{"id":18922,"date":"2025-10-18T00:42:42","date_gmt":"2025-10-18T03:42:42","guid":{"rendered":"https:\/\/moderniza.me\/bury\/?p=18922"},"modified":"2026-09-24T12:51:09","modified_gmt":"2026-09-24T15:51:09","slug":"building-a-winning-loyalty-engine-for-cloud-based-casino-platforms","status":"publish","type":"post","link":"https:\/\/moderniza.me\/bury\/blog\/2025\/10\/18\/building-a-winning-loyalty-engine-for-cloud-based-casino-platforms\/","title":{"rendered":"Building a Winning Loyalty Engine for Cloud\u2011Based Casino Platforms"},"content":{"rendered":"

Cloud gaming has moved from a novelty to the backbone of modern online casino operations. With elastic compute, global edge locations and AI\u2011driven analytics, operators can launch new slots, live dealer tables or sports\u2011betting widgets in minutes rather than months. Yet the hardware and software stack only become profitable when players keep returning, wagering, and climbing the reward ladder. Loyalty programs are the bridge that turns raw server capacity into sustainable revenue, turning a one\u2011time deposit into a lifelong relationship.<\/p>\n

For a deeper look at how loyalty can boost player retention, see our recent piece on casino dubai<\/a>. The Blogeristit site offers a neutral hub for industry news and can serve as a quick reference when you need to compare best\u2011practice checklists or explore case studies from other markets, including the fast\u2011growing UAE gambling scene.<\/p>\n

This article walks technical managers, product owners and marketing teams through a step\u2011by\u2011step guide. You\u2019ll learn how to map the player journey onto cloud services, pick the right provider, build a serverless points engine, and future\u2011proof the whole system for 5G and edge\u2011driven gaming. Every section ends with actionable templates, bullet\u2011point checklists or a small table you can copy into your own project plan.<\/p>\n

1. Mapping the Player Journey onto Cloud Architecture<\/h2>\n

The online casino player lifecycle can be broken into five recognizable stages: <\/p>\n\n\n\n\n\n\n\n\n\n
Stage<\/th>\nCore Actions<\/th>\nTypical Cloud Touchpoints<\/th>\n<\/tr>\n<\/thead>\n
Discovery<\/td>\nClick\u2011through ads, SEO, affiliate links<\/td>\nCDN edge cache, WAF<\/td>\n<\/tr>\n
Onboarding<\/td>\nAccount creation, KYC, first\u2011time bonus claim<\/td>\nAPI Gateway, Lambda authorizer, DynamoDB<\/td>\n<\/tr>\n
First Deposit<\/td>\nPayment processing, fraud check<\/td>\nPayment gateway, SQS, RDS<\/td>\n<\/tr>\n
Regular Play<\/td>\nBets on slots, live dealer, sports<\/td>\nGameLift\/Agones, Redis cache, analytics pipeline<\/td>\n<\/tr>\n
VIP \/ Retention<\/td>\nTier upgrades, exclusive tournaments<\/td>\nGlobal tables, AI\/ML inference, EventBridge<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Each stage creates data that must travel through the cloud stack without adding perceptible latency. For example, a bet placed on a high\u2011volatility slot generates a win\u2011loss event that should instantly update the player\u2019s points balance. If that update must travel from an edge node in Dubai to a central analytics cluster in Virginia, the round\u2011trip time can exceed 150\u202fms, breaking the illusion of real\u2011time reward delivery. <\/p>\n

To avoid such spikes, align loyalty touchpoints with the natural data flow of the game session. When a player finishes a spin, the game server publishes a \u201cBetCompleted\u201d event to a message queue that is already part of the gameplay pipeline. A downstream loyalty microservice consumes the same event, calculates points, and writes the result back to the player profile stored in a low\u2011latency cache. This co\u2011location eliminates extra hops and guarantees that the bonus popup appears within the 100\u202fms window that seasoned gamblers expect.<\/p>\n

