streaming analytics capabilities for real-time detection has enabled banks and payment processors to reduce fraud losses while simultaneously reducing the false positive rates that generate customer friction and operational costs when legitimate transactions are incorrectly flagged.<\/span><\/p>\n

Healthcare organizations have deployed Cloudera’s platform to integrate and analyze clinical data across patient populations at scales that enable population health management, clinical trial optimization, and the development of predictive models that identify patients at elevated risk of specific conditions before those conditions become acute and expensive to treat. Telecommunications companies have used the platform to analyze network performance data at scales reflecting the enormous volume of events generated by modern communications infrastructure, enabling more proactive network management, more sophisticated customer experience monitoring, and more effective capacity planning. Retail organizations have built comprehensive customer analytics capabilities on Cloudera’s platform, integrating transaction history, behavioral data, and external information sources to develop personalization systems and demand forecasting models that improve both customer experience and inventory efficiency. These applications across industries reflect the genuine breadth of the business problems that a powerful, governed, and integrated data management platform enables organizations to address.<\/span><\/p>\n

Cloudera’s Educational and Certification Programs Building Industry Expertise<\/b><\/h3>\n

Recognizing that the widespread adoption of its platform depended not only on product quality but on the availability of skilled professionals capable of deploying, managing, and developing on that platform effectively, Cloudera invested substantially in building an educational ecosystem that has trained hundreds of thousands of data professionals globally. Cloudera University offered instructor-led training courses covering every dimension of working with the Cloudera platform, from Hadoop administration and Spark development to data engineering, data science, and platform security. These courses were designed for different audience personas including developers who needed to write applications against Cloudera’s data infrastructure, administrators responsible for deploying and operating clusters, and data analysts seeking to leverage the platform’s analytical capabilities for business insight.<\/span><\/p>\n

The Cloudera certification program provided formal credential validation for professionals who completed training and demonstrated proficiency through rigorous examination. Cloudera Certified Associate and Cloudera Certified Professional credentials in tracks covering data analyst, Spark developer, administrator, and HBase developer roles became recognized industry qualifications that employers used to evaluate candidates for Hadoop-related positions. The professional credibility associated with these certifications reflected the genuine rigor of the examination process and the depth of knowledge required to pass, distinguishing them from certifications in other technology domains where examination processes are sometimes criticized for being insufficiently demanding to serve as meaningful capability signals. Cloudera’s educational investments created a network effect that benefited the entire ecosystem by expanding the pool of qualified professionals available to organizations seeking to build Hadoop expertise, while simultaneously reinforcing the platform’s market position by training professionals who would naturally advocate for the technologies they had invested in learning.<\/span><\/p>\n

Competitive Landscape and Cloudera’s Strategic Positioning Among Data Platform Rivals<\/b><\/h3>\n

Cloudera has navigated a competitive landscape that has grown increasingly complex and demanding over the course of its history, facing challenges from multiple directions simultaneously that have required continuous strategic adaptation. The rise of cloud-native data warehouse services, exemplified by Snowflake’s meteoric growth, demonstrated that many enterprise analytics workloads could be served by fully managed SQL-oriented services that eliminated much of the operational complexity associated with running Hadoop clusters. Amazon Redshift, Google BigQuery, and Azure Synapse Analytics represented further competition from cloud hyperscalers who could offer data warehousing capabilities tightly integrated with their broader cloud ecosystems and priced to make standalone data platform vendors’ economics challenging.<\/span><\/p>\n

Databricks, founded by the original creators of Apache Spark and built around a lakehouse architecture that combines data lake storage economics with data warehouse query performance, emerged as a particularly formidable competitor that appealed strongly to data engineering and machine learning professionals who appreciated its developer-centric platform design and its tight integration with the open-source Spark ecosystem. Cloudera’s response to this competitive pressure has centered on differentiating through its hybrid and multi-cloud capabilities, its comprehensive security and governance framework, and its ability to serve organizations with complex regulatory requirements and data residency constraints that fully cloud-managed services may not be able to accommodate. The company’s positioning as the enterprise platform for organizations that need to manage data across diverse infrastructure environments, rather than those seeking to migrate entirely to any single cloud provider’s managed services, represents a coherent strategic identity that addresses genuine market needs even as the overall competitive intensity continues to increase.<\/span><\/p>\n

The Future Trajectory of Cloudera in an Artificial Intelligence-Defined Data Landscape<\/b><\/h3>\n

