{"id":8547,"date":"2026-03-26T07:25:31","date_gmt":"2026-03-26T07:25:31","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/metabase\/"},"modified":"2026-03-26T07:25:31","modified_gmt":"2026-03-26T07:25:31","slug":"metabase","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/metabase\/","title":{"rendered":"Metabase: What It Is, Key Features, Benefits, Use Cases, and How It Fits in CDP & Data Infrastructure"},"content":{"rendered":"\n

Metabase is a business intelligence (BI) and analytics platform that helps teams ask questions of their data, build dashboards, and share insights without requiring every stakeholder to write SQL. In Marketing Operations & Data<\/strong>, it often becomes the \u201clast-mile\u201d layer that makes performance reporting, funnel analysis, and audience insights accessible to marketers, analysts, and leaders.<\/p>\n\n\n\n

As organizations invest in CDP & Data Infrastructure<\/strong>\u2014data warehouses\/lakes, customer data platforms, event pipelines, identity resolution, and governance\u2014Metabase helps translate those foundational datasets into decisions. It matters because modern marketing runs on fast feedback loops: campaign performance, creative learnings, pipeline impact, retention signals, and channel efficiency all depend on trustworthy, timely reporting. Metabase is one way to operationalize that reporting at scale.<\/p>\n\n\n\n

What Is Metabase?<\/h2>\n\n\n\n

Metabase is a platform for exploring data, creating visualizations, and publishing dashboards that support self-serve analytics. At a beginner level, think of it as a way to:<\/p>\n\n\n\n

    \n
  • Connect to a database or warehouse<\/li>\n
  • Ask questions using a visual query builder or SQL<\/li>\n
  • Turn results into charts and dashboards<\/li>\n
  • Share recurring insights via scheduled updates or alerts<\/li>\n<\/ul>\n\n\n\n

    The core concept is accessibility: Metabase lowers the barrier between raw tables and usable answers. For the business, that means fewer \u201cwhat happened?\u201d bottlenecks and more consistent \u201cwhy did it happen?\u201d conversations.<\/p>\n\n\n\n

    In Marketing Operations & Data<\/strong>, Metabase typically sits between technical data sources (warehouse, product analytics events, CRM exports) and business consumption (weekly reports, leadership dashboards, campaign readouts). Within CDP & Data Infrastructure<\/strong>, it is not the CDP itself; it is the analytics interface that helps teams interpret data produced by the CDP, the warehouse, and associated transformation models.<\/p>\n\n\n\n

    Why Metabase Matters in Marketing Operations & Data<\/h2>\n\n\n\n

    Metabase is valuable when marketing teams need dependable, repeatable answers\u2014especially when data volume and channel complexity outgrow spreadsheets.<\/p>\n\n\n\n

    Key reasons it matters to Marketing Operations & Data<\/strong> include:<\/p>\n\n\n\n

      \n
    • Faster decision cycles:<\/strong> Dashboards and saved questions reduce time-to-insight for campaign pacing, budget shifts, and creative iteration.<\/li>\n
    • Self-serve analytics without chaos:<\/strong> Marketers can explore curated datasets while analysts maintain governance and definitions.<\/li>\n
    • Cross-functional alignment:<\/strong> Sales, finance, product, and marketing can reference the same metrics, reducing debate over \u201cwhose numbers are right.\u201d<\/li>\n
    • Better use of CDP investments:<\/strong> CDP & Data Infrastructure<\/strong> can be expensive; Metabase helps ensure the organization actually uses that data to improve outcomes.<\/li>\n
    • Competitive advantage through responsiveness:<\/strong> When you can detect drops in lead quality or changes in conversion rate early, you can respond before competitors do.<\/li>\n<\/ul>\n\n\n\n

      In practice, Metabase can move a team from reactive reporting to proactive monitoring\u2014especially when paired with clear metric definitions and a reliable data pipeline.<\/p>\n\n\n\n

      How Metabase Works<\/h2>\n\n\n\n

      Metabase is straightforward operationally, but its impact depends on how well it\u2019s integrated into Marketing Operations & Data<\/strong> workflows and CDP & Data Infrastructure<\/strong> standards.<\/p>\n\n\n\n

