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In choosing between ClickHouse® and TimescaleDB for real-time analytics in applications, each offers distinct advantages: ClickHouse® is optimized for high-speed analytical queries across large datasets with its columnar storage, while TimescaleDB extends PostgreSQL for effective time-series data handling with SQL compatibility. ClickHouse® excels with denormalized schemas and large-scale aggregations, offering fast data processing through its columnar format and vectorized execution, making it suitable for applications requiring high-speed analytics over millions of rows. TimescaleDB, on the other hand, benefits from PostgreSQL's robust relational capabilities, making it ideal for integrating time-series data with relational tables, especially in teams already familiar with PostgreSQL. The databases differ in their approaches to storage, with ClickHouse® storing columns separately to enhance compression and query performance, and TimescaleDB using a row-based storage model that leverages PostgreSQL's features like foreign keys and complex joins while optimizing for time-series data. Both databases support real-time ingestion and offer various mechanisms for scaling and high availability, but they differ in operational complexity, with ClickHouse® demanding more expertise in distributed systems compared to the more familiar PostgreSQL-based TimescaleDB. Managed services like Tinybird simplify the deployment and management of ClickHouse® by automating infrastructure tasks, offering a developer-friendly environment for building data pipelines and APIs. Ultimately, the choice between these databases depends on specific use cases, data models, query patterns, and team expertise, with hybrid approaches using both databases also being viable for different workloads.