Quick checklist for mapping journey stages to cloud services <\/p>\n

    \n
  • Identify the primary event source for each stage (e.g., CloudFront for discovery, API Gateway for onboarding). <\/li>\n
  • Match each event to a managed service that already handles the payload (SQS, Pub\/Sub, Event Grid). <\/li>\n
  • Pin loyalty microservices to the same availability zone or edge location as the game server. <\/li>\n
  • Validate latency with synthetic transactions before going live. <\/li>\n<\/ul>\n

    2. Selecting the Right Cloud Provider for Loyalty Scalability<\/h2>\n

    When the loyalty engine must scale from a few hundred concurrent users to millions during a high\u2011profile tournament, the choice of cloud provider becomes a strategic decision. <\/p>\n\n\n\n\n\n\n\n\n\n
    Feature<\/th>\nAWS<\/th>\nGoogle Cloud<\/th>\nAzure<\/th>\n<\/tr>\n<\/thead>\n
    Global Edge Footprint<\/td>\n210+ edge locations, CloudFront<\/td>\n200+ edge POPs, Cloud CDN<\/td>\n150+ edge nodes, Azure Front Door<\/td>\n<\/tr>\n
    Serverless Functions<\/td>\nLambda (up to 15\u202fGB memory)<\/td>\nCloud Functions (up to 8\u202fGB)<\/td>\nAzure Functions (up to 14\u202fGB)<\/td>\n<\/tr>\n
    Built\u2011in AI\/ML<\/td>\nSageMaker, Personalize<\/td>\nVertex AI, Recommendations AI<\/td>\nAzure ML, Cognitive Services<\/td>\n<\/tr>\n
    Pricing Predictability<\/td>\nSavings Plans, Compute Savings<\/td>\nCommitted Use Discounts<\/td>\nReserved Instances, Azure Hybrid Benefit<\/td>\n<\/tr>\n
    Loyalty\u2011Specific Tools<\/td>\nGameLift, DynamoDB Global Tables<\/td>\nGame Servers (Agones), Firestore<\/td>\nPlayFab (acquired), Cosmos DB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    Cost\u2011predictability is crucial for tiered reward calculations. A \u201cGold\u201d tier that offers 1.5\u202f\u00d7 points during a weekend promotion must be priced against the expected \u201cpay\u2011as\u2011you\u2011go\u201d function invocations. Reserved instances can lock down the baseline cost of always\u2011on services (e.g., a PostgreSQL instance holding player profiles), while serverless workloads remain on a consumption model, scaling precisely with betting spikes.<\/p>\n

    Decision\u2011matrix template <\/p>\n\n\n\n\n\n\n\n\n\n
    Requirement<\/th>\nWeight (1\u20115)<\/th>\nAWS Score<\/th>\nGCP Score<\/th>\nAzure Score<\/th>\n<\/tr>\n<\/thead>\n
    Global low\u2011latency edge<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
    Serverless concurrency<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
    Integrated ML pipelines<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
    Cost\u2011predictable tiering<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
    Existing vendor contracts<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    Fill in the matrix with your own scores (1\u202f=\u202fpoor fit, 5\u202f=\u202fexcellent fit) and multiply by the weight to see which provider aligns best with your loyalty\u2011engine roadmap.<\/p>\n

    3. Designing a Real\u2011Time Points Engine on Serverless Infrastructure<\/h2>\n

    A points engine must process every wagering event, calculate the appropriate reward and persist the result\u2014all within a few milliseconds. Below is a practical blueprint using AWS Lambda; the same concepts translate to Azure Functions or Google Cloud Functions with minor syntax changes. <\/p>\n

