{"id":134862,"date":"2026-09-07T13:24:07","date_gmt":"2026-09-07T07:54:07","guid":{"rendered":"https:\/\/www.guvi.in\/blog\/?p=134862"},"modified":"2026-09-07T13:24:10","modified_gmt":"2026-09-07T07:54:10","slug":"apache-kafka-for-real-time-ml-data-streaming","status":"publish","type":"post","link":"https:\/\/www.guvi.in\/blog\/apache-kafka-for-real-time-ml-data-streaming\/","title":{"rendered":"Apache Kafka for Real-Time ML Data Streaming"},"content":{"rendered":"\n<p>Machine learning applications increasingly need fresh data to make predictions, detect anomalies, and respond to changing conditions. <strong>Apache Kafka<\/strong> provides a distributed event-streaming platform that can continuously move large volumes of data between applications and ML systems. By connecting data producers, processing services, and machine learning models, Kafka helps organizations build responsive real-time AI pipelines.&nbsp;&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>TL;DR Summary<\/strong><\/h3>\n\n\n\n<ul>\n<li>Apache Kafka is a distributed event-streaming platform.<\/li>\n\n\n\n<li>It continuously moves data between applications and services.<\/li>\n\n\n\n<li>Kafka can provide fresh data to ML pipelines.<\/li>\n\n\n\n<li>Topics organize streams of events.<\/li>\n\n\n\n<li>Kafka supports scalable real-time ML architectures.<\/li>\n<\/ul>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Direct Answer&nbsp;<\/strong><\/h4>\n\n\n\n<figure class=\"wp-block-table has-medium-font-size\"><table><tbody><tr><td><strong>Apache Kafka<\/strong> is a distributed event-streaming platform that enables applications to publish, store, and process streams of data in real time. In machine learning workflows, Kafka can continuously deliver events such as transactions, sensor readings, and user activity to data-processing and inference systems. This enables organizations to build scalable, responsive, and continuously updated ML applications.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Why Apache Kafka Matters for Machine Learning<\/strong><\/h2>\n\n\n\n<p>Traditional batch pipelines process data at scheduled intervals, which can introduce delays between an event occurring and an ML system responding to it.<\/p>\n\n\n\n<p>Kafka enables continuous data movement, making it useful when ML applications require fresh information.<\/p>\n\n\n\n<p>Key benefits include:<\/p>\n\n\n\n<ul>\n<li>Real-time data delivery<\/li>\n\n\n\n<li>Scalable event processing<\/li>\n\n\n\n<li>Reliable data pipelines<\/li>\n\n\n\n<li>Decoupled ML services<\/li>\n\n\n\n<li>High-throughput streaming<\/li>\n\n\n\n<li>Continuous data availability<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What Is Apache Kafka?<\/strong><\/h2>\n\n\n\n<p><a href=\"https:\/\/www.guvi.in\/blog\/what-is-apache-kafka\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Apache Kafka<\/strong> <\/a>is an open-source distributed event-streaming platform designed to handle large volumes of data in real time.<\/p>\n\n\n\n<p>Applications can publish events to Kafka, while other applications consume those events independently.<\/p>\n\n\n\n<p>For example, an e-commerce platform could publish customer activity events to Kafka. A machine learning service could then consume those events to update recommendations or detect unusual behavior.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>How Kafka Works<\/strong><\/h2>\n\n\n\n<p>Kafka uses several core components to manage event streams.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 1: Producers Send Events<\/strong><\/h3>\n\n\n\n<p>Applications called <strong>producers<\/strong> publish data to Kafka.<\/p>\n\n\n\n<p>Examples include:<\/p>\n\n\n\n<ul>\n<li>Website activity<\/li>\n\n\n\n<li>Financial transactions<\/li>\n\n\n\n<li>IoT sensor readings<\/li>\n\n\n\n<li>Application logs<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 2: Kafka Stores Events in Topics<\/strong><\/h3>\n\n\n\n<p>Events are organized into <strong>topics<\/strong>, which act as logical streams of data.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 3: Consumers Read Events<\/strong><\/h3>\n\n\n\n<p>Applications called <strong>consumers<\/strong> subscribe to topics and process the incoming data.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 4: ML Systems Process the Data<\/strong><\/h3>\n\n\n\n<p><a href=\"https:\/\/www.guvi.in\/blog\/machine-learning-pipeline\/\" target=\"_blank\" rel=\"noreferrer noopener\">Machine learning pipelines<\/a> can transform incoming events, generate features, or send data to inference services.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Step 5: Predictions Trigger Actions<\/strong><\/h3>\n\n\n\n<p>Predictions can be delivered to downstream applications for recommendations, alerts, fraud detection, or automated decisions.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Key Kafka Concepts<\/strong><\/h2>\n\n\n\n<p>Understanding these concepts makes <strong>Apache Kafka<\/strong> easier to use in ML systems.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Producers<\/strong><\/h3>\n\n\n\n<p>Producers publish events to Kafka topics.