diff --git a/blog/articles/data-quality-validation-pipelines.php b/blog/articles/data-quality-validation-pipelines.php
index 51b7b16..b3d3cfc 100644
--- a/blog/articles/data-quality-validation-pipelines.php
+++ b/blog/articles/data-quality-validation-pipelines.php
@@ -106,8 +106,43 @@ $read_time = 9;
             </div>
             <header class="article-header">
                 <h1>A UK Guide to Advanced Statistical Validation for Ensuring Data Accuracy</h1>
-                <p class="article-lead"><?php echo htmlspecialchars($article_description); ?></p>
-                <p>At its core, <strong>advanced statistical validation is the critical process that ensures accuracy</strong> in large datasets. For UK businesses relying on data for decision-making, moving beyond basic checks to implement robust statistical tests—like outlier detection, distribution analysis, and regression testing—is non-negotiable. This guide explores the practical application of these methods within a data quality pipeline, transforming raw data into a reliable, high-integrity asset.</p>
+
+<script type="application/ld+json">
+{
+    "@context": "https://schema.org",
+    "@type": "FAQPage",
+    "mainEntity": [
+        {
+            "@type": "Question",
+            "name": "What is the difference between data validation and data verification?",
+            "acceptedAnswer": {
+                "@type": "Answer",
+                "text": "Data verification confirms that data has been transcribed correctly from one source to another (e.g., no typos), while data validation ensures that the data is logical, accurate, and useful. Statistical validation is an advanced form of data validation."
+            }
+        },
+        {
+            "@type": "Question",
+            "name": "What are common statistical validation techniques?",
+            "acceptedAnswer": {
+                "@type": "Answer",
+                "text": "Common techniques include hypothesis testing (e.g., t-tests, chi-squared tests), regression analysis to check relationships, outlier detection using methods like Z-scores or Interquartile Range (IQR), and checking for normal distribution."
+            }
+        },
+        {
+            "@type": "Question",
+            "name": "How does statistical validation help with GDPR compliance in the UK?",
+            "acceptedAnswer": {
+                "@type": "Answer",
+                "text": "GDPR's 'accuracy principle' (Article 5(1)(d)) requires personal data to be accurate and kept up to date. Statistical validation provides a robust, auditable framework to prove that you are taking active steps to ensure and maintain data accuracy, supporting your compliance efforts."
+            }
+        }
+    ]
+}
+</script>
+                <p class="article-lead">In today's data-driven economy, the integrity of your data is not just a technical detail—it's a critical business asset. Poor data quality leads to flawed strategies and missed opportunities. This is where advanced statistical validation becomes essential to guarantee accuracy.</p>
+
+                <h2>Why Statistical Validation is a Business Imperative</h2>
+                <p>While basic data cleaning catches simple errors, it often misses subtle issues like distributional shifts, hidden biases, or sophisticated outliers. <strong>Advanced statistical validation is the critical process that ensures accuracy</strong> by applying rigorous mathematical checks. For any UK business leveraging <a href="/services/data-analytics-services.php">data analytics services</a>, this process is the difference between confident strategy and costly guesswork. It builds trust in your data and the decisions they inform.</p>e outlier detection, distribution analysis, and regression testing—is non-negotiable. This guide explores the practical application of these methods within a data quality pipeline, transforming raw data into a reliable, high-integrity asset.</p>
                 <div class="article-author">
                     <div class="author-info">
                         <span>By <?php echo htmlspecialchars($article_author); ?></span>
diff --git a/blog/articles/python-data-pipeline-tools-2025.php b/blog/articles/python-data-pipeline-tools-2025.php
index c61f3ed..411aad4 100644
--- a/blog/articles/python-data-pipeline-tools-2025.php
+++ b/blog/articles/python-data-pipeline-tools-2025.php
@@ -96,15 +96,16 @@ $breadcrumbs = [
                 <span class="read-time">6 min read</span>
             </div>
 <header class="article-header">
-<h1><?php echo htmlspecialchars($article_title); ?></h1>
+<h1>Airflow vs Prefect vs Dagster: Which Python Orchestrator Wins in 2026?</h1>
                 <p class="article-lead"><?php echo htmlspecialchars($article_description); ?></p>
             </header>
             
             <div class="article-content">
                 <section>
                     <h2>The Evolution of Python Data Pipeline Tools</h2>
-                    <p>The Python data engineering ecosystem has matured significantly in 2025, with new tools emerging and established frameworks evolving to meet the demands of modern data infrastructure. As organisations handle increasingly complex data workflows, the choice of pipeline orchestration tools has become critical for scalability, maintainability, and operational efficiency.</p>