Cloudera’s path forward unfolds against a backdrop of accelerating artificial intelligence adoption that is reshaping every aspect of the enterprise technology landscape, including the data management domain where Cloudera has built its reputation and business. The training and operation of large language models and other foundation AI systems requires data infrastructure capable of managing datasets of unprecedented scale with the governance controls necessary to ensure that training data meets quality, licensing, and regulatory requirements. Cloudera’s platform capabilities in data integration, metadata management, lineage tracking, and access control position it as a potential infrastructure foundation for enterprise AI initiatives where data governance is not an optional consideration but a legal and ethical imperative.<\/span><\/p>\n

The company has articulated a vision for an AI-powered data platform that incorporates machine learning capabilities not only as workloads that run on top of the data infrastructure but as intelligence embedded within the platform itself, automating aspects of data management, anomaly detection, performance optimization, and security monitoring that currently require significant human attention and expertise. Integration with large language model interfaces that allow data practitioners to interact with the platform using natural language rather than requiring deep expertise in specific query languages or command-line tools represents another dimension of the AI integration roadmap. Cloudera’s continued relevance in the evolving data landscape will depend on its ability to execute on this vision while maintaining the trust of enterprise customers who have built critical data infrastructure on its platform and who expect the reliability, security, and continuity of support that have historically distinguished Cloudera’s enterprise offering from less mature alternatives. The company that pioneered enterprise Hadoop now faces the challenge and opportunity of pioneering the next generation of enterprise data management for an era defined by artificial intelligence, hybrid infrastructure, and data governance requirements of unprecedented complexity and consequence.<\/span><\/p>\n

Conclusion<\/b><\/h3>\n

Reflecting on the full arc of Cloudera’s history and its contributions to the enterprise technology world, it becomes clear that the company’s significance extends considerably beyond its commercial achievements to encompass a broader and more lasting impact on how the industry thinks about and practices large-scale data management. Cloudera did not merely commercialize Hadoop; it helped transform the entire conversation about enterprise data from a narrow technical concern about database administration into a strategic organizational priority about deriving competitive value from data assets at scale. The company’s advocacy for data-driven decision-making, its investment in educating thousands of practitioners, its contributions to open-source technology that outlast any specific product cycle, and its demonstration that Hadoop could meet enterprise standards for security, reliability, and governance collectively advanced the maturity of the data management industry in ways that benefited organizations and professionals far beyond Cloudera’s direct customer base.<\/span><\/p>\n

The challenges Cloudera has faced, including the competitive disruption from cloud-native services, the difficulties of integrating two large organizations following the Hortonworks merger, and the pressure of operating as a public company in a market that rewarded cloud-native pure plays with premium valuations, have been substantial and consequential. Yet these challenges do not diminish the genuine pioneering contribution that Cloudera made during the formative years of the big data era, when the tools, practices, and organizational capabilities for managing data at scale were being invented largely from scratch. The professionals trained through Cloudera University, the open-source contributions embedded in Apache projects used globally, the enterprise architectures designed around CDH and the Cloudera Data Platform, and the organizational data strategies shaped by Cloudera’s thought leadership represent a legacy of genuine and lasting value. As the data management landscape continues its rapid evolution toward artificial intelligence integration, real-time analytics, and ubiquitous hybrid cloud deployment, the foundational work that Cloudera and its community accomplished in making large-scale data management accessible, governable, and operationally reliable for the enterprise market will continue to be recognized as one of the essential contributions of the first great era of big data technology.<\/span><\/p>\n

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The first decade of the twenty-first century brought with it a data explosion of proportions that existing enterprise technology architectures were fundamentally unprepared to handle. Traditional relational database management systems, which had served as the reliable backbone of corporate data infrastructure for three decades, began to buckle under the weight of data volumes, velocities, and varieties that the digital economy was generating at an accelerating pace. Transactional databases designed for structured data in predefined schemas could not economically store petabytes of unstructured web […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[1018,1021],"tags":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.certbolt.com\/certification\/wp-json\/wp\/v2\/posts\/4964"}],"collection":[{"href":"https:\/\/www.certbolt.com\/certification\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.certbolt.com\/certification\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.certbolt.com\/certification\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.certbolt.com\/certification\/wp-json\/wp\/v2\/comments?post=4964"}],"version-history":[{"count":4,"href":"https:\/\/www.certbolt.com\/certification\/wp-json\/wp\/v2\/posts\/4964\/revisions"}],"predecessor-version":[{"id":10401,"href":"https:\/\/www.certbolt.com\/certification\/wp-json\/wp\/v2\/posts\/4964\/revisions\/10401"}],"wp:attachment":[{"href":"https:\/\/www.certbolt.com\/certification\/wp-json\/wp\/v2\/media?parent=4964"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.certbolt.com\/certification\/wp-json\/wp\/v2\/categories?post=4964"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.certbolt.com\/certification\/wp-json\/wp\/v2\/tags?post=4964"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}