sanjay-kv · Oct 20, 2026 · Oct 20, 2026
@@ -4,6 +4,8 @@ authors: [Aditya-Singh-Rathore]
 sidebar_label: "Azure Data Factory — Build Your First ETL"
 tags: [azure-data-factory, adf, etl, data-pipeline, data-engineering, azure, blob-storage, adls, copy-activity, linked-service, dataset, trigger]
 date: 2026-05-06
+image: /img/blogs/adf-cover_image.png
+category: TUTORIAL
 
 description: Azure Data Factory is Microsoft's cloud-native ETL service — a visual, no-code platform for moving and transforming data at scale. This step-by-step guide walks you through building your first real pipeline in under 10 minutes, explaining every concept along the way.
 

@@ -1,5 +1,5 @@
 sanjay-kv:
-  name: Sanjay Viswanthan
+  name: Sanjay Viswanathan
   title: Founder at recode hive
   url: https://nomadbrains.dev
   image_url: https://avatars.githubusercontent.com/u/30715153?v=4

@@ -4,6 +4,8 @@ authors: [Aditya-Singh-Rathore]
 sidebar_label: "Azure Data Pipeline Cost Optimization"
 tags: [azure, cost-optimization, data-pipeline, adls-gen2, azure-synapse, apache-spark, azure-data-factory, delta-lake, data-engineering, cloud-cost]
 date: 2026-05-15
+image: /img/blogs/cost_optimzation_cover.png
+category: CLOUD COST
 
 description: Six costly mistakes in a 2GB Azure data pipeline drove a $4,200 monthly bill. Here's every mistake, root cause, and fix — with before-and-after numbers for each.
 

@@ -4,6 +4,8 @@ authors: [Aditya-Singh-Rathore]
 sidebar_label: "Azure Storage & ADLS Gen2: Complete Guide"
 tags: [azure-storage, blob-storage, adls-gen2, azure-data-lake, queue-storage, table-storage, file-storage, data-engineering, azure, big-data, medallion-architecture]
 date: 2026-05-06
+image: /img/blogs/azure-storage-cover.png
+category: AZURE
 
 description: Every Azure data pipeline needs a place to store data. But Azure gives you four different storage types and choosing the wrong one is easier than you think. This guide explains all four, shows how they work together in a real pipeline, and goes deep on ADLS Gen2, the storage layer that powers modern Azure data engineering.
 

@@ -4,6 +4,8 @@ authors: [Aditya-Singh-Rathore]
 sidebar_label: "Azure Synapse Analytics"
 tags: [azure-synapse-analytics, data-engineering, sql-pools, apache-spark, microsoft-fabric, data-warehouse, adls-gen2, azure, big-data, etl]
 date: 2026-05-06
+image: /img/blogs/azure-synapse-cover.png
+category: AZURE
 
 description: Azure Synapse Analytics is one of the most powerful tools in the Azure data stack. But in 2026, with Microsoft Fabric growing fast, the question isn't just "what is Synapse?" — it's "when should you still use it, and when should you move to Fabric?" Here's the honest answer.
 

@@ -4,6 +4,8 @@ authors: [Aditya-Singh-Rathore]
 sidebar_label: "Why We Rolled Back Our Kafka Pipeline to Batch After 6 Monthss"
 tags: [batch-processing, stream-processing, data-engineering, apache-kafka, apache-flink, apache-spark, data-pipeline, real-time, azure, medallion-architecture, data-architecture]
 date: 2026-05-06
+image: /img/blogs/batch-vs-stream-cover.png
+category: STREAMING
 
 description: Everyone talks about the benefits of streaming pipelines — real-time insights, millisecond latency, live dashboards. Nobody talks about what it actually costs you. I rebuilt a working batch pipeline as a streaming system. Here's what I learned the hard way.
 

@@ -4,6 +4,9 @@ authors: [Aditya-Singh-Rathore]
 sidebar_label: "Data Engineers vs MBAs as Business Analysts"
 tags: [data-engineering, career, business-analysis, data-driven, analytics, data-strategy, opinion, leadership]
 date: 2026-05-19
+image: /img/blogs/ba_de_cover_image.png
+category: CAREER
+featured: true
 
 description: MBAs learn to analyze businesses. Data engineers live inside the data those businesses actually run on. Here's why that difference matters more than any credential — and what both sides should learn from each other.
 

