1. Data Storage Design: Dremio is designed as a SQL engine that can connect to various data sources and query them directly, while Druid is structured for OLAP queries, mainly for time series data, with its column-oriented storage and aggregation capabilities.

2. Query Optimization: Dremio employs dynamic query optimization techniques to enhance query performance by rewriting queries on the fly and accelerating data access. In comparison, Druid focuses on pre-aggregating data during the ingestion process to optimize query speed and efficiency.

3. Data Ingestion Flexibility: Dremio offers flexibility in data ingestion by allowing data to be ingested as is, with no need for preprocessing, providing a more immediate access to the data. On the other hand, Druid requires a more structured and preprocessed data model during ingestion for optimal query performance.

4. Real-time Processing: Druid is designed with real-time ingestion and querying in mind, making it more suitable for use cases requiring up-to-date, real-time data analysis. Dremio, while keeping pace with query acceleration, may not be as proficient in real-time data processing capabilities.

5. Scalability and Distribution: Dremio is built to handle large-scale distributed data processing across multiple nodes, providing scalability for processing vast amounts of data. In contrast, Druid's architecture is focused on horizontal scalability for distributed querying and aggregation tasks within a cluster environment.

6. Use Cases: Dremio is well-suited for ad-hoc data exploration and interactive analytics, enabling users to query diverse data sources with ease. On the other hand, Druid is particularly effective for data warehousing scenarios where fast OLAP queries on large volumes of time-series data are required.

In Summary, Dremio and Druid differ in their data storage design, query optimization approaches, data ingestion flexibility, real-time processing capabilities, scalability and distribution models, as well as their ideal use cases.