        \n
      1. \n

        Input (data sources and definitions)<\/strong>\n Metabase connects to databases and warehouses that contain marketing, product, and revenue data. Ideally, those sources include cleaned, modeled tables (for example, standardized campaign performance tables, unified customer tables, or lead-to-revenue models) coming from your CDP & Data Infrastructure<\/strong>.<\/p>\n<\/li>\n

      2. \n

        Analysis (questions and exploration)<\/strong>\n Users create \u201cquestions\u201d using a visual query editor or SQL. They can filter by date ranges, channels, segments, and other dimensions. Teams can also define reusable models and metadata (field naming, descriptions, formatting) to guide non-technical users.<\/p>\n<\/li>\n

      3. \n

        Execution (sharing and operationalization)<\/strong>\n Questions become charts, dashboards, and recurring reporting assets. Some teams embed dashboards into internal portals or deliver scheduled summaries to stakeholders. This is where Marketing Operations & Data<\/strong> teams often standardize reporting cadences and templates.<\/p>\n<\/li>\n

      4. \n

        Output (decisions and iteration)<\/strong>\n The output is not just a dashboard\u2014it\u2019s a set of decisions: reallocating spend, fixing tracking, prioritizing lifecycle journeys, adjusting targeting, or diagnosing funnel friction. When Metabase is connected to well-governed CDP & Data Infrastructure<\/strong>, these decisions are more reliable and repeatable.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

        Key Components of Metabase<\/h2>\n\n\n\n

        Metabase is easiest to adopt when you understand its main building blocks and how they map to Marketing Operations & Data<\/strong> responsibilities.<\/p>\n\n\n\n

          \n
        • Data connections:<\/strong> Secure connectors to warehouses and databases where marketing and customer data lives.<\/li>\n
        • Semantic cues and metadata:<\/strong> Field names, descriptions, data types, and formatting that prevent misinterpretation (for example, clarifying whether \u201crevenue\u201d is gross, net, or recognized).<\/li>\n
        • Models \/ curated datasets:<\/strong> Cleaned tables or defined entities that act as the \u201capproved\u201d sources for dashboards (often created by data teams as part of CDP & Data Infrastructure<\/strong>).<\/li>\n
        • Questions (saved queries):<\/strong> Reusable analyses such as CAC by channel, MQL-to-SQL conversion, cohort retention, or ROAS trends.<\/li>\n
        • Dashboards:<\/strong> Collections of visuals aligned to a purpose\u2014weekly performance, funnel health, lifecycle KPIs, or regional breakdowns.<\/li>\n
        • Permissions and governance:<\/strong> Role-based access to ensure sensitive customer data is protected while enabling self-serve exploration.<\/li>\n
        • Distribution and monitoring:<\/strong> Scheduled reports, alerts, and subscriptions to keep teams informed without manual pulls.<\/li>\n<\/ul>\n\n\n\n

          Types of Metabase<\/h2>\n\n\n\n

          Metabase doesn\u2019t have \u201ctypes\u201d in the way a marketing channel does, but there are meaningful distinctions in how teams deploy and use it in Marketing Operations & Data<\/strong> and CDP & Data Infrastructure<\/strong> contexts:<\/p>\n\n\n\n

          Deployment approaches<\/h3>\n\n\n\n
            \n
          • Self-hosted:<\/strong> Greater control over security, networking, and data residency; requires operational ownership.<\/li>\n
          • Managed\/cloud:<\/strong> Faster setup and fewer infrastructure tasks; still requires strong governance and data modeling.<\/li>\n<\/ul>\n\n\n\n

            Usage modes<\/h3>\n\n\n\n
              \n
            • No-code \/ visual exploration:<\/strong> Best for marketers and operators exploring curated data models.<\/li>\n
            • SQL-first analytics:<\/strong> Best for analysts and technically oriented marketers who need complex joins, attribution logic, or custom cohorting.<\/li>\n
            • Embedded analytics:<\/strong> Used when dashboards are placed inside internal tools (for example, a marketing portal) to bring data closer to workflows.<\/li>\n<\/ul>\n\n\n\n