@@ -4,6 +4,8 @@ authors: [Aditya-Singh-Rathore]
 sidebar_label: "Delta lake Storage"
 tags: [deltalake,storage, Big Data, cloud, Data Engineering, fabric]
 date: 2026-05-01
+image: /img/blogs/delta-lake-logo.png
+category: STORAGE
 
 description: Delta Lake is an open-source storage framework that enables building a format agnostic Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, Hive, Snowflake, Google BigQuery, Athena, Redshift, Databricks, Azure Fabric and APIs for Scala, Java, Rust, and Python. With Delta Universal Format aka UniForm, you can read now Delta tables with Iceberg and Hudi clients.
 

@@ -4,6 +4,8 @@ authors: [Aditya-Singh-Rathore]
 sidebar_label: "Fabric Data Engineer"
 tags: [DP-700,azure, Big Data, cloud,certification,Data Engineering, fabric,experience]
 date: 2026-05-01
+image: /img/blogs/microsoft-certified-associate-badge.png
+category: CERTIFICATION
 
 description: "A comprehensive guide to clearing the Microsoft Fabric Data Engineer Associate (DP-700) certification. Learn the preparation strategy, key concepts, hands-on practice tips, and real exam experience from someone who passed it. Discover why Lakehouse, Delta Tables, Dataflows, and DirectLake mode matter, and how to approach scenario-based questions effectively."
 

@@ -4,6 +4,8 @@ sidebar_label: GitHub Copilot Coding Agent
 authors: [sanjay-kv]
 tags: [GitHub, SEO, Coding agent,Copilot, AI, Automation]
 date: 2025-07-04
+image: /img/blogs/06-github-agent.png
+category: AI TOOLS
 
 description: An overview of the GitHub Copilot Coding Agent, an AI-powered tool that automates software engineering tasks by taking GitHub Issues as input to write code, run tests, and create pull requests.
 draft: false

@@ -4,6 +4,8 @@ authors: [sanjay-kv]
 sidebar_label: "GitHub Copilot CLI"
 tags: [GitHub, CLI, tech, updates, Copilot, Coding, Assistant]
 date: 2025-09-17
+image: /img/blogs/github-cli-agent-cover.png
+category: GITHUB
 
 description: GitHub bought power of GitHub Copilot coding agent directly to your terminal, with GitHub Copilot CLI, you can work locally and synchronously with an AI agent.
 

@@ -4,6 +4,7 @@ description: "Google redesigned its Workspace icons in 2026, dropping the four-c
 authors: [sanjay-kv]
 tags: [google-workspace, ui-design, ux]
 date: 2026-05-21
+category: DESIGN
 image: /img/blogs/cover-google-icon.jpg
 ---
 

@@ -4,6 +4,8 @@ authors: [Aditya-Singh-Rathore]
 sidebar_label: "Lakehouse vs Data Warehouse"
 tags: [lakehouse, data-warehouse, data-engineering, big-data, delta-lake, spark, analytics, snowflake, databricks]
 date: 2026-05-01
+image: /img/blogs/datalake_vs_warehouse.png
+category: ARCHITECTURE
 
 description: Lakehouse and Data Warehouse are two of the most debated architectures in modern data engineering. This article breaks down how they differ, where each fits in the data lifecycle, and how to choose between them, without the platform bias.
 

@@ -4,6 +4,8 @@ authors: [Aditya-Singh-Rathore]
 sidebar_label: "Medallion Architecture Explained"
 tags: [medallion-architecture, data-engineering, bronze-silver-gold, data-pipeline, delta-lake, spark, databricks, microsoft-fabric, data-quality]
 date: 2026-05-07
+image: /img/blogs/medallion_architecture.png
+category: ARCHITECTURE
 
 description: Most data pipelines don't fail because of bad technology. They fail because raw data flows directly into reports with no checkpoints, no validation, and no clear ownership. Medallion Architecture fixes exactly this — here's how it works, why it matters, and how to implement it in practice.
 

@@ -4,6 +4,8 @@ authors: [Aditya-Singh-Rathore]
 sidebar_label: "Microsoft Purview Data Catalog"
 tags: [azure, microsoft-purview, data-catalog, data-governance, onboarding, data-lineage, metadata, data-engineering, azure-data-factory, delta-lake]
 date: 2026-05-19
+image: /img/blogs/purview_cover.png
+category: DATA GOVERNANCE
 
 description: "Learn how Microsoft Purview Data Catalog improves data discovery, lineage, ownership, and governance. This practical case study shows how we reduced data engineer onboarding from 2 weeks to 3 days — including the configuration that made it work."
 