      \n
    1. Event source \u2013 The game server publishes a JSON payload to an Amazon SQS queue named BetEvents<\/code>. The payload includes playerId<\/code>, gameId<\/code>, betAmount<\/code>, winAmount<\/code> and a timestamp. <\/li>\n
    2. Lambda trigger \u2013 A Lambda function CalculatePoints<\/code> is subscribed to the queue. The function reads batches of up to 10 messages, reducing per\u2011invocation overhead. <\/li>\n
    3. Points formula \u2013 points = floor(betAmount \u00d7 multiplier)<\/code>. Multipliers are stored in a DynamoDB table GameMultipliers<\/code> and refreshed daily via a separate scheduled Lambda. <\/li>\n
    4. Idempotency \u2013 Each event carries a unique eventId<\/code>. Before writing, the function checks a DynamoDB ProcessedEvents<\/code> table; if the ID exists, the function exits, guaranteeing exactly\u2011once semantics even if SQS delivers duplicates. <\/li>\n
    5. Concurrency control \u2013 The function uses DynamoDB\u2019s conditional writes (attribute_not_exists<\/code>) to prevent race conditions when two bets from the same player hit the queue simultaneously. <\/li>\n
    6. Write\u2011back \u2013 Updated points are added to the player\u2019s PointsLedger<\/code> (a DynamoDB partition keyed by playerId<\/code>). A UpdateExpression<\/code> increments the totalPoints<\/code> attribute atomically. <\/li>\n<\/ol>\n

      Monitoring \u2013 CloudWatch Metrics track Duration<\/code>, Throttles<\/code> and IteratorAge<\/code>. Alarms fire if average latency exceeds 100\u202fms or if the dead\u2011letter queue grows beyond 1\u202f% of total events. A lightweight dashboard visualizes these KPIs alongside business metrics such as \u201cpoints earned per active user.\u201d<\/p>\n

      4. Integrating Tiered Rewards with Distributed Databases<\/h2>\n

      A globally replicated NoSQL store is the most efficient way to keep tier status and reward inventories consistent for players who hop between data centers. DynamoDB Global Tables or Azure Cosmos DB with multi\u2011region replication provide sub\u2011second reads worldwide while handling millions of concurrent updates. <\/p>\n

      Schema snapshot <\/p>\n

        \n
      • PlayerProfile<\/code> (PK: playerId<\/code>) \u2013 stores personal data, current tier, and a pointer to the latest ledger entry. <\/li>\n
      • PointsLedger<\/code> (PK: playerId<\/code>, SK: timestamp<\/code>) \u2013 immutable record of every point transaction, useful for audits. <\/li>\n
      • RewardCatalog<\/code> (PK: rewardId<\/code>) \u2013 defines reward type, required points, expiration, and regional availability. <\/li>\n<\/ul>\n

        Consistency trade\u2011offs <\/p>\n

          \n
        • Eventual consistency<\/em> keeps write latency low; tier upgrades appear within 1\u20112 seconds across regions, which is acceptable for most bonus offers. <\/li>\n
        • Strong consistency<\/em> is required for high\u2011value rewards (e.g., a \u20ac5,000 cash voucher) where the player must see the new tier instantly before claiming. Cosmos DB offers configurable consistency per container, allowing you to mix models within the same database. <\/li>\n<\/ul>\n

          When a player crosses a threshold (e.g., 10,000 points), a DynamoDB Stream triggers a Lambda that upgrades the tier<\/code> attribute in PlayerProfile<\/code> and pushes a notification through Amazon SNS to the mobile app. The same stream can also write a \u201cTierUpgrade\u201d event to the analytics pipeline for cohort analysis.<\/p>\n

          5. Leveraging AI to Personalize Loyalty Offers<\/h2>\n

          Machine learning can turn raw betting data into highly targeted loyalty offers that increase both engagement and average revenue per user (ARPU). A typical pipeline looks like this: <\/p>\n

            \n
          1. Data lake \u2013 All game events, player demographics and past promotions flow into an S3 bucket (or Azure Data Lake). Partition by gameId<\/code> and date<\/code> for efficient queries. <\/li>\n
          2. Feature engineering \u2013 A Glue job (or Dataflow) aggregates metrics such as \u201caverage bet per session,\u201d \u201cvolatility preference,\u201d and \u201cchurn risk score.\u201d These features are stored in a feature store for reuse. <\/li>\n
          3. Model training \u2013 Using SageMaker (or Vertex AI), train a gradient\u2011boosted tree that predicts the probability a player will drop out within the next 7 days. The target variable is derived from a binary label: 1<\/code> if no login occurs for 7+ days, else 0<\/code>. <\/li>\n
          4. Inference endpoint \u2013 Deploy the model as a real\u2011time endpoint. When the points engine writes a new ledger entry, it also calls the endpoint with the player\u2019s latest feature vector. The response includes a churn risk score and recommended bonus type. <\/li>\n
          5. Personalized offer generation \u2013 A rules engine reads the score: if risk > 0.7, it creates a \u201cdouble points on your favorite slot\u201d coupon; if risk < 0.3, it suggests a \u201cfree spin bundle\u201d to encourage higher stakes. <\/li>\n<\/ol>\n

