There are a lot of good reasons to use Azure Data Factory (ADF) in your data management processes. If you’re an Azure-forward company, it is the best option for your needs. Still, you may have noticed that your Azure Data Factory pricing is coming in higher than you thought.
If this sounds familiar, it’s not a sign you need to switch to a new data management platform. Rather, it’s a sign that you could use a better strategy to help you optimize the cost of your data pipelines. You can save a lot of money with the right strategy while still reaping all the great benefits of ADF.
If you’re not sure how to get started, we can help. This article will present a digestible overview of how you can save costs on every ADF pipeline. We’ll discuss the Azure pricing structure, what that means for you, and how you can make your data movement more cost efficient.
How is Azure Data Factory Priced?
Costs for Azure Data Factory are divided into data pipeline and SQL server integration services. This is then further divided into data integration units (DIUs) and orchestration. DIUs are a combination of CPU, memory, and network resource allocation. While orchestration costs are associated with creating, managing, and monitoring data pipelines.
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Here’s how all that works
You’re charged based on the number of DIUs you use and the duration they run. For example, if you use 10 DIUs for 2 hours, and each DIU costs $1 per hour, your cost would be:
- 10 DIUs x 2 hours x $1 per hour = $20
Orchestration costs are determined by integration runtime hours. If your pipeline runs for 5 hours and the orchestration cost is $2 per hour, you’d pay:
- 5 hours x $2 per hour = $10
SQL Server Integration Services (SISS)
SISS pertains to running ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) packages in the cloud. You’re charged based on the number of vCores (virtual cores) you allocate to your SSIS runtime and the duration it runs.
To break that down, if you run an SSIS package using 4 vCores for 6 hours, and each vCore costs $0.50 per hour, your cost would be:
- 4 vCores x 6 hours x $0.50 per hour = $12
Active Data Pipelines
Data movement activities are simply the processes of moving data from one place to another. You’re charged based on the number of DIUs involved in the transfer and the number of hours they run. Moving data for 3 hours using 5 DIUs at a rate of $0.50 per DIU per hour would cost:
- 3 hours x 5 DIUs x $0.50 per DIU per hour = $7.50
Data flow activities allow for the transformation and enrichment of data at scale. These activities allow for the transformation of data within Azure Data Factory without the need for external tools or coding. Costs are based on vCore-hours, which represent the computational power used.
If a data flow runs on 8 vCores for 4 hours at a rate of $0.25 per vCore-hour, the cost would be:
- 8 vCores x 4 hours x $0.25 per vCore-hour = $8
Inactive Data Pipelines
Pipelines that aren’t active but still exist in the system incur a charge. If a pipeline has no associated trigger or doesn’t run within 30 days, there’s a separate fee. If you have 10 inactive pipelines and each costs $0.80 per month, you’d pay:
- 10 inactive pipelines x $0.80 per pipeline per month = $8 per month
Pricing Calculator for Azure Data Factory
As you may have noticed, that’s a lot of numbers to crunch to reveal your total ADF activity costs.
So, here’s an online calculator to help give you an estimate. Please note that this calculator will broadly estimate your total costs. Do not use it as a replacement for your detailed analytics and financial reporting activities.
Azure Data Factory Cost Calculator
4 Ways You Save on Your Data Factory Pipeline Costs
1. Optimize Data Movement Activities
Copious amounts of data movement can lead to high costs. Make sure you only move the data you need. Filter out unnecessary data early in the process and avoid transferring large volumes of redundant data. This not only saves on costs but also improves the efficiency of your pipelines.
2. Implement Incremental Data Loading
Incremental data loading, also known as delta loading, involves only loading new or changed data since the last update, rather than reloading the entire dataset. By processing only the changes, you make your pipelines more efficient and cost-effective.
3. Monitor & Analyze Pipeline Performance
Monitor your pipeline’s performance regularly to spot inefficiencies that increase costs. Azure Data Factory offers tools that let you analyze pipeline performance in real-time. Take advantage of these to fix bottlenecks or unnecessary processes.
4. Use Reserved Capacity
Committing to a longer-term usage can often lead to cost benefits. Azure Data Factory’s reserved capacity pricing offers discounts for 1 or 3-year commitments. If your workload is consistent and predictable, this option can lead to substantial savings over time.
Discover More About How You Can Optimize ADF Pricing
Relooking at how you use ADF is a great way to see where you can make more cost-effective decisions. Hopefully, this guide helped you get started and helped you understand where your pipeline costs are going.
If you’re in need of additional advice from an Azure expert, you can find one at Atmosera. We’ve seen how ADF has evolved over the years. That makes us more than equipped to look at your setup and see where you can save.