close

Understanding Azure Data Factory Pricing For Serverless Data Integration Service

Posted by Errin O'Connor on Aug, 31, 2021 07:08

The availability of huge volumes of data is one of the biggest advantages of this century. These volumes of data are collectively termed Big data which exert a lot of influence on the growth and expansion of organizations across the world. But, big data is not useful until actionable insights are derived from it through the process of advanced analytics. This is the reason behind the consistent introduction of several data management and analytics tools in the market, at par with the increasing availability of data.

Cloud-based services in this context provide the maximum benefits to the user organization. But, there is always a possibility that the productivity of the user can be hampered while transferring the data from the on-premise to the cloud platform. This issue is skillfully dealt with by the Azure services developed by Microsoft. One such service is provided under the Azure Data Factory Pricing module which provides a platform that helps the user in performing various types of activities with the organizational data.

Introduction to Azure Data Factory and Data Pipelines: Its use cases for an organization.

Azure Data Factory is a cloud-based data integration service that is designed to enable the user to create data-driven workflows in the cloud environment. It is used to perform data movement activity and transform the raw organizational data. But the service itself does not store any data in the Azure Storage. It simply allows the user to create workflows concentrated on data to begin the movement activity of data in between the supported data environments.

Within the Azure purview, along with the transport of data, the service also allows the process of analyzing the data using compute service in on-premise or other environments.

Azure Data factory Serverless Integration

There are four components of the Azure Data Factory. These include the following:-

  • Datasets reprinting data structures stored with data stores.
  • Data Pipelines are considered as a group of activities.
  • Activities, defining the type of activities to be performed on the data.
  • Linked services increase the integration capabilities of the Data Factory.

The term ‘Data Pipelines’ refers to a logical grouping of activities that are created to perform tasks. For instance, a pipeline can contain a set of activities that are used to ingest and clean log data. This action then initiates several mapping data for analyzing the log data. In simple words, data pipelines are designed to allow the user to manage activities in a complete set rather than each individually.

One of the most common use cases of ADF is the process of creating personalized product recommendations for customers of various organizations. The features within the Azure Data Factory Pricing range one of the many services that implement solution accelerators included in the Cortana Intelligence Suite

Details of the Azure Data Factory Pricing Structure:

Azure creates a fully managed environment for the user to explore a variety of data integration capabilities to fit the expenditure scale, infrastructure, or user-specific performance compatibility. The Azure Data Factory Pricing structure is divided into two categories, which includes the following:-

  • V1
  • V2

The pricing charges for the use of the features within the Azure Data Factory Structure under the V1 category re recalculated the basis of the following points:-

  • The frequency of the activities
  • On the basis of whether the activities are being run on-premise or on the cloud.
  • Whether there exists an Inactive data pipeline and
  • Re-running of activities.

The billing heads under the V2 category include the pricing details for the features used in the Data Pipeline. These are inclusive of the following:-

  • External Pipeline activity execution
  • Executing data flow in debug mode
  • Several Data Factory operations use the creation of Pipelines and pipeline monitoring.

Estimation of Costs before use:

Azure Data Factory calculator can provide the user organization with exact estimation of the costs that can be incurred while running the ETL processes within ADF. The calculator can be used by inputting the essential details like the number of activity runs, several data integration hours, type of Compute used, execution duration, and others.

Complete Azure Data Factory Pricing model: Including Data Pipelines

Azure Data Factory Cost

The details of the Azure Data Factory Pricing Structure can be presented under two categories in the following manner:-

   AZURE DATA FACTORY V1:

Frequency of Activities –

 Low FrequencyHigh Frequency
Activities running in the cloud (for instance: copying activity moving data from Azure Blob to an Azure SQL Database)$0.48 per activity per month$1 per activity per month
Activities running on-premises and involving a self-hosted integration runtime$1.20 per activity per month$2.50 per activity per month

Data Movement –

Data movement between cloud data stores   $0.25 per hour
Data movement when on-premises stores are involved   $ 0.10 per hour

Inactive Pipelines –

Inactive Pipelines are charged at the rate of $0.80 per month.

