MapD vs Scylla: What are the differences?

# Key Differences between MapD and Scylla

MapD and Scylla are two distinct database management systems with different use cases and functionalities.
1. **Database Type**: MapD is an in-memory SQL database that leverages GPUs for processing, making it ideal for complex analytics on large datasets. In contrast, Scylla is a NoSQL database designed for handling high-throughput, low-latency workloads, especially in distributed environments.
2. **Primary Use Case**: MapD is commonly used for data analytics and visualization tasks where speed and real-time insights are crucial. On the other hand, Scylla is preferred for applications requiring horizontal scaling and seamless data distribution across clusters.
3. **Consistency Model**: MapD follows a strict ACID-compliant consistency model, ensuring data integrity and reliability for transactional workflows. In contrast, Scylla offers tunable consistency levels to trade off between performance and data consistency based on use case requirements.
4. **Data Model**: MapD supports traditional relational data models with SQL queries, joins, and aggregations, making it familiar to SQL developers. Meanwhile, Scylla works with a wide-column store data model similar to Apache Cassandra, allowing for schema flexibility and wide distribution of data.
5. **Ecosystem Integration**: MapD typically integrates well with existing data analysis tools and frameworks like Jupyter, Tableau, and R for seamless data exploration and visualization. In comparison, Scylla integrates smoothly with popular NoSQL technologies like Kafka, Prometheus, and Grafana for monitoring and data pipelines.
6. **Deployment Complexity**: MapD requires specialized hardware with high-performance GPUs to leverage its full processing capabilities, potentially increasing the deployment cost. On the other hand, Scylla can run on standard hardware setups, offering more flexibility and cost-efficiency in deployment scenarios.

In Summary, MapD and Scylla differ in their database type, primary use cases, consistency models, data models, ecosystem integrations, and deployment complexities, catering to distinct needs in various database management scenarios.