{"id":6817,"date":"2026-03-23T13:41:32","date_gmt":"2026-03-23T13:41:32","guid":{"rendered":"https:\/\/www.wizbrand.com\/tutorials\/bigquery-streaming-export\/"},"modified":"2026-03-23T13:41:32","modified_gmt":"2026-03-23T13:41:32","slug":"bigquery-streaming-export","status":"publish","type":"post","link":"https:\/\/www.wizbrand.com\/tutorials\/bigquery-streaming-export\/","title":{"rendered":"Bigquery Streaming Export: What It Is, Key Features, Benefits, Use Cases, and How It Fits in Analytics"},"content":{"rendered":"\n

Bigquery Streaming Export is a data integration approach used in Conversion & Measurement<\/strong> to move event-level marketing and product data into a queryable warehouse in near real time. Instead of waiting for a daily batch file or delayed reporting table, teams use Bigquery Streaming Export<\/strong> to make fresh interactions\u2014page views, sign-ups, purchases, app events, lead submissions\u2014available quickly for Analytics<\/strong>, attribution, experimentation, and operational decision-making.<\/p>\n\n\n\n

This matters because modern Conversion & Measurement<\/strong> is no longer limited to \u201cDid we get a conversion?\u201d Marketing teams need to understand when<\/em> and why<\/em> conversions happen, how users behave across devices and touchpoints, and how to react quickly when performance changes. Bigquery Streaming Export<\/strong> supports that shift by enabling faster, more flexible Analytics<\/strong> than many UI-based reporting tools can provide.<\/p>\n\n\n\n

What Is Bigquery Streaming Export?<\/h2>\n\n\n\n

Bigquery Streaming Export<\/strong> is the continuous (or near-real-time) export of event data from a source system\u2014commonly an analytics or measurement platform\u2014into BigQuery so the data can be queried, modeled, and joined with other business datasets.<\/p>\n\n\n\n

At its core, the concept is simple: rather than exporting data once per day (batch export), events are exported as they occur (streaming). That makes Bigquery Streaming Export especially valuable for time-sensitive Conversion & Measurement<\/strong> tasks like rapid campaign optimization, anomaly detection, and up-to-the-minute funnel monitoring.<\/p>\n\n\n\n

From a business perspective, Bigquery Streaming Export turns \u201cmeasurement data\u201d into a reusable asset. Instead of being locked inside a reporting interface, your event stream becomes first-party data you can combine with CRM records, ad cost data, inventory, customer support interactions, and product usage\u2014enabling deeper Analytics<\/strong> and more credible decision-making.<\/p>\n\n\n\n

In Conversion & Measurement<\/strong>, Bigquery Streaming Export typically sits between collection (tags\/SDKs\/server events) and downstream activation (dashboards, audiences, bidding logic, lifecycle messaging), providing a reliable analytics-ready layer.<\/p>\n\n\n\n

Why Bigquery Streaming Export Matters in Conversion & Measurement<\/h2>\n\n\n\n

Bigquery Streaming Export<\/strong> strengthens Conversion & Measurement<\/strong> strategy in four practical ways:<\/p>\n\n\n\n

    \n
  1. \n

    Faster feedback loops for marketing performance<\/strong>\n When conversion rates drop at noon, waiting until tomorrow to diagnose it is costly. Streaming data supports rapid triage and faster optimization.<\/p>\n<\/li>\n

  2. \n

    More accurate, explainable attribution and funnel analysis<\/strong>\n UI reports often aggregate, sample, or apply opinionated attribution rules. Streaming exports provide event-level data that can be modeled transparently for Analytics<\/strong>.<\/p>\n<\/li>\n

  3. \n

    Better cross-system measurement<\/strong>\n Conversions rarely live in one tool. Bigquery Streaming Export helps unify onsite events with backend outcomes (orders, subscriptions, qualified leads) so Conversion & Measurement<\/strong> reflects the business truth.<\/p>\n<\/li>\n