+                    <p>The Python data engineering ecosystem has matured significantly in 2026, with new tools emerging and established frameworks evolving to meet the demands of modern data infrastructure. As organisations handle increasingly complex data workflows, the choice of pipeline orchestration tools has become critical for scalability, maintainability, and operational efficiency.</p>
                     
+                    <p>This article provides a head-to-head comparison of the leading Python data orchestration tools: Apache Airflow, Prefect, Dagster, and the rapidly growing Flyte. We'll analyse their core concepts, developer experience, multi-cloud support, and pricing to help you choose the right framework for your data engineering needs.</p>
                     <p>Key trends shaping the data pipeline landscape:</p>
                     <ul>
                         <li><strong>Cloud-Native Architecture:</strong> Tools designed specifically for cloud environments and containerised deployments</li>
diff --git a/blog/articles/real-time-analytics-streaming-data.php b/blog/articles/real-time-analytics-streaming-data.php
index 4cbd4ab..205b942 100644
--- a/blog/articles/real-time-analytics-streaming-data.php
+++ b/blog/articles/real-time-analytics-streaming-data.php
@@ -96,16 +96,64 @@ $breadcrumbs = [
                 <span class="read-time">9 min read</span>
             </div>
 <header class="article-header">
-<h1>Choosing the Best Streaming Data Analytics Platform: A 2024 UK Comparison</h1>
-                <p class="article-lead"><?php echo htmlspecialchars($article_description); ?></p>
+<h1>Best Streaming Data Analytics Platforms: A 2026 UK Comparison</h1>
+                <p class="article-lead">Struggling to choose the right platform for your streaming data? This guide compares the top real-time analytics platforms for UK businesses in 2026, covering features, performance, and costs.</p>
             </header>
             
             <div class="article-content">
                 <section>
                     <h2>Why Real-Time Analytics is a Game-Changer</h2>
                     <p>In today's fast-paced digital economy, the ability to analyse streaming data in real-time is no longer a luxury—it's a competitive necessity. Businesses need instant insights from continuous data flows to make immediate decisions, from detecting financial fraud to personalising user experiences as they happen.</p>
+                    <p>The demand for real-time analytics is driven by several key factors, including the rise of IoT devices, the need for dynamic pricing, and the expectation of hyper-personalised customer experiences. But with so many tools available, which one is right for your UK business?</p>
+                </section>
+                <section>
+                    <h2>Top Streaming Analytics Platforms Compared for 2026</h2>
+                    <p>To help you decide, we've compared four of the leading platforms optimised for streaming data. We'll look at their core strengths, ideal use cases, and key considerations for UK companies.</p>
                     
-                    <p>The demand for real-time analytics is driven by several key use cases:</p>
+                    <h3>1. Apache Kafka & Kafka Streams</h3>
+                    <ul>
+                        <li><strong>Best for:</strong> High-throughput, durable event streaming backbones.</li>
+                        <li><strong>Core Strength:</strong> A distributed streaming platform that acts as a central nervous system for data. It's incredibly scalable and fault-tolerant. Kafka Streams provides a lightweight library for building real-time applications on top of Kafka topics.</li>
+                        <li><strong>Considerations:</strong> Requires significant in-house expertise to manage and scale effectively. It's a foundational piece, often used with other processing frameworks like Flink or Spark.</li>
+                    </ul>
+                    
+                    <h3>2. Apache Flink</h3>
+                    <ul>
+                        <li><strong>Best for:</strong> Complex event processing (CEP) and true, low-latency stream processing.</li>
+                        <li><strong>Core Strength:</strong> A powerful, stateful stream processing framework. Flink excels at handling out-of-order events and provides exactly-once processing guarantees, crucial for financial applications.</li>
+                        <li><strong>Considerations:</strong> Can have a steeper learning curve than other options. It's a processing engine, not a full platform, and is often paired with Kafka for data ingestion.</li>
+                    </ul>
+                    
+                    <h3>3. Amazon Kinesis</h3>
+                    <ul>
+                        <li><strong>Best for:</strong> Businesses heavily invested in the AWS ecosystem looking for a managed service.</li>
+                        <li><strong>Core Strength:</strong> A fully managed service that makes it easy to collect, process, and analyse real-time streaming data. Kinesis Data Analytics allows you to use standard SQL to query streaming data.</li>
