This solution is a robust and scalable backend architecture built for high-throughput data ingestion, analytics, and full-text search. To support long-term analytical needs and reduce reliance on Elasticsearch, it includes automated migration scripts and infrastructure to efficiently transfer and normalize data into ClickHouse.

The system is optimized for real-time performance, horizontal scalability, and seamless integration with social media APIs such as TokAPI, TikHub, and Instagram Looter, along with third-party services like Stripe, SendGrid, Sentry, Slack, and OpenAI.

With built-in support for auto-scaling, read replicas, and CDN delivery, the platform aims to achieve 99.9 percent uptime, a 50 percent reduction in API response times, and a 40 percent decrease in infrastructure costs. This enhances developer efficiency and delivers a high-quality user experience.

Modern social media platforms generate massive volumes of user-generated content and metadata every hour. Businesses that rely on this data for marketing, analytics, or influencer intelligence face significant challenges:

• Ingesting and processing hundreds of thousands of profiles and updates in near real-time.
• Running complex filters and full-text searches with high accuracy and speed.
• Aggregating and analyzing time-series data across millions of data points.
• Managing data across multiple storage systems (e.g., Elasticsearch, PostgreSQL) without consistency issues or performance bottlenecks.
• Scaling infrastructure efficiently without over-provisioning or driving up costs.
• Ensuring reliability and fault tolerance while maintaining fast developer iteration cycles.

Without a unified, scalable architecture, teams struggle with rising infrastructure costs, slow response times, data inconsistencies, and poor user experience.