            Privacy and security \u2013 All raw player data must be pseudonymized before entering the lake, and access is limited to the ML role via IAM policies. GDPR and CCPA compliance is achieved by providing an opt\u2011out endpoint that removes the player\u2019s identifier from the feature store within 30 days of request. <\/p>\n

            6. Ensuring Security and Fair Play in Loyalty Transactions<\/h2>\n

            Loyalty transactions are monetary equivalents; a breach could lead to regulatory fines and loss of brand trust. The following controls form a defense\u2011in\u2011depth posture: <\/p>\n

              \n
            • Encryption \u2013 Enable server\u2011side encryption (SSE\u2011KMS) for all databases and S3 objects. Use TLS\u202f1.2 for all API traffic. <\/li>\n
            • IAM segregation \u2013 Create a dedicated role LoyaltyEngineRole<\/code> with dynamodb:UpdateItem<\/code> on PointsLedger<\/code> only. No human user should have this permission directly. <\/li>\n
            • Audit logging \u2013 CloudTrail (or Azure Activity Log) records every write to loyalty tables. Export logs to a centralized SIEM for real\u2011time anomaly detection. <\/li>\n
            • Immutable ledger \u2013 For high\u2011value rewards (e.g., a VIP cruise), store a hash chain of each transaction in a blockchain\u2011style ledger (Amazon QLDB or Azure Confidential Ledger). The hash links each entry to the previous one, making retroactive tampering practically impossible. <\/li>\n<\/ul>\n

              Quick audit checklist <\/p>\n

                \n
              • \u2610 Encryption at rest and in transit enabled for all loyalty data. <\/li>\n
              • \u2610 Least\u2011privilege IAM roles applied to every microservice. <\/li>\n
              • \u2610 Immutable ledger configured for rewards > $1,000. <\/li>\n
              • \u2610 Daily log export to SIEM and retention for 90\u202fdays. <\/li>\n
              • \u2610 Regular penetration test focusing on API gateways. <\/li>\n<\/ul>\n

                7. Syncing Loyalty Data Across Multiple Casino Brands<\/h2>\n

                Large operators often run several branded portals\u2014one focused on slots, another on live dealer tables, and a third on sports betting. A unified loyalty backbone prevents players from feeling forced to start over when they switch brands. <\/p>\n

                Multi\u2011tenant architecture <\/p>\n

                  \n
                • Shared services layer \u2013 A set of microservices (points engine, tier manager, reward catalog) expose generic APIs. <\/li>\n
                • Tenant\u2011specific partitions \u2013 In DynamoDB, use a composite primary key PK = tenantId#playerId<\/code>. This isolates each brand\u2019s data while allowing cross\u2011brand queries when needed. <\/li>\n
                • API Gateway \u2013 Deploy a single gateway with stage variables for each brand (e.g., \/brandA\/*<\/code>, \/brandB\/*<\/code>). The gateway injects the tenantId<\/code> header, which downstream services use to route to the correct partition. <\/li>\n<\/ul>\n

                  Case\u2011study snippet <\/p>\n

                  Brand\u202fA introduced a \u201cLucky Spin\u201d promotion that awarded 500 points per 10\u202f\u20ac bet. A player who earned 2,000 points on Brand\u202fA later logged into Brand\u202fB, which runs a high\u2011roller tournament requiring 5,000 points. Because the loyalty backbone shared the same PointsLedger<\/code> table with tenant partitions, the player\u2019s balance was instantly visible on Brand\u202fB, and the system automatically applied a 10\u202f% boost to meet the tournament entry threshold. No manual reconciliation was required, and the player perceived a seamless experience across brands. <\/p>\n