              Data maturity contexts<\/h3>\n\n\n\n
                \n
              • Early-stage reporting:<\/strong> A few core dashboards connected to a single database.<\/li>\n
              • Scaled measurement:<\/strong> Multiple business units, standardized definitions, and a tight relationship to CDP & Data Infrastructure<\/strong> models.<\/li>\n<\/ul>\n\n\n\n

                Real-World Examples of Metabase<\/h2>\n\n\n\n

                1) Weekly growth and channel performance for an ecommerce brand<\/h3>\n\n\n\n

                A brand centralizes ad, email, and onsite behavior data in a warehouse (part of CDP & Data Infrastructure<\/strong>). Metabase dashboards show:<\/p>\n\n\n\n

                  \n
                • Spend, revenue, and contribution margin by channel<\/li>\n
                • New vs returning customer mix<\/li>\n
                • AOV trends and discount impact<\/li>\n<\/ul>\n\n\n\n

                  The Marketing Operations & Data<\/strong> team uses this to shift budget mid-week when ROAS drops, while leadership reviews a consistent weekly scorecard.<\/p>\n\n\n\n

                  2) Lead-to-revenue funnel reporting for B2B SaaS<\/h3>\n\n\n\n

                  A SaaS company models CRM leads, opportunities, and product activation events in the warehouse. Metabase provides:<\/p>\n\n\n\n

                    \n
                  • MQL \u2192 SQL \u2192 Opportunity \u2192 Closed-won conversion rates<\/li>\n
                  • Pipeline sourced by campaign and content<\/li>\n
                  • Time-to-convert by segment<\/li>\n<\/ul>\n\n\n\n

                    This ties top-of-funnel activity to revenue, making Marketing Operations & Data<\/strong> reporting more credible and aligning with finance\u2019s view of performance.<\/p>\n\n\n\n

                    3) Lifecycle retention monitoring using CDP event data<\/h3>\n\n\n\n

                    A company streams product events and customer attributes into its CDP & Data Infrastructure<\/strong>. Metabase is used to monitor:<\/p>\n\n\n\n

                      \n
                    • Trial-to-paid conversion by acquisition source<\/li>\n
                    • Retention cohorts by onboarding path<\/li>\n
                    • Feature adoption correlated with churn risk<\/li>\n<\/ul>\n\n\n\n

                      The marketing team uses insights to refine onboarding emails, retargeting audiences, and in-app messaging\u2014without waiting on ad hoc analyst pulls.<\/p>\n\n\n\n

                      Benefits of Using Metabase<\/h2>\n\n\n\n

                      When implemented with clear definitions and reliable data models, Metabase can deliver:<\/p>\n\n\n\n

                        \n
                      • Efficiency gains:<\/strong> Less time spent exporting CSVs, stitching spreadsheets, and rebuilding recurring reports.<\/li>\n
                      • Faster experimentation:<\/strong> Quicker readouts on A\/B tests, creative rotations, landing page changes, and lifecycle programs.<\/li>\n
                      • Lower reporting costs at scale:<\/strong> Reduced dependency on custom one-off reporting requests (while still enabling analyst-grade SQL when needed).<\/li>\n
                      • Improved stakeholder trust:<\/strong> Consistent dashboards and documented metrics reduce conflicting numbers in meetings.<\/li>\n
                      • Better customer experiences:<\/strong> Better segmentation and lifecycle insight can improve relevance, reduce wasted impressions, and support personalization\u2014especially when informed by CDP & Data Infrastructure<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                        Challenges of Metabase<\/h2>\n\n\n\n

                        Metabase is powerful, but it won\u2019t fix underlying measurement issues. Common challenges in Marketing Operations & Data<\/strong> include:<\/p>\n\n\n\n