  4. \n

    Competitive advantage through operationalization<\/strong>\n Teams that can detect issues, segment users, and measure experiments faster tend to out-iterate competitors. Bigquery Streaming Export is often an enabling layer for that speed.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

    How Bigquery Streaming Export Works<\/h2>\n\n\n\n

    While implementations vary, Bigquery Streaming Export<\/strong> is best understood as a workflow:<\/p>\n\n\n\n

      \n
    1. \n

      Input \/ Trigger: events are generated<\/strong>\n User actions (e.g., product view, add-to-cart, form submit) are captured by client-side tags, mobile SDKs, or server-side event collectors. Each event includes timestamps and parameters (campaign, device, user identifiers, product metadata).<\/p>\n<\/li>\n

    2. \n

      Processing: events are validated and prepared<\/strong>\n The source system applies basic formatting and schema mapping so events can land in BigQuery tables. Some setups also include enrichment (e.g., adding geo, traffic source, consent state) before export.<\/p>\n<\/li>\n

    3. \n

      Execution: events are streamed into BigQuery<\/strong>\n Events are written continuously into one or more BigQuery tables\/partitions. Unlike batch pipelines, data becomes queryable quickly, which is the defining benefit of Bigquery Streaming Export<\/strong>.<\/p>\n<\/li>\n

    4. \n

      Output \/ Outcome: teams query and activate<\/strong>\n Analysts run SQL for Analytics<\/strong> (funnels, cohorts, attribution models). Data teams build transformations and curated datasets. Marketing teams consume dashboards and insights to improve Conversion & Measurement<\/strong> performance.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

      Key Components of Bigquery Streaming Export<\/h2>\n\n\n\n

      A strong Bigquery Streaming Export<\/strong> setup typically includes these elements:<\/p>\n\n\n\n

        \n
      • Event collection layer<\/strong>: tags, SDKs, or server-side collectors that define what is measured and how consistently events fire.<\/li>\n
      • Schema and event design<\/strong>: naming conventions, parameter definitions, required fields, and versioning so events remain usable over time.<\/li>\n
      • Identity and matching strategy<\/strong>: rules for user identifiers, session logic, and how to connect events to leads\/orders while respecting consent.<\/li>\n
      • BigQuery dataset structure<\/strong>: partitioning, clustering, and table design to keep Analytics<\/strong> queries fast and costs predictable.<\/li>\n
      • Data quality checks<\/strong>: validation rules for missing parameters, duplicate events, unexpected spikes, and schema drift.<\/li>\n
      • Governance and roles<\/strong>: clear ownership across marketing, analytics, data engineering, and privacy\/compliance for Conversion & Measurement<\/strong> integrity.<\/li>\n
      • Downstream transformations<\/strong>: standardized models (e.g., sessions, funnels, channel groupings) that turn raw exports into business-ready metrics.<\/li>\n<\/ul>\n\n\n\n

        Types of Bigquery Streaming Export<\/h2>\n\n\n\n

        \u201cTypes\u201d of Bigquery Streaming Export<\/strong> are usually best described as practical distinctions rather than formal categories:<\/p>\n\n\n\n

        Streaming vs. batch export<\/h3>\n\n\n\n
          \n
        • Streaming<\/strong>: events appear in BigQuery quickly, supporting near-real-time Analytics<\/strong> and monitoring for Conversion & Measurement<\/strong>.<\/li>\n
        • Batch<\/strong>: events arrive on a schedule (often daily). Cheaper and simpler, but slower for decision-making.<\/li>\n<\/ul>\n\n\n\n

          Raw event export vs. curated export<\/h3>\n\n\n\n
            \n
          • Raw export<\/strong>: granular events with many parameters; flexible but requires modeling.<\/li>\n
          • Curated export<\/strong>: transformed tables (sessions, conversions, attribution-ready datasets) built on top of streaming data to standardize reporting.<\/li>\n<\/ul>\n\n\n\n

