Understanding Azure Data Factory Cost Structure

Azure Data Factory (ADF) is a cloud-based data integration service that can get expensive if not managed properly.

The cost of using ADF is based on the number of data integration units (DIUs) consumed, with each DIU costing a certain amount per hour.

There are several factors that can affect the number of DIUs consumed, including the type of data being integrated, the frequency of integration, and the size of the datasets involved.

To minimize costs, it's essential to understand how ADF pricing works and to optimize your data integration processes accordingly.

Azure Pricing is a crucial factor to consider when evaluating the cost of Azure Data Factory.

The pricing model for Azure Data Factory is based on a consumption-based pricing structure.

You only pay for the resources you use, which means you can scale up or down as needed without being locked into a fixed cost.

Azure Data Factory pricing is based on a combination of the number of Data Factory units (DFUs) and the amount of data processed.

Each Data Factory unit costs $0.008 per hour, and you can purchase a minimum of 1 unit, with prices scaling down for larger commitments.

The cost of data processing is based on the amount of data moved, with prices ranging from $0.004 per GB for the first 10 TB to $0.003 per GB for data processed above 10 TB.

This pricing model allows you to save money by scaling back on less frequently used resources, and by taking advantage of discounts for larger commitments.

Overall, the pricing model for Azure Data Factory is designed to be flexible and cost-effective, allowing you to scale your resources to meet your needs without breaking the bank.

Data Factory Operations are a crucial aspect of Azure Data Factory cost. You're charged per 50,000 modified or referenced entities for Read/Write operations, which include create, read, update, and delete actions on entities like datasets, linked services, pipelines, integration runtime, and triggers.

The pricing for Data Factory usage is based on several factors, including the frequency of activities. High-frequency activities execute more than once a day, while low-frequency activities execute once a day or less.

Data Factory Operations also consider where activities run. You're charged differently if your activities run in the cloud or on-premises.

Inactive pipelines can also affect your costs. If a pipeline is inactive, you'll still be charged for Read/Write operations, but not for Monitoring operations.

Re-running activities can also impact your costs. If you re-run an activity, you'll be charged again for the Read/Write operations associated with it.

Here's a breakdown of the pricing for Data Factory Operations:

*Read/write operations for Azure Data Factory entities include create, read, update, and delete.

**Monitoring operations include get and list for pipeline, activity, trigger, and debug runs.

Inactive pipelines are charged at a specific rate per month, but the actual cost depends on the duration of inactivity.

If a pipeline is inactive for an entire month, it's billed at the applicable "inactive pipeline" rate for that month.

Pipelines that are inactive for a portion of a month are charged prorated for the number of hours they are inactive in that month.

For instance, if a pipeline is active for 20 days and inactive for 11 days in a month, the charge for the inactive period is prorated accordingly.

Inactive pipelines are charged at a rate of $- per month, regardless of whether they're actually running or not.

You must specify an active data processing period for each pipeline to avoid being charged for inactivity.

A pipeline is considered active for the specified period, even if its activities aren't actually running.

Pipelines that are inactive for a whole month are billed at the applicable "inactive pipeline" rate for the month.

If a pipeline has a start date and end time, it's considered active for those days and inactive for the rest.

For example, if a pipeline is active from January 1 to January 20, it's charged for 11 days of inactivity.

Pipelines without an active data processing period are considered inactive.

Re-running activities can be a necessary part of managing pipelines, especially if data sources become unavailable during scheduled runs.

You can re-run activities as needed, and the cost varies based on the location where the activity is run. Re-running activities in the cloud costs $- per 1,000 re-runs, while on-premises re-runs cost $- per 1,000 re-runs.

If you have a data pipeline with activities that run at low frequency, like once a day, re-running can be a significant concern. For example, a data pipeline with a Copy activity that copies data from an on-premises SQL Server database to an Azure blob, and a Hive activity that runs a hive script on an Azure HDInsight cluster, may incur re-run costs.

The cost of re-running activities is factored into the overall pipeline cost. For instance, the total activities cost for the pipeline mentioned above is $- per month.

To give you a better idea of the costs involved, here's a breakdown of the costs associated with re-running activities in the cloud and on-premises:

Keep in mind that these costs are subject to change and may vary depending on your specific pipeline setup and usage.