                          \n
                        • Data quality and tracking gaps:<\/strong> If UTMs, events, or CRM hygiene are inconsistent, dashboards will amplify flawed inputs.<\/li>\n
                        • Metric definition drift:<\/strong> \u201cCAC,\u201d \u201cconversion,\u201d and \u201cactive user\u201d can mean different things across teams without governance.<\/li>\n
                        • Permissioning and privacy:<\/strong> Marketing dashboards can inadvertently expose PII or sensitive segments if access controls aren\u2019t designed well.<\/li>\n
                        • Performance and cost constraints:<\/strong> Poorly modeled tables or heavy queries can slow dashboards and increase warehouse costs.<\/li>\n
                        • Overconfidence in visuals:<\/strong> Dashboards can create false certainty if attribution logic, deduplication, or identity resolution in CDP & Data Infrastructure<\/strong> is incomplete.<\/li>\n<\/ul>\n\n\n\n

                          Best Practices for Metabase<\/h2>\n\n\n\n

                          To make Metabase a durable part of Marketing Operations & Data<\/strong>, focus on operational discipline as much as charts.<\/p>\n\n\n\n

                            \n
                          1. \n

                            Start with \u201cnorth-star\u201d dashboards, not endless charts<\/strong>\n Define a small set of KPIs (pipeline, revenue, retention, efficiency) and build dashboards that answer the most frequent decisions.<\/p>\n<\/li>\n

                          2. \n

                            Treat the warehouse model as the product<\/strong>\n Invest in clean, documented tables and consistent dimensions. Metabase shines when it sits on top of well-designed CDP & Data Infrastructure<\/strong> models.<\/p>\n<\/li>\n

                          3. \n

                            Document metrics inside the workflow<\/strong>\n Use field descriptions, dashboard text, and naming conventions so users understand definitions (and exclusions).<\/p>\n<\/li>\n

                          4. \n

                            Create curated datasets for marketers<\/strong>\n Don\u2019t point non-technical users at raw event tables. Provide \u201cready-to-use\u201d models like campaign_daily<\/code>, customer_360<\/code>, or funnel_stage_history<\/code>.<\/p>\n<\/li>\n

                          5. \n

                            Implement role-based access and privacy guardrails<\/strong>\n Restrict PII, limit row-level access where appropriate, and align with consent and retention policies.<\/p>\n<\/li>\n

                          6. \n

                            Operationalize monitoring<\/strong>\n Use alerts for anomalies (conversion drops, spend spikes, tracking outages) and review dashboards on a cadence.<\/p>\n<\/li>\n

                          7. \n

                            Establish an ownership model<\/strong>\n In Marketing Operations & Data<\/strong>, assign owners for metric definitions, data freshness SLAs, and dashboard maintenance.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                            Tools Used for Metabase<\/h2>\n\n\n\n

                            Metabase is a platform, but it relies on a broader ecosystem. In Marketing Operations & Data<\/strong> and CDP & Data Infrastructure<\/strong>, teams typically pair it with:<\/p>\n\n\n\n

                              \n
                            • Data warehouses\/lakes:<\/strong> Central storage for unified marketing, product, and revenue data.<\/li>\n
                            • ETL\/ELT and reverse ETL pipelines:<\/strong> To move data into the warehouse and, when needed, push segments or attributes back into operational tools.<\/li>\n
                            • Data transformation and modeling workflows:<\/strong> To create curated, reliable datasets and shared dimensions.<\/li>\n
                            • CRM systems:<\/strong> For lead stages, opportunity data, and revenue outcomes needed for pipeline reporting.<\/li>\n
                            • Marketing automation platforms:<\/strong> For lifecycle performance, email engagement, and journey analytics.<\/li>\n
                            • Ad platforms and analytics sources:<\/strong> For spend, impressions, clicks, and conversion signals that feed multi-channel reporting.<\/li>\n
                            • Data quality and governance tooling:<\/strong> For monitoring freshness, schema changes, and definition consistency.<\/li>\n<\/ul>\n\n\n\n

                              Metabase usually becomes the presentation and exploration layer on top of this stack\u2014making CDP & Data Infrastructure<\/strong> usable for day-to-day decisions.<\/p>\n\n\n\n

                              Metrics Related to Metabase<\/h2>\n\n\n\n

                              Metabase itself is not a metric, but it enables measurement of the metrics that matter in Marketing Operations & Data<\/strong>:<\/p>\n\n\n\n