@@ -4,6 +4,8 @@ authors: [Aditya-Singh-Rathore]
 sidebar_label: "Microsoft Fabric"
 tags: [microsoft-fabric, onelake, data-engineering, lakehouse, delta-lake, big-data, cloud, power-bi]
 date: 2026-05-01
+image: /img/blogs/microsoft-farbic-blog-image.png
+category: MICROSOFT FABRIC
 
 description: Microsoft Fabric is a unified analytics platform that brings together data engineering, data science, real-time analytics, and business intelligence under a single roof — all built on OneLake. Learn how Fabric is architected, how data flows through it, and why it matters for modern data teams.
 

@@ -4,6 +4,8 @@ authors: [Aditya-Singh-Rathore]
 sidebar_label: "N8N AI Workflows Explained"
 tags: [N8N, AI Automation, Workflow Automation, No-Code, Integration, Machine Learning, API Integration]
 date: 2025-09-17
+image: /img/blogs/n8n-logo.png
+category: AUTOMATION
 
 description: N8N revolutionizes automation by integrating AI capabilities into visual workflows. Learn how to build intelligent automation pipelines that can process data, make decisions, and interact with multiple services seamlessly.
 

@@ -4,6 +4,8 @@ authors: [Aditya-Singh-Rathore]
 sidebar_label: "How Netflix Handles 2 Trillion Events Every Day"
 tags: [netflix, data-engineering, kafka, apache-flink, real-time, event-streaming, data-pipeline, cassandra, avro, iceberg, keystone]
 date: 2026-05-04
+image: /img/blogs/thumbnail.png
+category: CASE STUDY
 
 description: Every click, pause, search, and scroll on Netflix generates an event. With 300 million subscribers across 190 countries, Netflix processes 2 trillion events every single day through a pipeline called Keystone. Here's a deep dive into how Kafka, Flink, Cassandra, and Iceberg make it all work in real time.
 

@@ -4,6 +4,8 @@ authors: [Aditya-Singh-Rathore]
 sidebar_label: "OpenAI AgentKit Explained"
 tags: [OpenAI, AgentKit, AI Agents, Agent Builder, Agentic AI, Developer Tools]
 date: 2025-10-15
+image: /img/blogs/Agent_Builder.png
+category: AI AGENTS
 
 description: OpenAI's AgentKit revolutionizes how developers build AI agents with its visual Agent Builder, integrated ChatKit, comprehensive evaluation tools, and seamless third-party integrations. Learn how this complete toolkit takes agents from prototype to production with minimal friction.
 

@@ -4,6 +4,8 @@ authors: [Aditya-Singh-Rathore, sanjay-kv]
 sidebar_label: "How SSO Works - Case Study"
 tags: [sso, single-sign-on, authentication, identity-provider, oauth, openid-connect, saml, security, web]
 date: 2026-05-04
+image: /img/blogs/sso_cover.png
+category: SECURITY
 
 description: SSO lets you log into dozens of apps with a single set of credentials. But how does it actually work under the hood? This beginner-friendly guide walks through the full flow — from clicking "Sign in with Google" to getting access — step by step.
 

@@ -4,6 +4,8 @@ authors: [Aditya-Singh-Rathore,sanjay-kv]
 sidebar_label: "Spark Architecture Explained"
 tags: [Apache Spark, Spark Architecture, Big Data, Distributed Computing, Data Engineering]
 date: 2025-08-22
+image: /img/blogs/07-spark-blog-banner.png
+category: ARCHITECTURE
 
 description: Apache Spark is a fast, open-source big data framework that leverages in-memory computing for high performance. Its architecture powers scalable distributed processing across clusters, making it essential for analytics and machine learning.
 

@@ -4,6 +4,8 @@ authors: [Aditya-Singh-Rathore]
 sidebar_label: "PySpark Optimization Techniques"
 tags: [pyspark, spark, optimization, data-engineering, delta-lake, performance, partitioning, joins, caching, cluster-config]
 date: 2026-05-16
+image: /img/blogs/pyspark_optimization_cover.png
+category: PERFORMANCE
 
 description: Six PySpark mistakes that silently kill pipeline performance and how to fix every one of them. Covering partitioning, shuffle tuning, caching, join strategies, UDFs, predicate pushdown, and cluster config. Explained from scratch.
 

