KQL databases are a powerful way to perform real-time analytics on large volumes of data. They operate on a fully managed Kusto engine, which provides fast query response times and auto-scaling compute resources. However, using KQL databases involves some costs and fees, depending on the type of database, the amount of data, and the number of transactions.
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Different Types of KQL Databases
KQL databases can be classified into three types based on their temperature: hot, warm, and cold. The temperature of a database reflects how frequently the data is accessed and how fast the queries need to be executed. Each type of database has different storage and performance characteristics, as well as different pricing models.
- Hot databases are designed for high-performance analytics on data that is frequently accessed and updated. They use premium cache storage, which provides the fastest query response times and the highest throughput. Hot databases are ideal for scenarios such as real-time monitoring, alerting, and interactive dashboards. However, they also have the highest storage costs and fees, as they store all the data in the premium cache tier.
- Warm databases are optimized for cost-effective analytics on data that is less frequently accessed and updated. They use a combination of cache and standard storage, which provides a balance between performance and cost. Warm databases are suitable for scenarios such as historical analysis, reporting, and data exploration. They have lower storage costs and fees than hot databases, as they store only a portion of the data in the cache tier, based on the caching policy.
- Cold databases are intended for archival analytics on data that is rarely accessed and updated. They use only standard storage, which provides the lowest cost and the highest durability. Cold databases are appropriate for scenarios such as compliance, auditing, and backup. They have the lowest storage costs and fees, as they do not use any cache storage. However, they also have the lowest performance and the lowest throughput.
Costs Associated with Real-Time Databases in KQL
The costs and fees for using real-time databases in KQL are based on two factors: the capacity units (CUs) and the storage units (SUs). The CUs are the compute resources that are used to run queries and ingest data, while the SUs are the storage resources that are used to store data. The CUs and SUs are billed separately from each other, and they vary depending on the type of database and the usage pattern.
Capacity Units (CUs)
The CUs are the units of compute consumption that are shared across all KQL databases in a Fabric capacity. A Fabric capacity is a dedicated set of resources that is available at a given time to be used by different Fabric workloads, such as KQL databases, warehouses, lakehouses, datasets, etc. For more information on Fabric SKUs, see Microsoft Fabric licenses.
The CUs are billed at a pay-as-you-go rate, which depends on the region and the currency. The CUs are charged per second, and they are rounded up to the nearest second. Finally, the CUs are reported and billed in the Azure portal, under the subscription in Microsoft Cost Management.
Storage Units (SUs)
The SUs are the units of storage consumption that are used to store data in OneLake, which is the underlying storage layer for KQL databases. OneLake is a distributed storage system that provides high availability, durability, and scalability for data. OneLake supports two tiers of storage: cache and standard. The cache storage is a premium storage tier that provides the fastest query response times and the highest throughput, while the standard storage is a persistent storage tier that provides the lowest cost and the highest durability.
The SUs are billed at a pay-as-you-go rate, which depends on the region and the currency. The SUs are charged per GB per month, and they are rounded up to the nearest GB. The SUs are reported and billed in the Azure portal, under the subscription in Microsoft Cost Management. For more information on SU pricing, see Microsoft Fabric pricing.
Best Practices to Maintain Costs for KQL Databases
The costs and fees for using KQL databases can be controlled and optimized by following some best practices, such as:
- Choosing the right type of database for the data and the analytics needs
- Hot databases are more expensive than warm and cold databases, but they also provide better performance and throughput.
- Configuring the retention and the caching policies appropriately
- The retention policy determines how long the data is kept in the database, while the caching policy determines how much of the data is stored in the cache tier. By reducing the retention period and the caching ratio, the storage costs and fees can be lowered.
- Monitoring the capacity and the storage usage regularly
- The capacity and the storage usage can be monitored with the Microsoft Fabric Capacity Metrics app, which provides visibility into the CUs and the SUs that are consumed by the KQL databases and other Fabric workloads.
- Applying data compression and partitioning techniques
- Data compression and partitioning are techniques that can reduce the size and the complexity of the data, and improve the query performance and the storage efficiency. Data compression reduces the size of the data by applying encoding and deduplication algorithms, while data partitioning.
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