                              Performance and growth metrics<\/h3>\n\n\n\n
                                \n
                              • Revenue, pipeline, and conversion rates by channel and campaign<\/li>\n
                              • ROAS, CAC, LTV, payback period<\/li>\n
                              • Funnel velocity (time between stages)<\/li>\n<\/ul>\n\n\n\n

                                Engagement and lifecycle metrics<\/h3>\n\n\n\n
                                  \n
                                • Activation rate, retention cohorts, churn rate<\/li>\n
                                • Repeat purchase rate, frequency, AOV<\/li>\n
                                • Email engagement, journey step conversion<\/li>\n<\/ul>\n\n\n\n

                                  Operational and data reliability metrics<\/h3>\n\n\n\n
                                    \n
                                  • Data freshness (lag between source and dashboard)<\/li>\n
                                  • Coverage (percentage of campaigns with valid UTMs, events, or mappings)<\/li>\n
                                  • Dashboard adoption (active viewers, recurring subscriptions)<\/li>\n
                                  • Query performance and warehouse cost drivers<\/li>\n<\/ul>\n\n\n\n

                                    Future Trends of Metabase<\/h2>\n\n\n\n

                                    Metabase adoption is evolving alongside broader changes in Marketing Operations & Data<\/strong>:<\/p>\n\n\n\n

                                      \n
                                    • AI-assisted analysis:<\/strong> More natural-language querying, automated chart suggestions, and anomaly explanations\u2014useful, but only as reliable as the underlying CDP & Data Infrastructure<\/strong>.<\/li>\n
                                    • Stronger metric governance:<\/strong> Teams are moving toward shared metric layers and stricter definitions to prevent KPI drift across dashboards.<\/li>\n
                                    • Privacy-driven measurement changes:<\/strong> As third-party signals decline, first-party data and modeled conversions increase in importance\u2014raising the value of internal analytics platforms like Metabase.<\/li>\n
                                    • Embedded decisioning:<\/strong> Dashboards increasingly live inside internal tools and workflows, reducing context switching for marketing teams.<\/li>\n
                                    • Near-real-time expectations:<\/strong> More teams want faster refresh cycles for campaign pacing, which pressures data pipelines, modeling, and cost management.<\/li>\n<\/ul>\n\n\n\n

                                      Metabase vs Related Terms<\/h2>\n\n\n\n

                                      Metabase vs a CDP<\/h3>\n\n\n\n

                                      A CDP collects, unifies, and activates customer data (identities, attributes, event streams). Metabase analyzes and visualizes data but doesn\u2019t replace identity resolution, audience building, or activation workflows. In mature stacks, Metabase sits downstream of CDP & Data Infrastructure<\/strong> to help evaluate results.<\/p>\n\n\n\n

                                      Metabase vs a data warehouse<\/h3>\n\n\n\n

                                      A warehouse stores and processes data; Metabase queries and visualizes it. Warehouses are foundational infrastructure, while Metabase is a consumption layer that supports Marketing Operations & Data<\/strong> reporting and exploration.<\/p>\n\n\n\n

                                      Metabase vs a metrics layer<\/h3>\n\n\n\n

                                      A metrics layer standardizes KPI definitions so every tool calculates \u201crevenue\u201d or \u201cactive user\u201d the same way. Metabase can document and curate models, but organizations with complex needs may still implement dedicated metric standardization as part of CDP & Data Infrastructure<\/strong>.<\/p>\n\n\n\n

                                      Who Should Learn Metabase<\/h2>\n\n\n\n

                                      Metabase is worth learning for anyone who needs reliable, repeatable answers from marketing and customer data:<\/p>\n\n\n\n