            Client-side vs. server-side event sourcing<\/h3>\n\n\n\n
              \n
            • Client-side<\/strong>: captured in the browser\/app; faster to deploy but more exposed to blockers and inconsistent connectivity.<\/li>\n
            • Server-side<\/strong>: captured from backend systems; often more reliable for revenue and lead outcomes, strengthening Conversion & Measurement<\/strong> accuracy.<\/li>\n<\/ul>\n\n\n\n

              Real-World Examples of Bigquery Streaming Export<\/h2>\n\n\n\n

              Example 1: Ecommerce campaign monitoring during a flash sale<\/h3>\n\n\n\n

              A retailer runs a limited-time promotion and needs immediate visibility into checkout errors and conversion drops. With Bigquery Streaming Export<\/strong>, the team monitors funnel steps (view \u2192 add-to-cart \u2192 checkout \u2192 purchase) in near real time. If a payment method fails, they can quantify impact quickly, alert engineering, and adjust spend before wasted budget accumulates\u2014an actionable Conversion & Measurement<\/strong> win powered by timely Analytics<\/strong>.<\/p>\n\n\n\n

              Example 2: Lead quality measurement for B2B paid media<\/h3>\n\n\n\n

              A B2B company measures form submissions as conversions, but the real KPI is qualified pipeline. Streaming exports bring web events into BigQuery, where they\u2019re joined with CRM outcomes (SQL, opportunity, revenue). The team builds a model of \u201cexpected pipeline value by campaign\u201d and updates it frequently. Bigquery Streaming Export<\/strong> enables fast iteration on targeting and landing pages while keeping Conversion & Measurement<\/strong> tied to downstream business impact.<\/p>\n\n\n\n

              Example 3: App onboarding optimization with cohort tracking<\/h3>\n\n\n\n

              A subscription app streams onboarding events into BigQuery and tracks cohorts by acquisition source and app version. Analysts detect that a new release reduces activation for one device model. With Analytics<\/strong> based on streaming data, product and marketing teams respond quickly\u2014pausing spend for affected segments and prioritizing a hotfix. This is Conversion & Measurement<\/strong> beyond ads: it\u2019s full-funnel optimization.<\/p>\n\n\n\n

              Benefits of Using Bigquery Streaming Export<\/h2>\n\n\n\n

              Used well, Bigquery Streaming Export<\/strong> delivers measurable advantages:<\/p>\n\n\n\n

                \n
              • Fresher insights<\/strong>: faster detection of tracking breaks, UX issues, and campaign volatility improves Conversion & Measurement<\/strong> response time.<\/li>\n
              • More flexible Analytics<\/strong>: event-level SQL enables custom funnels, multi-touch analyses, and tailored segmentation that UI reports may not support.<\/li>\n
              • Reduced reporting bottlenecks<\/strong>: teams can self-serve analysis without waiting for platform exports or manual extracts.<\/li>\n
              • Better data unification<\/strong>: join marketing touchpoints with revenue, margin, refunds, and retention for more honest performance measurement.<\/li>\n
              • Improved customer experience<\/strong>: quicker detection of friction (e.g., slow pages, broken forms) reduces conversion loss and supports more relevant personalization.<\/li>\n<\/ul>\n\n\n\n

                Challenges of Bigquery Streaming Export<\/h2>\n\n\n\n

                Bigquery Streaming Export<\/strong> also introduces real constraints that teams should plan for:<\/p>\n\n\n\n

                  \n
                • Cost management<\/strong>: streaming ingestion and frequent querying can raise costs if partitioning and query discipline are weak.<\/li>\n
                • Data quality risk<\/strong>: near-real-time pipelines can propagate errors quickly\u2014bad event definitions or duplicates can distort Analytics<\/strong> and Conversion & Measurement<\/strong> decisions.<\/li>\n
                • Schema drift and versioning<\/strong>: changing event parameters without governance leads to broken dashboards and inconsistent reporting over time.<\/li>\n
                • Identity complexity<\/strong>: connecting anonymous events to known users (and doing so compliantly) is difficult but central to trustworthy measurement.<\/li>\n
                • Latency expectations<\/strong>: \u201cstreaming\u201d is often near-real-time, not instantaneous. Teams must set realistic SLAs and monitoring thresholds.<\/li>\n
                • Privacy and consent<\/strong>: exporting granular events increases responsibility for retention rules, access controls, and compliant processing.<\/li>\n<\/ul>\n\n\n\n

