

-- ---------------------------------------------
-- 5. ABOUT PAGE
-- ---------------------------------------------
INSERT INTO `pages` (`title`, `slug`, `content`, `meta_title`, `meta_description`, `is_published`, `order`, `created_at`, `updated_at`) VALUES
('About', 'about', '<h2>About Tirumalarao Naidu</h2>\n<p>I am an internationally recognized technology executive and data engineering leader with over 19 years of distinguished experience across the U.S., South Africa, and India.</p>\n\n<h3>Current Role</h3>\n<p>Currently serving as the Manager of Customer Technology Data at MARTA (Metropolitan Atlanta Rapid Transit Authority), I spearhead regionally critical data systems that enhance equitable access to public transportation for millions.</p>\n\n<h3>Expertise</h3>\n<ul>\n<li>Data Engineering & Architecture</li>\n<li>Cloud Infrastructure (AWS, Azure, GCP)</li>\n<li>Enterprise Digital Transformation</li>\n<li>Public Transit Technology Systems</li>\n<li>AI/ML Integration</li>\n<li>Agile & Scaled Agile Leadership</li>\n</ul>\n\n<h3>Education</h3>\n<ul>\n<li>MBA - Henley Business School (2020)</li>\n<li>B.E. Electronics & Communications - Anna University (2005)</li>\n</ul>\n\n<h3>Memberships</h3>\n<ul>\n<li>IEEE (Senior Member)</li>\n<li>IET (Fellow)</li>\n<li>Institute of IT Professionals South Africa (IITPSA)</li>\n<li>American Public Transportation Association (APTA)</li>\n</ul>', 'About Tirumalarao Naidu - Technology Executive', 'Learn about Tirumalarao Naidu, a technology executive and data engineering leader with 19+ years of experience.', 1, 1, NOW(), NOW());

-- ---------------------------------------------
-- 6. BLOG CATEGORIES
-- ---------------------------------------------
INSERT INTO `blog_categories` (`name`, `slug`, `description`, `created_at`, `updated_at`) VALUES
('Data Engineering', 'data-engineering', 'Articles about data pipelines, ETL, and data architecture', NOW(), NOW()),
('Cloud Computing', 'cloud-computing', 'AWS, Azure, GCP, and cloud infrastructure topics', NOW(), NOW()),
('Public Transit Technology', 'public-transit-technology', 'Technology in public transportation systems', NOW(), NOW()),
('AI & Machine Learning', 'ai-machine-learning', 'Artificial intelligence and ML applications', NOW(), NOW()),
('Enterprise Architecture', 'enterprise-architecture', 'Enterprise-level system design and architecture', NOW(), NOW()),
('Leadership', 'leadership', 'Technology leadership and management insights', NOW(), NOW());

-- ---------------------------------------------
-- 7. BLOG TAGS
-- ---------------------------------------------
INSERT INTO `blog_tags` (`name`, `slug`, `created_at`, `updated_at`) VALUES
('GTFS', 'gtfs', NOW(), NOW()),
('Real-time Data', 'real-time-data', NOW(), NOW()),
('Microservices', 'microservices', NOW(), NOW()),
('Docker', 'docker', NOW(), NOW()),
('Kubernetes', 'kubernetes', NOW(), NOW()),
('AWS', 'aws', NOW(), NOW()),
('Azure', 'azure', NOW(), NOW()),
('Python', 'python', NOW(), NOW()),
('Java', 'java', NOW(), NOW()),
('API Design', 'api-design', NOW(), NOW()),
('Data Pipelines', 'data-pipelines', NOW(), NOW()),
('DevOps', 'devops', NOW(), NOW()),
('Agile', 'agile', NOW(), NOW()),
('Transit', 'transit', NOW(), NOW()),
('Digital Transformation', 'digital-transformation', NOW(), NOW());

-- ---------------------------------------------
-- 8. SAMPLE BLOG POSTS
-- ---------------------------------------------
INSERT INTO `blog_posts` (`title`, `slug`, `excerpt`, `content`, `category_id`, `author_id`, `is_published`, `is_featured`, `published_at`, `views`, `created_at`, `updated_at`) VALUES
('Building Resilient Real-Time Transit Data Systems', 'building-resilient-real-time-transit-data-systems', 'Exploring the architecture and best practices for building highly available real-time transit data systems using GTFS-RT.', '<p>In the world of public transportation, real-time data is not just a convenience—it''s a necessity. As the Manager of Customer Technology Data at MARTA, I''ve learned firsthand the critical importance of building resilient systems that can handle the demands of millions of daily riders.</p>\n\n<h2>The Challenge of Real-Time Transit Data</h2>\n<p>Modern transit systems generate enormous amounts of data every second. Vehicle positions, arrival predictions, service alerts—all of this information needs to be processed, validated, and distributed in real-time.</p>\n\n<h2>Key Architecture Principles</h2>\n<ul>\n<li><strong>High Availability:</strong> Systems must be designed for 99.99% uptime</li>\n<li><strong>Scalability:</strong> Handle peak loads during rush hours</li>\n<li><strong>Fault Tolerance:</strong> Graceful degradation when components fail</li>\n</ul>\n\n<h2>Implementation with Azure</h2>\n<p>Our implementation leverages Azure''s cloud services to ensure stability and performance. The key components include Azure Kubernetes Service for container orchestration and Azure Event Hubs for real-time data streaming.</p>\n\n<h2>Lessons Learned</h2>\n<p>Building these systems has taught me valuable lessons about balancing technical excellence with practical constraints. The most important takeaway is that simplicity often trumps complexity when it comes to reliable systems.</p>', 3, 1, 1, 1, NOW(), 0, NOW(), NOW()),