                  8. Monitoring Performance and Optimizing Costs<\/h2>\n

                  Effective monitoring ties technical health to business outcomes. <\/p>\n

                  Key performance indicators <\/p>\n

                    \n
                  • Points\u2011engine latency \u2013 Target \u2264\u202f100\u202fms per event. <\/li>\n
                  • Reward redemption rate \u2013 Percentage of issued coupons that are claimed within 7\u202fdays. <\/li>\n
                  • Cost per transaction \u2013 Total cloud spend divided by number of loyalty events processed. <\/li>\n<\/ul>\n

                    Dashboard recommendation <\/p>\n

                      \n
                    • Use Grafana connected to CloudWatch (or Azure Monitor) for real\u2011time latency charts. <\/li>\n
                    • Pull business metrics from the analytics warehouse into Power\u202fBI and embed them alongside technical graphs. A single \u201cLoyalty Health\u201d dashboard gives CEOs a one\u2011page view of both sides. <\/li>\n<\/ul>\n

                      Cost\u2011optimisation tips <\/p>\n

                        \n
                      • Right\u2011size functions \u2013 Start with 256\u202fMB memory for the points engine; increase only if latency spikes. AWS Lambda\u2019s cost curve is linear, so over\u2011provisioning quickly inflates spend. <\/li>\n
                      • Spot instances for batch jobs \u2013 Monthly \u201creward inventory reconciliation\u201d can run on EC2 Spot or Azure Low\u2011Priority VMs, cutting compute costs by up to 70\u202f%. <\/li>\n
                      • Auto\u2011scaling thresholds \u2013 Set the queue length trigger at 1,000 messages; the function will spin up additional concurrency automatically, avoiding over\u2011provisioned idle capacity. <\/li>\n<\/ul>\n

                        9. Future\u2011Proofing: Preparing for 5G and Edge\u2011Driven Gaming<\/h2>\n

                        5G promises sub\u201110\u202fms round\u2011trip times, while edge platforms bring compute literally to the user\u2019s ISP. Loyalty engines can exploit this by delivering hyper\u2011responsive triggers that feel like a natural extension of the game. <\/p>\n

                        Architectural upgrades <\/p>\n

                          \n
                        • Edge\u2011deployed containers \u2013 Package the points engine as a Docker container and run it on AWS Wavelength or Cloudflare Workers. The container receives the bet event from a local edge Pub\/Sub node, calculates points, and responds within 30\u202fms. <\/li>\n
                        • Ultra\u2011low\u2011latency bonus push \u2013 During a live dealer session, a \u201cwin\u2011back\u201d bonus can be sent the moment a player\u2019s streak ends, encouraging an immediate re\u2011bet. The edge function can access the player\u2019s session token directly, bypassing central API hops. <\/li>\n
                        • Incremental rollout \u2013 Start with a pilot region (e.g., Dubai) where 5G coverage is dense. Mirror the existing serverless pipeline in the edge, validate latency, then gradually extend to other markets. <\/li>\n<\/ul>\n

                          Roadmap checklist <\/p>\n

                            \n
                          • \u2610 Evaluate edge providers (Wavelength, Cloudflare, Azure Edge Zones). <\/li>\n
                          • \u2610 Refactor points engine into a stateless container image. <\/li>\n
                          • \u2610 Deploy a test suite that measures end\u2011to\u2011end latency under 5G conditions. <\/li>\n
                          • \u2610 Set up feature flags to toggle edge processing per region. <\/li>\n
                          • \u2610 Monitor cost impact; edge compute is premium but can be justified by higher ARPU from instant bonuses. <\/li>\n<\/ul>\n

                            Conclusion<\/h2>\n

                            A robust loyalty engine is no longer an afterthought; it is a core component of any cloud\u2011native casino platform. By mapping the player journey onto your architecture, selecting a provider that matches scalability needs, and building a serverless points microservice, you lay a technical foundation that delivers instant, secure rewards. Adding AI\u2011driven personalization, multi\u2011brand synchronization and edge\u2011ready designs ensures the program stays relevant as 5G reshapes player expectations. <\/p>\n