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Consumers<\/strong><\/h3>\n\n\n\n<p>Consumers read and process events from Kafka topics.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Topics<\/strong><\/h3>\n\n\n\n<p>Topics organize related streams of events.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Partitions<\/strong><\/h3>\n\n\n\n<p>Topics can be divided into partitions, allowing data to be distributed and processed in parallel.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Brokers<\/strong><\/h3>\n\n\n\n<p>Kafka brokers store and serve event data within a Kafka cluster.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Consumer Groups<\/strong><\/h3>\n\n\n\n<p>Consumer groups allow multiple consumers to work together to process streams efficiently.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Kafka in a Real-Time ML Pipeline<\/strong><\/h2>\n\n\n\n<p>A typical architecture can look like:<\/p>\n\n\n\n<p><strong>Data Sources \u2192 Kafka \u2192 Stream Processing \u2192 Feature Generation \u2192 ML Model \u2192 Prediction \u2192 Application<\/strong><\/p>\n\n\n\n<p>For example, in fraud detection:<\/p>\n\n\n\n<ol>\n<li>A customer makes a transaction.<\/li>\n\n\n\n<li>The transaction event is published to Kafka.<\/li>\n\n\n\n<li>A streaming service processes the event.<\/li>\n\n\n\n<li>Relevant features are generated.<\/li>\n\n\n\n<li>The ML model evaluates the transaction.<\/li>\n\n\n\n<li>A fraud score is produced.<\/li>\n\n\n\n<li>The system can trigger an appropriate response.<\/li>\n<\/ol>\n\n\n\n<p>This architecture allows ML systems to respond to new data without waiting for a scheduled batch process.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Common Applications<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Fraud Detection<\/strong><\/h3>\n\n\n\n<p>Process financial transactions as they occur and send relevant information to ML models for real-time risk scoring.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Recommendation Systems<\/strong><\/h3>\n\n\n\n<p>Analyze customer interactions and use fresh activity data to improve personalized recommendations.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Predictive Maintenance<\/strong><\/h3>\n\n\n\n<p>Stream sensor readings from machines and use ML models to identify unusual patterns that may indicate equipment problems.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Cybersecurity<\/strong><\/h3>\n\n\n\n<p>Process network and system events continuously to detect suspicious behavior.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Customer Analytics<\/strong><\/h3>\n\n\n\n<p>Stream clicks, purchases, searches, and other interactions for real-time behavioral analysis.<\/p>\n\n\n\n<div style=\"background-color: #099f4e; border: 3px solid #110053; border-radius: 12px; padding: 18px 22px; color: #FFFFFF; font-size: 18px; font-family: Montserrat, Helvetica, sans-serif; line-height: 1.6; box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15); max-width: 750px;\"> \n  <strong style=\"font-size: 22px; color: #FFFFFF;\">\ud83d\udca1 Did You Know?<\/strong> \n  <br \/><br \/> \n\nKafka is not itself a machine learning framework. Its role is to transport and manage streaming data so that ML systems can access fresh information reliably. A production ML architecture may combine Kafka with stream-processing tools, feature stores, model-serving systems, and monitoring platforms.\n\n\n\n\n\n\n \n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Benefits of Apache Kafka for ML<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Real-Time Data Access<\/strong><\/h3>\n\n\n\n<p>Kafka continuously delivers new events to downstream systems.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Scalability<\/strong><\/h3>\n\n\n\n<p>Partitions allow workloads to be distributed across multiple consumers and brokers.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Fault Tolerance<\/strong><\/h3>\n\n\n\n<p>Kafka is designed to provide durable event storage and resilient distributed processing.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Decoupled Architecture<\/strong><\/h3>\n\n\n\n<p>Data producers and ML consumers can operate independently, making systems easier to scale and maintain.<\/p>\n\n\n\n<p>Professionals interested in machine learning pipelines, AI engineering, data streaming, and real-time analytics can strengthen their expertise through <strong>HCL GUVI&#8217;s <\/strong><a href=\"https:\/\/www.guvi.in\/courses\/bundles\/artificial-intelligence-machine-learning\/?utm_source=blog&amp;utm_medium=hyperlink+&amp;utm_campaign=Apache+Kafka+for+Real-Time+ML+Data+Streaming\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>Artificial Intelligence and Machine Learning<\/strong><\/a><strong> Course<\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>When Should You Use Apache Kafka for ML?<\/strong><\/h2>\n\n\n\n<p><strong>Apache Kafka<\/strong> is most useful when machine learning systems need a continuous flow of fresh data rather than periodic batch updates.