                                        \n
                                      • Marketers:<\/strong> To understand channel performance, lifecycle impact, and experiment results without waiting for custom reports.<\/li>\n
                                      • Analysts:<\/strong> To publish governed dashboards, reduce ad hoc requests, and scale trusted measurement in Marketing Operations & Data<\/strong>.<\/li>\n
                                      • Agencies:<\/strong> To standardize reporting across clients and communicate performance with clarity and transparency.<\/li>\n
                                      • Business owners and founders:<\/strong> To monitor growth drivers, unit economics, and pipeline health with fewer intermediaries.<\/li>\n
                                      • Developers and data engineers:<\/strong> To support secure access patterns, optimize performance, and integrate reporting into broader CDP & Data Infrastructure<\/strong> workflows.<\/li>\n<\/ul>\n\n\n\n

                                        Summary of Metabase<\/h2>\n\n\n\n

                                        Metabase is an analytics and BI platform used to explore data, build dashboards, and share insights. In Marketing Operations & Data<\/strong>, it helps teams operationalize performance measurement, reduce reporting friction, and speed up decision-making. It fits into CDP & Data Infrastructure<\/strong> as a downstream layer that turns unified customer and campaign data into accessible reporting\u2014provided the underlying data model, definitions, and governance are strong.<\/p>\n\n\n\n

                                        Frequently Asked Questions (FAQ)<\/h2>\n\n\n\n

                                        1) What is Metabase used for in marketing teams?<\/h3>\n\n\n\n

                                        Metabase is used for self-serve reporting and dashboards: channel performance, funnel conversion, cohort retention, lifecycle engagement, and revenue attribution outputs\u2014typically powered by a warehouse in your CDP & Data Infrastructure<\/strong>.<\/p>\n\n\n\n

                                        2) Is Metabase a CDP?<\/h3>\n\n\n\n

                                        No. Metabase is a BI\/analytics platform. A CDP focuses on collecting and unifying customer data and activating audiences. Metabase helps analyze and communicate results from that data, which is why it complements CDP & Data Infrastructure<\/strong> rather than replacing it.<\/p>\n\n\n\n

                                        3) Do you need SQL to use Metabase?<\/h3>\n\n\n\n

                                        Not always. Many analyses can be built with a visual query editor. SQL becomes important for advanced attribution logic, complex joins, or custom cohort definitions\u2014common needs in Marketing Operations & Data<\/strong>.<\/p>\n\n\n\n

                                        4) How does Metabase support CDP & Data Infrastructure investments?<\/h3>\n\n\n\n

                                        It increases adoption and usability of centralized data by turning modeled tables into dashboards, scheduled reports, and accessible exploration. That makes the outputs of CDP & Data Infrastructure<\/strong> actionable for non-technical stakeholders.<\/p>\n\n\n\n

                                        5) What data should you connect to Metabase first?<\/h3>\n\n\n\n

                                        Start with curated, high-confidence datasets: campaign spend and outcomes, CRM funnel stages, core web\/app events, and a unified customer table. Avoid exposing raw event streams to beginners until definitions are clear.<\/p>\n\n\n\n

                                        6) What are common mistakes when rolling out Metabase?<\/h3>\n\n\n\n

                                        Common issues include skipping metric definitions, giving broad access to sensitive tables, building dashboards on raw unmodeled data, and failing to assign ownership for maintenance\u2014each of which can undermine trust in Marketing Operations & Data<\/strong> reporting.<\/p>\n\n\n\n

                                        7) Can Metabase scale for enterprise reporting?<\/h3>\n\n\n\n

                                        It can, but scaling depends on governance, permissions, performance tuning, and strong upstream modeling in CDP & Data Infrastructure<\/strong>. Larger organizations typically formalize dashboard standards, access controls, and refresh SLAs to keep reporting reliable.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                        Metabase is a business intelligence (BI) and analytics platform that helps teams ask questions of their data, build dashboards, and share insights without requiring every stakeholder to write SQL. In **Marketing Operations & Data**, it often becomes the \u201clast-mile\u201d layer that makes performance reporting, funnel analysis, and audience insights accessible to marketers, analysts, and leaders.<\/p>\n","protected":false},"author":10235,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1898],"tags":[],"class_list":["post-8547","post","type-post","status-publish","format-standard","hentry","category-cdp-data-infrastructure"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/8547","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/users\/10235"}],"replies":[{"embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/comments?post=8547"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/8547\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=8547"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=8547"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=8547"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}