                  Best Practices for Bigquery Streaming Export<\/h2>\n\n\n\n

                  These practices help make Bigquery Streaming Export<\/strong> sustainable and credible for Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                    \n
                  1. \n

                    Define a measurement plan before exporting everything<\/strong>\n Choose events that map to business outcomes (leads, purchases, activation). Capture necessary parameters (campaign, content, product, value) with consistent naming.<\/p>\n<\/li>\n

                  2. \n

                    Use strong dataset design<\/strong>\n Partition tables by date\/time and consider clustering on common filters (e.g., event name, user identifier). This keeps Analytics<\/strong> queries efficient.<\/p>\n<\/li>\n

                  3. \n

                    Create a \u201ctrusted metrics\u201d layer<\/strong>\n Build curated tables for sessions, conversions, channel groupings, and deduped purchases. This reduces confusion and aligns stakeholders on Conversion & Measurement<\/strong> numbers.<\/p>\n<\/li>\n

                  4. \n

                    Implement data quality monitoring<\/strong>\n Track missing parameters, event volume anomalies, conversion rate shifts, and schema changes. Alert on sudden drops that may indicate tagging issues.<\/p>\n<\/li>\n

                  5. \n

                    Control access and document governance<\/strong>\n Limit access to sensitive identifiers, define retention windows, and document event schemas. Governance is part of high-integrity Analytics<\/strong>, not bureaucracy.<\/p>\n<\/li>\n

                  6. \n

                    Plan for backfills and reconciliation<\/strong>\n Even with Bigquery Streaming Export<\/strong>, you may need to backfill late events or reconcile with backend transaction systems to ensure financial accuracy.<\/p>\n<\/li>\n<\/ol>\n\n\n\n

                    Tools Used for Bigquery Streaming Export<\/h2>\n\n\n\n

                    Because Bigquery Streaming Export<\/strong> is a pattern spanning collection to analysis, teams typically use several tool categories:<\/p>\n\n\n\n

                      \n
                    • Analytics and measurement platforms<\/strong>: generate event data and define conversions; streaming export makes that event stream usable for deeper Analytics<\/strong>.<\/li>\n
                    • Tag management and event collection<\/strong>: manage client-side tags and server-side event routing to improve reliability in Conversion & Measurement<\/strong>.<\/li>\n
                    • ETL\/ELT and transformation workflows<\/strong>: schedule models, standardize metrics, and create curated datasets from raw streaming tables.<\/li>\n
                    • Data quality and observability<\/strong>: monitor volumes, freshness, schema drift, and duplicates so stakeholders trust the numbers.<\/li>\n
                    • Reporting dashboards and BI<\/strong>: visualize funnels, cohorts, and campaign performance using BigQuery as the source of truth.<\/li>\n
                    • CRM and marketing automation systems<\/strong>: provide lead\/customer outcomes that enrich streaming event data for closed-loop Conversion & Measurement<\/strong>.<\/li>\n
                    • Experimentation and personalization systems<\/strong>: consume streaming-backed insights to evaluate tests and target experiences.<\/li>\n<\/ul>\n\n\n\n

                      Metrics Related to Bigquery Streaming Export<\/h2>\n\n\n\n

                      To evaluate whether Bigquery Streaming Export<\/strong> is improving Conversion & Measurement<\/strong> and Analytics<\/strong>, track metrics across three layers:<\/p>\n\n\n\n

                      Pipeline health<\/h3>\n\n\n\n
                        \n
                      • Data freshness (latency)<\/strong>: time from event occurrence to queryable availability.<\/li>\n
                      • Ingestion success rate<\/strong>: proportion of events successfully exported.<\/li>\n
                      • Duplicate rate<\/strong>: percentage of duplicate conversions\/events.<\/li>\n
                      • Schema error rate<\/strong>: failures caused by unexpected fields or invalid types.<\/li>\n<\/ul>\n\n\n\n

