Scylla vs TimescaleDB: What are the differences?

1. Architecture: Scylla is built on Apache Cassandra's Dynamo-based architecture which optimizes for write throughput and low-latency while TimescaleDB is a time-series optimized relational database that leverages PostgreSQL's architecture with improvements for time-series data handling.

2. Data Model: Scylla uses a wide-column store data model where each row has dynamic columns while TimescaleDB employs a relational table design with structured columns for better query flexibility and rich data analysis.

3. Query Language: Scylla uses Cassandra Query Language (CQL) which is similar to SQL but has limitations in complex querying functionalities, whereas TimescaleDB supports full SQL capabilities due to its PostgreSQL foundation, enabling advanced querying and analytics.

4. Scalability: Scylla is highly scalable horizontally, allowing easy addition of nodes for increased capacity and performance, while TimescaleDB may face scaling challenges with huge datasets due to its relational database structure.

5. Use Cases: Scylla is ideal for real-time big data applications requiring high availability and low latency such as IoT, AdTech, and e-commerce, while TimescaleDB is best suited for time-series data applications like monitoring, sensor data, financial analytics, and industrial IoT.

6. Consistency Model: Scylla guarantees eventual consistency by default but can be configured for strong consistency, whereas TimescaleDB provides strict consistency with the consistency level adjustable to meet specific application requirements.

In Summary, Scylla and TimescaleDB differ in their architecture, data model, query language, scalability, use cases, and consistency model, catering to distinct use cases and preferences in data management.