Scaling Resources
Scaling resources is pivotal for optimizing stream processing and involves strategies to efficiently allocate and utilize resources, ensuring scalability and performance services such as Event Hubs, ASA, and Azure Databricks. The key aspects include the following:
- Balancing load: It distributes workloads across resources to prevent bottlenecks.
- Resource optimization: It efficiently uses CPU, memory, and storage.
- Scalability: It designs systems to handle varying data volumes.
- Partition processing: It manages data within partitions effectively.
Note
This section primarily focuses on the Scale resources concept of the DP-203: Data Engineering on Microsoft Azure exam.
In the ensuing sections, you will look at how you can scale resources in Event Hubs, ASA, and Azure Databricks Spark.
Scaling in Event Hubs
There are two ways in which Event Hubs support scaling:
- Partitioning: You have already learned how partitioning can help...
