Scalability and High Availability

The ability to develop scalable and highly available data pipelines is critical for organizations that require fast and reliable access to data. Here, we will discuss some best practices for building scalable and highly available data pipelines in Azure Data Factory.

Optimization in ADF

Optimizing ADF for scalability and high availability involves fine-tuning pipelines and architecture to ensure high performance, scalability, and availability. This optimization ensures efficient pipeline operation, effective resource utilization, and the ability to easily scale pipelines as required.

Scalability in Azure

Scalability refers to the ability of a system to handle an increasing amount of work or traffic without sacrificing performance. In the context of Azure Data Factory (ADF), scalability means the ability of the platform to handle large volumes of data and process them efficiently.

Horizontal scaling

Horizontal scaling, also known as scaling out, is a common approach to scaling in ADF. In horizontal scaling, the data processing workload is distributed across multiple compute resources, such as Azure Data Factory integration runtimes, to increase the processing capacity of the system. It is a popular approach for scaling data processing workloads because it is cost-effective and relatively easy to implement.

Vertical scaling

Vertical scaling, also known as scaling up, involves increasing the resources allocated to a single compute node. This approach is useful when the data processing workload is not distributed across multiple resources. It can also be useful for tasks that require a significant amount of memory or CPU resources, such as data transformations or machine learning models.

Capacity reservation in Azure

Capacity reservation in Azure enables scaling by allowing users to pre-allocate and reserve resources, ensuring that a specified amount of capacity is dedicated to specific services or SKUs. This reservation mechanism provides a predictable and reliable infrastructure for scaling applications and workloads. By reserving capacity in advance, users ensure that resources are readily available when needed, reducing latency, optimizing performance, and supporting scalability with the ability to handle increased demand efficiently. The capacity reservation also offers cost benefits, allowing users to optimize resource usage while guaranteeing a level of availability for their applications.

Benefits of capacity reservation for ADF

Creating capacity reservations with high capacities can help in scaling Azure Data Factory (ADF) compute operations by ensuring that sufficient resources are reserved in advance, providing better performance and reliability. Here’s how it contributes to scaling ADF compute operations:

1. Resource reservation: Capacity reservations allow us to reserve a certain amount of capacity for specific Azure services or SKUs within a given region. By creating capacity reservations with high capacities, we ensure that a significant portion of resources is dedicated and available when needed.

2. Concurrency and parallelism: ADF compute operations often involve processing data in parallel or handling multiple tasks concurrently. High-capacity reservations enable better support for concurrent and parallel processing by providing the necessary resources to handle multiple operations simultaneously.

3. Scaling out: Depending on the specific Azure services or SKUs associated with the capacity reservation, high capacities can support scaling out operations. Scaling out involves distributing the workload across multiple instances or nodes, allowing ADF to handle larger datasets and complex computations more efficiently.

4. Reduced latency: With high-capacity reservations, we reduce the likelihood of latency issues during peak workloads. The reserved resources are immediately available for ADF operations, minimizing the time spent waiting for resources to be provisioned dynamically.

Applying scaling to Azure resources

Let’s now look at an example of adding scaling capacity to Azure resources. Azure supports scaling updates through capacity reservation. It is important to note that capacity reservation is applied at a resource group level, meaning once a resource group has been assigned to a specific scaling capacity, all compute resources within that resource group (including Azure Data Factory) will be able to access the increased capacity.

1. To start, let’s create a new resource group, e.g., scalingRG.