diff --git a/blog/articles/data-quality-validation-pipelines.php b/blog/articles/data-quality-validation-pipelines.php
index b3d3cfc..51b7b16 100644
--- a/blog/articles/data-quality-validation-pipelines.php
+++ b/blog/articles/data-quality-validation-pipelines.php
@@ -106,43 +106,8 @@ $read_time = 9;
             </div>
             <header class="article-header">
                 <h1>A UK Guide to Advanced Statistical Validation for Ensuring Data Accuracy</h1>
-
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-        {
-            "@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>
+                <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>
                 <div class="article-author">
                     <div class="author-info">
                         <span>By <?php echo htmlspecialchars($article_author); ?></span>
diff --git a/blog/articles/real-time-analytics-streaming-data.php b/blog/articles/real-time-analytics-streaming-data.php
index 205b942..4cbd4ab 100644
--- a/blog/articles/real-time-analytics-streaming-data.php
+++ b/blog/articles/real-time-analytics-streaming-data.php
@@ -96,64 +96,16 @@ $breadcrumbs = [
                 <span class="read-time">9 min read</span>
             </div>
 <header class="article-header">
-<h1>Best Streaming Data Analytics Platforms: A 2026 UK Comparison</h1>
-                <p class="article-lead">Struggling to choose the right platform for your streaming data? This guide compares the top real-time analytics platforms for UK businesses in 2026, covering features, performance, and costs.</p>
+<h1>Choosing the Best Streaming Data Analytics Platform: A 2024 UK Comparison</h1>
+                <p class="article-lead"><?php echo htmlspecialchars($article_description); ?></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>
                     
-                    <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>
+                    <p>The demand for real-time analytics is driven by several key 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>
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index dd19e16..3da52d9 100644
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   <url><loc>https://ukdataservices.co.uk/data-scraping-services/index</loc><lastmod>2026-03-02</lastmod><changefreq>weekly</changefreq></url>
-  <url><loc>https://ukdataservices.co.uk/faq-enhanced</loc><lastmod>2026-03-02</lastmod><changefreq>weekly</changefreq></url>
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   <url><loc>https://ukdataservices.co.uk/project-types</loc><lastmod>2026-03-02</lastmod><changefreq>weekly</changefreq></url>
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   <url><loc>https://ukdataservices.co.uk/quote</loc><lastmod>2026-03-02</lastmod><changefreq>weekly</changefreq></url>
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   <url><loc>https://ukdataservices.co.uk/services/csharp-development-services</loc><lastmod>2026-03-02</lastmod><changefreq>weekly</changefreq></url>
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