Re-running Activities –

Re-running activities on cloud     $1.370 per 1,000 re-runs
Re-running activities on-premises     $3.425 per 1,000 re-runs

AZURE DATA FACTORY V2:

Data Factory External Pipeline Activity Execution

TypeAzure Integration Runtime PriceAzure Managed VNET Integration Runtime PriceSelf-hosted Integration Runtime Price
Orchestration$1 per 1,000 runs$1 per 1,000 runs$1.50 per 1,000 runs
Data Movement Activity$0.25/DIU-hour$0.25/DIU-hour$0.10/hour
Pipeline Activity$0.005/hour$1/hour (up to 50 concurrent pipeline activities)$0.002/hour
External Pipeline Activity$0.00025/hour$1/hour (up to 800 concurrent pipeline activities)$0.0001/hour

 *External Pipeline activities include the Databricks created on Azure Databricks

 Data flow execution on debug mode –

TypePriceOne Year Savings in Reserved (%)Three-year Savings in Reserved (%)
General Optimized$0.193 per vCore-hour    N/A      N/A
General Purpose$0.274 per vCore-hour$0.205 per vCore-hour      ~25% savings$0.178 per vCore-hour        ~35% savings
Memory-optimized$0.343 per vCore-hour$0.258 per vCore-hour   ~25% savings$0.223 per vCore-hour    ~35% savings

Data Factory Operations –

TypePriceExamples
Read/Write$0.50 per 50,000 modified or referenced entitiesRead or written entities of Azure Data Factory
Monitoring$0.25 per 50,000 run records retrievedMonitoring of Pipelines, activity, triggers, and debugging runs.

A Discussion on the Important costs: An Azure Data Factory Perspective

Azure Data Factory

The important costs in Azure Data Factory Pricing Structure can be enumerated as follows:-

  • Pipeline Activity Execution – This is measured on the basis of DMUs and the user needs to be aware of these because these tend to change into the auto mode.
  • External Pipeline Activity Execution– There are various Pricing models to measure these and include the process of moving data from on-premises to a cloud platform or vice versa.
  • Data Factory Artifacts– The Inactive pipelines are billed at a small rate.
  • SSIS integration– These Runtime involve the A-series and D-series Compute levels. While going through these, the billing depends on the computing needs that are to begin the process.

Method of monitoring costs of ADF and Data Pipelines:

You can categorize the methods of monitoring cost in the following parts:

  • Monitoring costs at Factory level: This monitoring category employs the features of tables for different time intervals to consistently keep a check on the expenses incurred while using Azure Data Factory.
  • Monitoring costs at pipeline run level: The user can view the various quantities of consumption for different meters for individual pipeline runs in the Azure Data Factory.
  • Lastly, Monitoring costs at the activity run level: After deciphering the consumption at the pipeline run level, the user needs to finally understand and identify the most costly activities within the pipeline which incur additional network bandwidth charges.

Consultation on Azure Data Factory: An EPC Group Perspective

The EPC Group consists of a highly experienced team for Azure Consulting for Azure data factory who have been consistently working towards helping organizations implement and use the cloud services adeptly to their advantage. The company has over more than two decades of experience in consulting organizations in the concept of Azure services like the Azure Synapse Analytics, Azure Data Lake storage, creating Azure Synapse Analytics Insights, Azure Data Share, Azure Analysis Services, Azure Data Explorer, Azure stream analytics service and coaching data analysts on Azure Machine learning technology.

Features of Azure Data Factoru

One of the major issues faced by organizations while shifting to the cloud environment is to understand the procedure of migrating the data from on-premise to the cloud without hampering the ongoing organizational activities. The Azure Data Factory service is the most appropriate tool for such organizations and the tailor-made training programs made by the EPC Group, related to this service can assist organizations to migrate to the cloud with ease without sacrificing their business capabilities. Being a Microsoft gold certificate consulting partner for Digital Transformation, the company also provides round-the-clock customer service to help organizations through the complete process of data migration to the cloud.

Conclusion:

The Data Factory provides an array of benefits to the user which makes it one of the suitable tools for an organization looking towards migrating their data into the cloud without stagnating their progress or sacrificing their productivity in the process. The fully managed Azure service provides code-free or low code transformations of data. The combination of GUI and script-based interfaces make the migration of data from the on-premises to the cloud smooth and hassle-free.

Finally, the strict consumption-based Azure Data Factory Pricing model makes the service quite cost-effective and beneficial for companies irrespective of their size and industrial background.

