There are a number of things that can affect query performance in a cloud data warehouse, such as the amount of data in the warehouse, the network latency, and the query optimiser used by the database. Here are some key considerations:

1. Data distribution: The distribution of data across the nodes of the data warehouse plays a crucial role. Uneven data distribution can cause data skew and impact query performance. It is essential to choose an appropriate distribution strategy (e.g., hash, round-robin, or composite) based on the characteristics of your data and query patterns.

2. Query complexity: The type of queries that are run against the data warehouse will also affect performance. Queries that involve joins and aggregations will generally be more complex and take longer to run.

3. Data volume and partitioning: The larger the dataset, the longer it will take to run queries. This is because larger datasets require more CPU and memory resources. By organising data into smaller, more manageable partitions, the data warehouse can efficiently process queries that involve only a subset of the data, leading to faster execution times.

4. Query tuning and indexing: You can tune your queries to improve performance. For example, you can use indexes to speed up queries that need to scan large datasets. Proper indexing based on query patterns enables faster data retrieval and improves query performance. However, indexing involves a trade-off as it increases storage overhead and can impact write performance, so it should be used judiciously.

5. Data compression: Effective data compression techniques can reduce storage requirements and improve query performance. By compressing the data, you can reduce disk I/O and network overhead, leading to faster query execution.

6. Query optimisation: The data warehouse’s query optimiser plays a crucial role in determining the most efficient execution plan for a query. It considers factors such as indexes, statistics, and data distribution to determine the optimal approach. Optimising the query design (e.g., avoiding Cartesian products, reducing unnecessary joins, or filtering data early in the query plan) or using materialised views can significantly affect performance. ByteHouse, for example, uses a self-developed query optimiser that combines cost-based optimisation and rule-based optimisation to achieve blazing fast performance.

7. Hardware scaling: Cloud data warehouses often provide options to scale hardware resources, such as CPU, memory, and storage. Scaling ensures optimal performance by scaling up the resources during peak times, or by optimising resource allocation for specific queries.

8. Query caching: Caching frequently executed or complex queries can significantly improve performance by reducing the need for query re-execution. By storing the results of a query in memory, subsequent executions can retrieve the results directly from the cache, eliminating the need for expensive computation.

9. Data denormalisation: In certain scenarios, denormalising data by aggregating and precalculating results can lead to improved query performance. This approach trades off storage space for faster query execution, especially for analytical workloads.

10. Cloud provider: The cloud provider you choose can also have an impact on query performance. Some cloud providers offer more powerful hardware and better infrastructure for running queries, which can lead to better performance.

The specific factors affecting query performance may vary depending on the cloud data warehouse platform you are using. You’ll need to optimise your queries for the specific data warehouse you are using. Use a well-designed data-model and monitor your queries to identify performance bottlenecks. You can also use a distributed query engine to run queries across multiple nodes. By following these tips, you can improve the performance of your cloud data warehouse queries and get the insights you need from your data faster.