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Real-Time Fraud Detection<\/strong><\/h3>\n\n\n\n<p>Stream transactions continuously to an ML model so suspicious activity can be identified shortly after it occurs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Recommendation Systems<\/strong><\/h3>\n\n\n\n<p>Capture clicks, searches, purchases, and browsing behavior to provide recommendations based on recent user activity.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Predictive Maintenance<\/strong><\/h3>\n\n\n\n<p>Stream machine and sensor data into an ML pipeline to identify patterns associated with potential equipment failures.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Cybersecurity<\/strong><\/h3>\n\n\n\n<p>Process network traffic, authentication events, and system logs continuously to identify unusual behavior.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Real-Time Customer Analytics<\/strong><\/h3>\n\n\n\n<p>Stream customer interactions to ML systems for behavioral analysis, personalization, and automated decision-making.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Key Concepts to Remember<\/strong><\/h2>\n\n\n\n<p>Understanding these Kafka concepts is essential for building real-time ML pipelines.<\/p>\n\n\n\n<ul>\n<li><strong>Producers<\/strong> publish events to Kafka.<\/li>\n\n\n\n<li><strong>Consumers<\/strong> read and process events.<\/li>\n\n\n\n<li><strong>Topics<\/strong> organize related event streams.<\/li>\n\n\n\n<li><strong>Partitions<\/strong> distribute data for parallel processing.<\/li>\n\n\n\n<li><strong>Brokers<\/strong> store and serve Kafka data.<\/li>\n\n\n\n<li><strong>Consumer groups<\/strong> allow multiple consumers to process streams together.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>A Practical Real-Time ML Workflow<\/strong><\/h2>\n\n\n\n<p>A Kafka-powered ML system can follow this architecture:<\/p>\n\n\n\n<p><strong>Data Source \u2192 Kafka Producer \u2192 Kafka Topic \u2192 Stream Processor \u2192 ML Model \u2192 Prediction \u2192 Application<\/strong><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Capture Data<\/strong><\/h3>\n\n\n\n<p>Applications generate events such as transactions, sensor readings, or user interactions.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Stream Through Kafka<\/strong><\/h3>\n\n\n\n<p>Producers publish those events to the appropriate Kafka topics.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>3. Process the Stream<\/strong><\/h3>\n\n\n\n<p>A stream-processing system cleans, transforms, and prepares incoming events.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>4. Generate Features<\/strong><\/h3>\n\n\n\n<p>Relevant information is converted into features required by the ML model.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>5. Generate Predictions<\/strong><\/h3>\n\n\n\n<p>The model receives the processed data and produces predictions in real time.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>6. Trigger Actions<\/strong><\/h3>\n\n\n\n<p>Applications use predictions to provide recommendations, generate alerts, detect fraud, or automate decisions.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Real-World Applications<\/strong><\/h2>\n\n\n\n<p>Kafka supports real-time ML workflows across multiple industries.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Finance<\/strong><\/h3>\n\n\n\n<ul>\n<li>Fraud detection<\/li>\n\n\n\n<li>Transaction monitoring<\/li>\n\n\n\n<li>Risk scoring<\/li>\n\n\n\n<li>Financial anomaly detection<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Retail<\/strong><\/h3>\n\n\n\n<ul>\n<li>Personalized recommendations<\/li>\n\n\n\n<li>Customer behavior analysis<\/li>\n\n\n\n<li>Real-time demand insights<\/li>\n\n\n\n<li>Purchase prediction<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Manufacturing<\/strong><\/h3>\n\n\n\n<ul>\n<li>Predictive maintenance<\/li>\n\n\n\n<li>Sensor monitoring<\/li>\n\n\n\n<li>Equipment anomaly detection<\/li>\n\n\n\n<li>Production optimization<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Cybersecurity<\/strong><\/h3>\n\n\n\n<ul>\n<li>Threat detection<\/li>\n\n\n\n<li>Login monitoring<\/li>\n\n\n\n<li>Network anomaly detection<\/li>\n\n\n\n<li>Security event analysis<\/li>\n<\/ul>\n\n\n\n<p>The <strong>HCL GUVI&#8217;s Artificial Intelligence <\/strong><a href=\"https:\/\/www.guvi.in\/mlp\/genai-ebook\/?utm_source=blog&amp;utm_medium=hyperlink+&amp;utm_campaign=Apache+Kafka+for+Real-Time+ML+Data+Streaming\" target=\"_blank\" rel=\"noreferrer noopener\"><strong>eBook<\/strong><\/a> introduces the fundamentals of artificial intelligence, machine learning, generative AI, and intelligent automation. It helps learners develop a broader understanding of AI technologies and how they can support modern data-driven applications.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Best Practices<\/strong><\/h2>\n\n\n\n<ul>\n<li>Design Kafka topics around meaningful event streams.<\/li>\n\n\n\n<li>Choose partitions according to expected workload and scalability requirements.<\/li>\n\n\n\n<li>Monitor consumer lag and system performance.<\/li>\n\n\n\n<li>Validate and clean streaming data before model inference.<\/li>\n\n\n\n<li>Keep ML models and streaming infrastructure independently maintainable.<\/li>\n\n\n\n<li>Monitor prediction quality as well as infrastructure health.