[gravityforms id=41 title=”true” description=”false”]
<div class='gf_browser_chrome gform_wrapper exit_intent_popup_wrapper gform_legacy_markup_wrapper' id='gform_wrapper_41' > <div class='gform_heading'> <h3 class="gform_title">Exit Intent</h3> <span class='gform_description'></span> </div><form method='post' enctype='multipart/form-data' id='gform_41' class='exit_intent_popup gform_legacy_markup' action='/understanding-azure-data-factory-pricing-for-serverless-data-integration-service/' > <div class='gform_body gform-body'><ul id='gform_fields_41' class='gform_fields top_label form_sublabel_below description_below'><li id="field_41_1" class="gfield gform_hidden field_sublabel_below field_description_below gfield_visibility_visible" ><div class='ginput_container ginput_container_text'><input name='input_1' id='input_41_1' type='hidden' class='gform_hidden' aria-invalid="false" value='https://www.epcgroup.net/understanding-azure-data-factory-pricing-for-serverless-data-integration-service/' /></div></li><li id="field_41_9" class="gfield gfield_contains_required field_sublabel_below field_description_below gfield_visibility_visible" ><label class='gfield_label' for='input_41_9' >Full Name<span class="gfield_required"><span class="gfield_required gfield_required_asterisk">*</span></span></label><div class='ginput_container ginput_container_text'><input name='input_9' id='input_41_9' type='text' value='' class='medium' placeholder='Full Name' aria-required="true" aria-invalid="false" /> </div></li><li id="field_41_6" class="gfield gfield_contains_required field_sublabel_below field_description_below gfield_visibility_visible" ><label class='gfield_label' for='input_41_6' >Email<span class="gfield_required"><span class="gfield_required gfield_required_asterisk">*</span></span></label><div class='ginput_container ginput_container_email'> <input name='input_6' id='input_41_6' type='text' value='' class='medium' placeholder='Email Address' aria-required="true" aria-invalid="false" /> </div></li><li id="field_41_7" class="gfield gfield_contains_required field_sublabel_below field_description_below gfield_visibility_visible" ><label class='gfield_label' for='input_41_7' >Phone<span class="gfield_required"><span class="gfield_required gfield_required_asterisk">*</span></span></label><div class='ginput_container ginput_container_phone'><input name='input_7' id='input_41_7' type='text' value='' class='medium' placeholder='Phone Number' aria-required="true" aria-invalid="false" /></div></li><li id="field_41_10" class="gfield gfield_contains_required field_sublabel_below field_description_below gfield_visibility_visible" ><label class='gfield_label' for='input_41_10' >Company Name<span class="gfield_required"><span class="gfield_required gfield_required_asterisk">*</span></span></label><div class='ginput_container ginput_container_text'><input name='input_10' id='input_41_10' type='text' value='' class='medium' placeholder='Company Name' aria-required="true" aria-invalid="false" /> </div></li><li id="field_41_8" class="gfield gfield_contains_required field_sublabel_below field_description_below gfield_visibility_visible" ><label class='gfield_label' for='input_41_8' >Message<span class="gfield_required"><span class="gfield_required gfield_required_asterisk">*</span></span></label><div class='ginput_container ginput_container_textarea'><textarea name='input_8' id='input_41_8' class='textarea medium' placeholder='Type your message here...' aria-required="true" aria-invalid="false" rows='10' cols='50'></textarea></div></li></ul></div> <div class='gform_footer top_label'> <input type='submit' id='gform_submit_button_41' class='gform_button button' value='Submit' onclick='if(window["gf_submitting_41"]){return false;} window["gf_submitting_41"]=true; ' onkeypress='if( event.keyCode == 13 ){ if(window["gf_submitting_41"]){return false;} window["gf_submitting_41"]=true; jQuery("#gform_41").trigger("submit",[true]); }' /> <input type='hidden' class='gform_hidden' name='is_submit_41' value='1' /> <input type='hidden' class='gform_hidden' name='gform_submit' value='41' /> <input type='hidden' class='gform_hidden' name='gform_unique_id' value='' /> <input type='hidden' class='gform_hidden' name='state_41' value='WyJbXSIsIjEwNTJhNGVmMWMyNzI3YTJmMjdiZTA1NjU4ZDMzYzY3Il0=' /> <input type='hidden' class='gform_hidden' name='gform_target_page_number_41' id='gform_target_page_number_41' value='0' /> <input type='hidden' class='gform_hidden' name='gform_source_page_number_41' id='gform_source_page_number_41' value='1' /> <input type='hidden' name='gform_field_values' value='' /> </div> </form> </div>