                        Measurement quality<\/h3>\n\n\n\n
                          \n
                        • Event completeness<\/strong>: percent of key events containing required parameters (campaign, value, content, consent state).<\/li>\n
                        • Cross-system match rate<\/strong>: percent of orders\/leads that can be joined to event streams.<\/li>\n
                        • Reconciliation variance<\/strong>: difference between warehouse totals and backend financial\/CRM totals.<\/li>\n<\/ul>\n\n\n\n

                          Business outcomes<\/h3>\n\n\n\n
                            \n
                          • Conversion rate and cost per conversion<\/strong> (by channel\/campaign) using warehouse-modeled rules.<\/li>\n
                          • Revenue or pipeline per session\/user<\/strong> when joined to backend outcomes.<\/li>\n
                          • Time to detect and resolve measurement incidents<\/strong> (tracking breaks, checkout errors), a practical KPI enabled by streaming Analytics<\/strong>.<\/li>\n<\/ul>\n\n\n\n

                            Future Trends of Bigquery Streaming Export<\/h2>\n\n\n\n

                            Several trends are shaping how Bigquery Streaming Export<\/strong> evolves within Conversion & Measurement<\/strong>:<\/p>\n\n\n\n

                              \n
                            • AI-assisted analysis and anomaly detection<\/strong>: near-real-time exports support automated alerts, root-cause hints, and forecasting based on fresh data.<\/li>\n
                            • More server-side measurement<\/strong>: as client-side tracking becomes less reliable, streaming pipelines increasingly ingest server events to preserve Analytics<\/strong> accuracy.<\/li>\n
                            • Privacy-driven modeling<\/strong>: expect more emphasis on consent-aware data handling, aggregation, and modeled conversions where direct identifiers are limited.<\/li>\n
                            • Operational Analytics<\/strong>: streaming exports will be used not only for reporting but also for operational triggers\u2014fraud checks, journey orchestration, and near-real-time experimentation readouts.<\/li>\n
                            • Standardized metric layers<\/strong>: organizations will invest more in shared definitions (semantic layers) so Conversion & Measurement<\/strong> metrics remain consistent across teams and dashboards.<\/li>\n<\/ul>\n\n\n\n

                              Bigquery Streaming Export vs Related Terms<\/h2>\n\n\n\n

                              Bigquery Streaming Export vs batch export<\/h3>\n\n\n\n

                              Batch export is delivered on a schedule and is often sufficient for weekly reporting. Bigquery Streaming Export<\/strong> is designed for speed\u2014supporting rapid Analytics<\/strong> and faster reactions in Conversion & Measurement<\/strong> when timing matters.<\/p>\n\n\n\n

                              Bigquery Streaming Export vs data replication<\/h3>\n\n\n\n

                              Data replication typically refers to copying databases (e.g., transactional systems) into a warehouse. Bigquery Streaming Export<\/strong> usually refers to exporting event<\/em> data from measurement systems. Both can coexist: replication provides \u201cground truth\u201d orders, while streaming export provides behavioral context.<\/p>\n\n\n\n

                              Bigquery Streaming Export vs server-side tracking<\/h3>\n\n\n\n

                              Server-side tracking is a collection method (how events are captured). Bigquery Streaming Export<\/strong> is an export method (how events are delivered into BigQuery). Many mature Conversion & Measurement<\/strong> stacks use server-side tracking to improve reliability and then rely on streaming export for fast Analytics<\/strong>.<\/p>\n\n\n\n