<\/li>\n\n\n\n<li>Plan for failures and temporary interruptions in downstream ML services.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Conclusion&nbsp;<\/strong><\/h2>\n\n\n\n<p><strong>Apache Kafka<\/strong> provides the streaming foundation required by many real-time ML systems. By continuously moving events from applications to processing pipelines and machine learning models, Kafka enables faster fraud detection, recommendations, predictive maintenance, and behavioral analysis. Its scalable, distributed architecture helps organizations build responsive AI systems that can process continuously changing data instead of relying solely on periodic batch processing.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>FAQs<\/strong><\/h2>\n\n\n<div id=\"rank-math-faq\" class=\"rank-math-block\">\n<div class=\"rank-math-list \">\n<div id=\"faq-question-1787336869186\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>1. What is Apache Kafka?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p><strong>Apache Kafka<\/strong> is a distributed event-streaming platform that allows applications to publish, store, and consume streams of data at scale.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1787336874088\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>2. How does Kafka support machine learning?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Kafka continuously delivers data to ML pipelines, allowing models to process fresh events for real-time prediction, anomaly detection, recommendations, and other applications.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1787336894501\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>3. What is a Kafka topic?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>A <strong>Kafka topic<\/strong> is a logical stream where related events are published and stored, allowing consumers to subscribe to specific categories of data.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1787336908206\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>4. What are Kafka producers and consumers?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p><strong>Producers<\/strong> publish events to Kafka, while <strong>consumers<\/strong> subscribe to topics and read those events for processing or downstream applications.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1787336917581\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>5. Can Kafka be used for real-time predictions?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Yes. Kafka can deliver streaming events to feature-processing and model-serving systems, enabling ML applications to generate predictions from continuously arriving data.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1787336927690\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>6. Is Kafka a machine learning framework?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>No. Kafka is an event-streaming platform. It handles the movement and storage of streaming data while separate ML frameworks and model-serving systems handle machine learning tasks.<\/p>\n\n<\/div>\n<\/div>\n<div id=\"faq-question-1787336948619\" class=\"rank-math-list-item\">\n<h3 class=\"rank-math-question \"><strong>7. Why is Kafka useful for real-time ML?<\/strong><\/h3>\n<div class=\"rank-math-answer \">\n\n<p>Kafka provides scalable and reliable data streaming, allowing machine learning systems to access continuously arriving information and respond quickly to changing events and conditions.<\/p>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Machine learning applications increasingly need fresh data to make predictions, detect anomalies, and respond to changing conditions. Apache Kafka provides a distributed event-streaming platform that can continuously move large volumes of data between applications and ML systems. By connecting data producers, processing services, and machine learning models, Kafka helps organizations build responsive real-time AI pipelines.&nbsp;&nbsp; [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":137260,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[933],"tags":[],"views":"20","authorinfo":{"name":"HCL GUVI","url":"https:\/\/www.guvi.in\/blog\/author\/guvipr\/"},"thumbnailURL":"https:\/\/www.guvi.in\/blog\/wp-content\/uploads\/2026\/09\/Apache-Kafka-for-Real-Time-ML-Data-Streaming-300x116.webp","_links":{"self":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/134862"}],"collection":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/users\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/comments?post=134862"}],"version-history":[{"count":3,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/134862\/revisions"}],"predecessor-version":[{"id":137170,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/posts\/134862\/revisions\/137170"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media\/137260"}],"wp:attachment":[{"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/media?parent=134862"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/categories?post=134862"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.guvi.in\/blog\/wp-json\/wp\/v2\/tags?post=134862"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}