+                        <li><strong>Considerations:</strong> Can lead to vendor lock-in. Cost can escalate with high data volumes, so careful monitoring is needed.</li>
+                    </ul>
+                    
+                    <h3>4. Google Cloud Dataflow</h3>
+                    <ul>
+                        <li><strong>Best for:</strong> Unified batch and stream data processing with autoscaling.</li>
+                        <li><strong>Core Strength:</strong> A managed service built on Apache Beam, providing a unified programming model for both batch and streaming jobs. Its serverless, no-ops approach and powerful autoscaling are major benefits.</li>
+                        <li><strong>Considerations:</strong> Part of the Google Cloud Platform (GCP) ecosystem, which may not suit all businesses. Pricing is based on resource consumption, which can be complex to predict.</li>
+                    </ul>
+                </section>
+                <section>
+                    <h2>How UK Data Services Can Help</h2>
+                    <p>Choosing and implementing the right streaming analytics platform is a complex task. As a leading UK data agency, our experts can help you design and build a robust, scalable, and GDPR-compliant data architecture tailored to your specific needs. <a href="/contact.php">Contact us today</a> for a free consultation on your real-time data project.</p>
+                </section>
+                <section>
+                    <h2>Frequently Asked Questions (FAQ)</h2>
+                    
+                    <h3>What is the difference between real-time data streaming and batch processing?</h3>
+                    <p>Real-time data streaming involves processing data continuously as it's generated, enabling immediate insights and actions. Batch processing, in contrast, collects data over a period (e.g., hours or days) and processes it in large chunks, which is suitable for non-urgent tasks like daily reporting.</p>
+                    
+                    <h3>Which platform is best for advanced analytics for stream performance?</h3>
+                    <p>For advanced analytics and complex event processing, Apache Flink is often considered the top choice due to its stateful processing capabilities and low latency. However, the 'best' platform depends on your specific performance requirements, existing infrastructure, and team expertise.</p>
+                    
+                    <h3>Are open-source platforms like Kafka and Flink suitable for enterprise use?</h3>
+                    <p>Absolutely. Both Apache Kafka and Apache Flink are widely used in large enterprises, including tech giants and financial institutions. While they require more management overhead than managed services, they offer unparalleled flexibility, performance, and cost-effectiveness at scale.</p>
+                </section>ey use cases:</p>
                     <ul>
                         <li><strong>Customer Experience:</strong> Personalising user interactions on the fly.</li>
                         <li><strong>Fraud Detection:</strong> Identifying suspicious transactions in milliseconds.</li>
diff --git a/index.php b/index.php
index 8908198..9812be5 100644
--- a/index.php
+++ b/index.php
@@ -15,7 +15,7 @@ header('Strict-Transport-Security: max-age=31536000; includeSubDomains');
 header('Content-Security-Policy: default-src \'self\'; script-src \'self\' \'unsafe-inline\' https://cdnjs.cloudflare.com https://www.googletagmanager.com https://www.google-analytics.com https://www.clarity.ms https://www.google.com https://www.gstatic.com; style-src \'self\' \'unsafe-inline\' https://fonts.googleapis.com; font-src \'self\' https://fonts.gstatic.com; img-src \'self\' data: https://www.google-analytics.com; connect-src \'self\' https://www.google-analytics.com https://analytics.google.com https://region1.google-analytics.com https://www.google.com; frame-src https://www.google.com;');
 
 // SEO and performance optimizations
-$page_title = "Web Scraping Services UK | GDPR Compliant | UK Data Services";
+$page_title = "UK Web Scraping Services | Custom & GDPR Compliant Data";
 $page_description = "UK's leading web and data scraping company. We deliver 99.8% accurate data, fully GDPR compliant. Get a free, no-obligation quote for your project today.";
 $canonical_url = "https://ukdataservices.co.uk/";
 $keywords = "web scraping services UK, data analytics London, web scraping UK, data extraction services, business intelligence, competitive analysis, price monitoring, data analytics Manchester, market research, web data mining, GDPR compliant scraping, enterprise data solutions, automated data collection UK";
diff --git a/price-monitoring-services.php b/price-monitoring-services.php
new file mode 100644
index 0000000..178788b
--- /dev/null
+++ b/price-monitoring-services.php
@@ -0,0 +1,91 @@
+<?php
+// Enhanced security headers
+// Session for CSRF token
+ini_set('session.cookie_samesite', 'Lax');
+ini_set('session.cookie_httponly', '1');
+ini_set('session.cookie_secure', '1');
+session_start();
+