                              Who Should Learn Bigquery Streaming Export<\/h2>\n\n\n\n
                                \n
                              • Marketers<\/strong> benefit by getting faster, clearer feedback on campaign performance and conversion drivers, especially when platform UI reports are limited.<\/li>\n
                              • Analysts<\/strong> gain event-level access for advanced Analytics<\/strong>, attribution modeling, cohort analysis, and data quality validation.<\/li>\n
                              • Agencies<\/strong> can deliver more credible Conversion & Measurement<\/strong> for clients by building repeatable pipelines and standardized reporting layers.<\/li>\n
                              • Business owners and founders<\/strong> get closer to real unit economics by connecting marketing activity to revenue, refunds, churn, and LTV.<\/li>\n
                              • Developers and data engineers<\/strong> are key to making Bigquery Streaming Export<\/strong> reliable, cost-efficient, and compliant through schema design and governance.<\/li>\n<\/ul>\n\n\n\n

                                Summary of Bigquery Streaming Export<\/h2>\n\n\n\n

                                Bigquery Streaming Export<\/strong> is the near-real-time export of event data into BigQuery so teams can run flexible, scalable Analytics<\/strong> beyond standard reporting interfaces. It matters because modern Conversion & Measurement<\/strong> requires faster insights, better cross-system reconciliation, and more transparent modeling of funnels and attribution. When designed with governance, data quality checks, and a trusted metrics layer, Bigquery Streaming Export<\/strong> becomes a durable foundation for accurate measurement and smarter marketing decisions.<\/p>\n\n\n\n

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

                                1) What is Bigquery Streaming Export used for?<\/h3>\n\n\n\n

                                Bigquery Streaming Export is used to make event data available in BigQuery quickly so teams can run near-real-time Analytics<\/strong>, monitor funnels, and improve Conversion & Measurement<\/strong> without waiting for daily batch updates.<\/p>\n\n\n\n

                                2) Is streaming export always real time?<\/h3>\n\n\n\n

                                Not always. \u201cStreaming\u201d typically means low latency, but there can still be delays due to processing, validation, or system load. Define acceptable freshness targets (SLAs) for your Conversion & Measurement<\/strong> use cases.<\/p>\n\n\n\n

                                3) How does Bigquery Streaming Export improve conversion tracking?<\/h3>\n\n\n\n

                                It improves conversion tracking by enabling faster detection of broken tags, duplicate conversions, and checkout issues, and by supporting deeper analysis (deduplication, joining to backend orders) that strengthens Conversion & Measurement<\/strong> accuracy.<\/p>\n\n\n\n

                                4) What should we monitor to trust the Analytics from streaming exports?<\/h3>\n\n\n\n

                                Monitor freshness\/latency, event volumes by type, duplicate rates, schema changes, and reconciliation against CRM or transaction systems. These controls make Analytics<\/strong> outputs credible for decision-making.<\/p>\n\n\n\n

                                5) Does Bigquery Streaming Export replace dashboards and reporting tools?<\/h3>\n\n\n\n

                                No. It usually feeds them. Bigquery Streaming Export<\/strong> provides the underlying data layer; dashboards visualize curated models built from that data for Conversion & Measurement<\/strong> stakeholders.<\/p>\n\n\n\n

                                6) What\u2019s the biggest mistake teams make with Bigquery Streaming Export?<\/h3>\n\n\n\n

                                Exporting everything without a measurement plan. That leads to bloated schemas, higher costs, and inconsistent definitions. Start with key events and build a governed layer that supports reliable Analytics<\/strong> and Conversion & Measurement<\/strong> reporting.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                Bigquery Streaming Export is a data integration approach used in **Conversion & Measurement** to move event-level marketing and product data into a queryable warehouse in near real time. Instead of waiting for a daily batch file or delayed reporting table, teams use **Bigquery Streaming Export** to make fresh interactions\u2014page views, sign-ups, purchases, app events, lead submissions\u2014available quickly for **Analytics**, attribution, experimentation, and operational decision-making.<\/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":[1887],"tags":[],"class_list":["post-6817","post","type-post","status-publish","format-standard","hentry","category-analytics"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/6817","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=6817"}],"version-history":[{"count":0,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/posts\/6817\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/media?parent=6817"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/categories?post=6817"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.wizbrand.com\/tutorials\/wp-json\/wp\/v2\/tags?post=6817"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}