                            Start with the journey\u2011mapping checklist, prototype the points engine in a sandbox, and iterate using the decision matrix and cost\u2011optimisation tips provided. As you refine latency, security and personalization, you\u2019ll see player lifetime value climb in step with the loyalty tier ladder. For further reading, the Blogeristit site hosts additional resources on cloud best practices and compliance guidelines that can help you fine\u2011tune each component. Feel free to share your results or ask questions in the comments; the community is eager to see how your loyalty engine evolves.<\/p>\n","protected":false},"excerpt":{"rendered":"

                            Cloud gaming has moved from a novelty to the backbone of modern online casino operations. With elastic compute, global edge…<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"jetpack_featured_media_url":"","yoast_head":"\nBuilding a Winning Loyalty Engine for Cloud\u2011Based Casino Platforms -<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/moderniza.me\/bury\/blog\/2025\/10\/18\/building-a-winning-loyalty-engine-for-cloud-based-casino-platforms\/\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:title\" content=\"Building a Winning Loyalty Engine for Cloud\u2011Based Casino Platforms -\" \/>\n<meta name=\"twitter:description\" content=\"Cloud gaming has moved from a novelty to the backbone of modern online casino operations. With elastic compute, global edge…\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\">\n\t<meta name=\"twitter:data1\" content=\"12 minutos\">\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebSite\",\"@id\":\"https:\/\/moderniza.me\/bury\/#website\",\"url\":\"https:\/\/moderniza.me\/bury\/\",\"name\":\"\",\"description\":\"\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":\"https:\/\/moderniza.me\/bury\/?s={search_term_string}\",\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"pt-BR\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/moderniza.me\/bury\/blog\/2025\/10\/18\/building-a-winning-loyalty-engine-for-cloud-based-casino-platforms\/#webpage\",\"url\":\"https:\/\/moderniza.me\/bury\/blog\/2025\/10\/18\/building-a-winning-loyalty-engine-for-cloud-based-casino-platforms\/\",\"name\":\"Building a Winning Loyalty Engine for Cloud\\u2011Based Casino Platforms -\",\"isPartOf\":{\"@id\":\"https:\/\/moderniza.me\/bury\/#website\"},\"datePublished\":\"2025-10-18T03:42:42+00:00\",\"dateModified\":\"2026-09-24T15:51:09+00:00\",\"author\":{\"@id\":\"https:\/\/moderniza.me\/bury\/#\/schema\/person\/ad08316ddc83d084d89ee909de30d4a3\"},\"inLanguage\":\"pt-BR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/moderniza.me\/bury\/blog\/2025\/10\/18\/building-a-winning-loyalty-engine-for-cloud-based-casino-platforms\/\"]}]},{\"@type\":\"Person\",\"@id\":\"https:\/\/moderniza.me\/bury\/#\/schema\/person\/ad08316ddc83d084d89ee909de30d4a3\",\"name\":\"Vittoria Thomasini\",\"image\":{\"@type\":\"ImageObject\",\"@id\":\"https:\/\/moderniza.me\/bury\/#personlogo\",\"inLanguage\":\"pt-BR\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/618c3fca2a619ce19dcbadd6705297d4?s=96&d=mm&r=g\",\"caption\":\"Vittoria Thomasini\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","_links":{"self":[{"href":"https:\/\/moderniza.me\/bury\/wp-json\/wp\/v2\/posts\/18922"}],"collection":[{"href":"https:\/\/moderniza.me\/bury\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/moderniza.me\/bury\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/moderniza.me\/bury\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/moderniza.me\/bury\/wp-json\/wp\/v2\/comments?post=18922"}],"version-history":[{"count":1,"href":"https:\/\/moderniza.me\/bury\/wp-json\/wp\/v2\/posts\/18922\/revisions"}],"predecessor-version":[{"id":18923,"href":"https:\/\/moderniza.me\/bury\/wp-json\/wp\/v2\/posts\/18922\/revisions\/18923"}],"wp:attachment":[{"href":"https:\/\/moderniza.me\/bury\/wp-json\/wp\/v2\/media?parent=18922"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/moderniza.me\/bury\/wp-json\/wp\/v2\/categories?post=18922"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/moderniza.me\/bury\/wp-json\/wp\/v2\/tags?post=18922"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}