+if (!isset($_SESSION['csrf_token'])) {
+    $_SESSION['csrf_token'] = bin2hex(random_bytes(32));
+}
+header('Strict-Transport-Security: max-age=31536000; includeSubDomains');
+header('Content-Security-Policy: default-src \'self\'; script-src \'self\' \'unsafe-inline\' https://cdnjs.cloudflare.com https://www.googletagmanager.com https://www.google-analytics.com https://www.clarity.ms https://www.google.com https://www.gstatic.com; style-src \'self\' \'unsafe-inline\' https://fonts.googleapis.com; font-src \'self\' https://fonts.gstatic.com; img-src \'self\' data: https://www.google-analytics.com; connect-src \'self\' https://www.google-analytics.com https://analytics.google.com https://region1.google-analytics.com https://www.google.com; frame-src https://www.google.com;');
+
+// SEO and performance optimizations
+$page_title = "Competitor Price Monitoring Services UK | UK Data Services";
+$page_description = "Automate competitor price tracking with our UK-based price monitoring services. Get accurate, real-time pricing data to optimise your strategy. Free quote.";
+$canonical_url = "https://ukdataservices.co.uk/price-monitoring-services.php";
+$keywords = "price monitoring services, competitor price tracking, automated pricing data, dynamic pricing, e-commerce price scraping, UK price monitoring";
+$author = "UK Data Services";
+$og_image = "https://ukdataservices.co.uk/assets/images/ukds-main-logo.png";
+$twitter_card_image = "https://ukdataservices.co.uk/assets/images/ukds-main-logo.png";
+?>
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title><?php echo htmlspecialchars($page_title); ?></title>
+    <meta name="description" content="<?php echo htmlspecialchars($page_description); ?>" />
+    <meta name="keywords" content="<?php echo htmlspecialchars($keywords); ?>">
+    <meta name="author" content="<?php echo htmlspecialchars($author); ?>">
+    <meta name="robots" content="index, follow">
+    <link rel="canonical" href="<?php echo htmlspecialchars($canonical_url); ?>">
+    
+    <!-- Open Graph / Social Media -->
+    <meta property="og:type" content="website">
+    <meta property="og:url" content="<?php echo htmlspecialchars($canonical_url); ?>">
+    <meta property="og:title" content="<?php echo htmlspecialchars($page_title); ?>">
+    <meta property="og:description" content="<?php echo htmlspecialchars($page_description); ?>">
+    <meta property="og:image" content="<?php echo htmlspecialchars($og_image); ?>">
+    
+    <!-- Twitter Card -->
+    <meta name="twitter:card" content="summary_large_image">
+    <meta name="twitter:url" content="<?php echo htmlspecialchars($canonical_url); ?>">
+    <meta name="twitter:title" content="<?php echo htmlspecialchars($page_title); ?>">
+    <meta name="twitter:description" content="<?php echo htmlspecialchars($page_description); ?>">
+    <meta name="twitter:image" content="<?php echo htmlspecialchars($twitter_card_image); ?>">
+    
+    <!-- Favicon and other head elements from main template -->
+    <link rel="icon" type="image/svg+xml" href="/assets/images/favicon.svg">
+
+</head>
+<body>
+    <header>
+        <!-- Main site header here -->
+    </header>
+
+    <main>
+        <section class="hero">
+            <h1>Automated Competitor Price Monitoring Services</h1>
+            <p>Gain a competitive edge with real-time, accurate pricing data from any e-commerce site or marketplace. Our automated price monitoring service gives you the insights to optimise your pricing strategy, maximise profits, and protect your brand.</p>
+        </section>
+
+        <section>
+            <h2>Stay Ahead with Real-Time Pricing Intelligence</h2>
+            <p>In a dynamic market, manual price checks are slow and inefficient. We provide a fully managed data service that automatically scrapes competitor websites, delivering structured pricing data on your schedule (daily, hourly, or on-demand).</p>
+            <ul>
+                <li>Track unlimited products and competitors.</li>
+                <li>Monitor price changes, stock availability, and promotions.</li>
+                <li>Analyse historical pricing trends.</li>
+                <li>Receive data in CSV, JSON, or via API.</li>
+            </ul>
+        </section>
+
+        <section>
+            <h2>How Our Price Tracking Service Works</h2>
+            <ol>
+                <li><strong>Consultation:</strong> We work with you to identify target competitors and the specific data points you need.</li>
+                <li><strong>Scraper Development:</strong> Our UK-based team builds custom scrapers for each target website.</li>
+                <li><strong>Data Extraction & QA:</strong> We run the scrapers at your desired frequency, and our QA process ensures data is 99.8%+ accurate.</li>
+                <li><strong>Delivery:</strong> You receive clean, structured data in your preferred format, ready for analysis.</li>
+            </ol>
+        </section>
+    </main>
+
+    <footer>
+        <!-- Main site footer here -->
+    </footer>
+</body>
+</html>
\ No newline at end of file
diff --git a/sitemap.xml b/sitemap.xml
index f7cae8e..dd19e16 100644
--- a/sitemap.xml
+++ b/sitemap.xml
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