@@ -7,6 +7,8 @@ tags:
   - AI
 
 date: 2025-06-05 10:32:00
+image: /img/blogs/04-ux-job-design.png
+category: CAREER
 description: Are you passionate about design and dreaming of a career in it? Or maybe you’re already in the design space and looking to pivot into UI/UX? .
 draft: false
 # meta:

@@ -161,6 +161,15 @@ We welcome contributions! Follow these steps to get started.
 
    > Tip: If your branch falls behind main, you can fetch and merge updates from upstream again before pushing.
 
+## Blog banners
+
+Every blog post needs a banner so it looks right on the Blogs page.
+
+- Set it in the post's front matter with `image: /img/blogs/your-banner.png`, and add a `category:` (a short label such as ARCHITECTURE or TUTORIAL).
+- Banners should be **1600 x 900 pixels (16:9)**, with **one big headline of 3 to 5 words**. The Blogs page shows the whole banner without cropping, so a banner in another shape gets empty bars on its sides.
+- Keep important text away from the edges and keep the file small (under 300 KB where you can).
+- Add `featured: true` only to the post the editors pick for the Featured spot.
+
 ## Formatting
 
 To ensure consistent code style and catch errors before committing, please follow these steps:

@@ -91,7 +91,7 @@ Replace my name with your name and respective website links of yours.
 
 ```yaml
 sanjay-kv:
-  name: Sanjay Viswanthan
+  name: Sanjay Viswanathan
   title: Founder at recode hive
   url: https://nomadbrains.dev
   image_url: https://avatars.githubusercontent.com/u/30715153?v=4

@@ -297,6 +297,8 @@ const config: Config = {
     "./plugins/learn-words",
     // Publish dates for the Broadcasts page from YouTube public RSS feeds (no API key)
     "./plugins/broadcasts-feed",
+    // Exposes every blog post to the /blogs listing page
+    "./plugins/blog-listing",
     // Real GitHub Discussions, fetched at build time (token never reaches the browser)
     "./plugins/github-discussions",
     [

@@ -0,0 +1,49 @@
+// Exposes every blog post to the /blogs listing page as global data, so the page never needs a
+// hardcoded post list. Reads the blog plugin's content after it has loaded.
+const path = require('path');
+
+function githubOf(author) {
+  const gh = author.socials && author.socials.github;
+  if (gh) return `https://github.com/${gh}`;
+  if (author.url && /github\.com/i.test(author.url)) return author.url;
+  return undefined;
+}
+
+module.exports = function blogListingPlugin() {
+  return {
+    name: 'blog-listing',
+    async allContentLoaded({ allContent, actions }) {
+      const blog = allContent['docusaurus-plugin-content-blog'];
+      const posts = (blog && blog.default && blog.default.blogPosts) || [];
+
+      const out = posts
+        .filter((p) => !p.metadata.unlisted)
+        .map((p) => {
+          const m = p.metadata;
+          const fm = m.frontMatter || {};
+          return {
+            id: p.id,
+            folder: path.basename(path.dirname(m.source.replace(/^@site[\\/]/, ''))),
+            title: m.title,
+            permalink: m.permalink,
+            description: m.description || '',
+            date: m.date instanceof Date ? m.date.toISOString() : String(m.date),
+            readingTime: Math.max(1, Math.ceil(m.readingTime || 1)),
+            image: typeof fm.image === 'string' ? fm.image : undefined,
+            category: typeof fm.category === 'string' ? fm.category : undefined,
+            featured: fm.featured === true,
+            tags: (m.tags || []).map((t) => ({ label: t.label, permalink: t.permalink })),
+            authors: (m.authors || []).map((a) => ({
+              key: a.key || a.name,
+              name: a.name,
+              imageURL: a.imageURL,
+              github: githubOf(a),
+            })),
+          };
+        })
+        .sort((a, b) => new Date(b.date).getTime() - new Date(a.date).getTime());
+
+      actions.setGlobalData({ posts: out });
+    },
+  };
+};
@@ -0,0 +1,102 @@
+import React, { useMemo } from 'react';
+import { Search } from 'lucide-react';
+import { TOPICS, uniqueAuthors, type Post } from './blogData';