('Data Engineering Best Practices in Enterprise Environments', 'data-engineering-best-practices-enterprise', 'A comprehensive guide to implementing data engineering solutions that scale across large organizations.', '<p>Data engineering has evolved significantly over the past decade. What was once a niche specialty has become a critical function in virtually every technology organization.</p>\n\n<h2>Foundation of Modern Data Engineering</h2>\n<p>The foundation of any successful data engineering initiative is a clear understanding of business requirements and data governance principles.</p>\n\n<h2>Key Technologies</h2>\n<ul>\n<li><strong>Apache Kafka:</strong> For real-time data streaming</li>\n<li><strong>Apache Spark:</strong> For large-scale data processing</li>\n<li><strong>dbt:</strong> For data transformation and modeling</li>\n</ul>\n\n<h2>Building Data Pipelines</h2>\n<p>Effective data pipelines are the backbone of data engineering. They must be reliable, maintainable, and observable.</p>\n\n<h2>Conclusion</h2>\n<p>Success in data engineering requires a combination of technical expertise, business acumen, and a commitment to continuous improvement.</p>', 1, 1, 1, 1, DATE_SUB(NOW(), INTERVAL 5 DAY), 0, NOW(), NOW()),

('Cloud Migration Strategies: Lessons from the Field', 'cloud-migration-strategies-lessons', 'Practical insights from leading multiple cloud migration projects across different industries.', '<p>Cloud migration is one of the most impactful decisions an organization can make. Done right, it can transform operations. Done poorly, it can be a costly mistake.</p>\n\n<h2>Assessment Phase</h2>\n<p>Every successful migration begins with a thorough assessment of existing infrastructure, applications, and data.</p>\n\n<h2>Migration Approaches</h2>\n<ul>\n<li><strong>Lift and Shift:</strong> Quick but may not leverage cloud benefits</li>\n<li><strong>Re-platforming:</strong> Optimizing for cloud during migration</li>\n<li><strong>Re-architecting:</strong> Full cloud-native transformation</li>\n</ul>\n\n<h2>Common Pitfalls</h2>\n<p>Understanding common pitfalls helps avoid costly mistakes. These include underestimating complexity, inadequate planning, and neglecting security considerations.</p>', 2, 1, 1, 0, DATE_SUB(NOW(), INTERVAL 10 DAY), 0, NOW(), NOW());

-- ---------------------------------------------
-- 9. BLOG POST TAGS (Relationships)
-- ---------------------------------------------
INSERT INTO `blog_post_tag` (`blog_post_id`, `blog_tag_id`) VALUES
(1, 1), (1, 2), (1, 7), (1, 14),
(2, 8), (2, 11), (2, 12), (2, 15),
(3, 6), (3, 7), (3, 5), (3, 15);

-- ---------------------------------------------
-- 10. RESEARCH PUBLICATIONS
-- ---------------------------------------------
INSERT INTO `research_publications` (`title`, `slug`, `abstract`, `type`, `published_date`, `journal_name`, `is_published`, `created_at`, `updated_at`) VALUES
('Improving IT Service Management: A Case Study of the Help Desk Process', 'improving-it-service-management', 'This research paper analyzes IT service management practices and proposes improvements for help desk processes in enterprise environments.', 'paper', '2017-08-19', 'Managing Process and Systems', 1, NOW(), NOW()),

('Strategic Growth Options for Express Services in South Africa: A B2B/B2C Market Expansion Approach', 'strategic-growth-options-express-services', 'Analysis of strategic growth options for express delivery services targeting both business and consumer markets.', 'paper', '2018-06-09', 'Strategy', 1, NOW(), NOW()),

('Strategic Integration and Change Management in Global Logistics', 'strategic-integration-change-management', 'Insights from the FedEx–TNT merger examining strategic integration and change management practices.', 'paper', '2019-04-18', 'Leadership Challenge', 1, NOW(), NOW()),

('Logistics Last Mile Parcel Delivery Service Innovation', 'logistics-last-mile-delivery-innovation', 'Research thesis examining innovation in last-mile delivery services to meet evolving customer demands, focusing on on-location delivery solutions.', 'thesis', '2019-11-22', 'Henley Business School', 1, NOW(), NOW());

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