+import { AuthorAvatar } from './PostCard';
+import styles from './blogsPage.module.css';
+
+const COLS = 32;
+const ROWS = 5;
+
+// Seeded so server and client agree. Levels rise toward the right.
+const CELLS = Array.from({ length: COLS * ROWS }, (_, i) => {
+  const col = Math.floor(i / ROWS);
+  const row = i % ROWS;
+  const noise = Math.abs(Math.sin((col + 1) * 12.9898 + (row + 3) * 78.233) * 43758.5453) % 1;
+  const base = (col / (COLS - 1)) * 3.2;
+  const level = Math.max(0, Math.min(4, Math.round(base + noise * 1.8 - 0.6)));
+  return { level, pulse: i % 9 === 0 && level >= 3, delay: (i % 7) * 0.45 };
+});
+
+function GithubTile() {
+  return (
+    <a className={styles.ghTile} href="https://github.com/recodehive" target="_blank" rel="noopener noreferrer">
+      <div className={styles.ghGrid} aria-hidden="true">
+        {CELLS.map((c, i) => (
+          <i key={i} className={`${styles[`lv${c.level}` as 'lv0']} ${c.pulse ? styles.cellPulse : ''}`} style={c.pulse ? { animationDelay: `${c.delay}s` } : undefined} />
+        ))}
+      </div>
+      <p className={styles.ghLine}>
+        <span className={styles.liveDot} aria-hidden="true" />
+        github.com/recodehive
+        <span className={styles.srOnly}> (opens in a new tab)</span>
+      </p>
+    </a>
+  );
+}
+
+function AuthorsStrip({ posts }: { posts: Post[] }) {
+  const authors = useMemo(() => uniqueAuthors(posts), [posts]);
+  if (authors.length === 0) return null;
+  return (
+    <div className={styles.strip}>
+      <ul className={styles.avatarRow} aria-label="Blog authors">
+        {authors.map((a) => {
+          const face = <AuthorAvatar author={a} size={30} />;
+          return (
+            <li key={a.key} className={styles.av}>
+              {a.github ? (
+                <a href={a.github} target="_blank" rel="noopener noreferrer" aria-label={`${a.name} on GitHub (opens in a new tab)`}>{face}</a>
+              ) : (
+                <span tabIndex={0} aria-label={a.name}>{face}</span>
+              )}
+              <span className={styles.tip} role="tooltip">{a.name}</span>
+            </li>
+          );
+        })}
+      </ul>
+      <p className={styles.stripText}>
+        <strong>{authors.length} authors</strong> have written {posts.length} posts
+      </p>
+      <a className={styles.stripLink} href="https://www.recodehive.com/docs/Getting-Started" target="_blank" rel="noopener noreferrer">
+        Become one {'→'}<span className={styles.srOnly}> (opens in a new tab)</span>
+      </a>
+    </div>
+  );
+}
+
+interface Props {
+  posts: Post[];
+  query: string;
+  onQuery: (q: string) => void;
+  topic: string;
+  onTopic: (label: string) => void;
+}
+
+export default function Hero({ posts, query, onQuery, topic, onTopic }: Props) {
+  return (
+    <header className={styles.hero}>
+      <div className={styles.heroGridBg} aria-hidden="true" />
+      <div className={styles.heroText}>
+        <p className={styles.eyebrow}>{'✦'} THE RECODE HIVE BLOG</p>
+        <h1 className={styles.h1}>Engineering <span className={styles.accent}>uptime</span></h1>
+        <p className={styles.subtitle}>blog by recode community.</p>
+        <label className={styles.search}>
+          <Search size={18} aria-hidden="true" />
+          <span className={styles.srOnly}>Search tutorials, tools, or technologies</span>
+          <input type="search" placeholder="Search tutorials, tools, or technologies" value={query} onChange={(e) => onQuery(e.target.value)} />
+        </label>
+        <div className={styles.chips} role="group" aria-label="Filter by topic">
+          {['All', ...TOPICS.map((t) => t.label)].map((label) => (
+            <button key={label} type="button" aria-pressed={topic === label} className={`${styles.chip} ${topic === label ? styles.chipOn : ''}`} onClick={() => onTopic(label)}>
+              {label}
+            </button>
+          ))}
+        </div>
+      </div>
+      <div className={styles.heroRight}>
+        <GithubTile />
+        <AuthorsStrip posts={posts} />
+      </div>
+    </header>
+  );
+}