close

Microsoft Azure Stream Analytics For Real-Time Analytics Service

Posted by Kevin Booth on Aug, 25, 2021 07:08

Microsoft Azure Stream Analytics is one of the most scalable complex event processing engines developed by the company. The engine is designed to enable the user organization to develop data in order to create real-time insights. This process of creating actionable insights in real-time is completed on the data sourced from multiple data streaming pipelines. The application also allows users to identify anomalies, predict changing market trends and set up triggers for creating workflows into other connected devices. Along with these actions, the features included in the Azure Stream Analytics pricing range help organizations to make relevant data available to other applications downstream for presentation, archiving, or further analysis of the data to create actionable insights.

What is Azure Stream Analytics?

Azure Stream Analytics

The term ‘Azure Stream Analytics’ refers to an Analytical engine designed by Microsoft that aims to analyze and process huge volumes of fast streaming data. This streaming pipeline of data can be retrieved from multiple sources and all of it is analyzed simultaneously in order to identify any recurring patterns or relationships within the data.

Azure Stream Analytics Pricing and Licensing model structure

The features are a cloud service designed to aid user companies in complex event processing. The actions undertaken by the engine available under the Azure can produce actionable insights. These insights are generated in real-time on the basis of the data generated by the cloud infrastructure services. The engine can integrate Azure IoT Edge and the Azure Event Hubs.

This helps the product to ingest and subsequently process uncountable events every second. Through this feature, the engine can deliver real-time insights in extremely low latencies, design visual dashboards. The Azure Stream Analytics Pricing range is created in a way in which the user company is billed by the number of streaming units provisioned for it. Thus, the Pricing structure can be categorized on the basis of the streaming technology, in the following manner:-

Standard Streaming Unit

             Standard               Dedicated
Resource TypeStream Analytics jobStream Analytics cluster
Streaming Unit$0.11/hour with a 1 SU minimum$0.11/hour with a 36 SU minimum*
Visual Network SupportNOYES
C# user-defined functionsLimited to West Central US, North Europe, East US, West US, East US 2 ad West EuropeAll Regions
Custom deserializersLimited to West Central US, North Europe, East US, West US, East US 2 and West EuropeAll Regions

As far as the methods of purchasing an Azure account is concerned, there are three ways in which organizations can do so. These include the following:-

  • Purchasing directly from Microsoft – This implies that the user company can manage the Azure environment on their own or receive help from a partner. The user will receive a monthly bill and will have the option to choose an Azure Support plan.
  • Purchasing from the Azure Website – An interested organization can also choose to buy an Azure account through its website directly.
  • From an Azure Sales Specialist – The final method of purchasing an Azure account is through an Azure sales specialist.

Total Cost of Owning Services in the Azure Stream Analytics structure:

The features help organizations to discover actionable insights using predictive analytics from the data generated by devices, cloud infrastructure services, and others. The product is designed to ingest and process innumerable events every second and can provide rich visual dashboards, decrease latency and increase the query complexity processed by the system.

The features included in Azure Stream Analytics pricing that can run on IoT Edge devices are available in public preview format. This implies that the interested organizations can use the Azure Stream on IoT Edge on their devices by creating stream processing jobs, without incurring any charges. Apart from this, the features are billed according to the concept of a standard streaming unit. The billing format can be explained in the following manner:-

                   Price
Standard Streaming Unit         $0.0066/hour

Azure Stream Analytics on IoT Edge:

Streaming Data In Azure Stream Analytics

The Azure IoT Edge refers to a completely managed service built upon the Azure IoT Hub. This helps companies to deploy their cloud service workloads and other third-party services to run on the IoT Edge devices. The resulting hybrid architectures help the systems of the user organization to interact less with the cloud. Also, react faster to local changes. One of the biggest advantages of using an IoT Edge device is the ability to respond faster to the data received and subsequently creating real-time dashboards.

Government entities are one of the most common yet crucial examples of organizations that require the ability to respond in real-time. They are required to take critical decisions in a short span of time. The IoT Edge Azure Stream Analytics along with their built-in machine learning models aid in processing the data of an organization locally. And also, send the relevant data in cluster provisioning patterns, to the cloud through the analytics pipeline. 

The Azure Stream Analytics jobs are also capable of getting input and output of data from other modules running on Azure IoT Edge Devices. The following are the supported stream input types on Azure IoT Edge:-

  • Azure Edge Hub
  • Azure Event Hubs
  • IoT Hub Allowance
Azure data input Source

The supported stream output types are the following:-

Azure Data output in Power BI

The Azure Stream Analytics on IoT Edge device makes the user organization capable to run complex event processing closer to IoT devices and run analytics on multiple streams of blob-type data on devices.

The pricing structure provides for the price range of Azure Stream Analytics’ on IoT Edge. It can be categorized as follows:-

             Standard
Price per job$1/device/month

Real-Time Analysis of moving Stream of data:

IoT Data Analytics using Azure Stream Analytics

The term ‘Stream of data’ implies the continuous flow of data that is generated by a wide range of sources. The Streaming technology is used to process, store, analyze and act upon the streamed data in real-time. The Azure Machine Learning pattern is combined with streaming technology to provide beneficial outcomes to organizations in various sectors like deciphering real-time stock trades, calculating exchange rates, fraud detection, and several others.

The Azure features provide advanced analytics on data within a short period of time. This is termed as real-time analytics of streaming data. In simple words, through this process, the large volumes of data produced by various sources are analyzed in real-time to create visual dashboards and also, other relevant actionable insights.

Features of services in the Azure Stream Analytics Pricing range:

Data Sources

The features of the services that are included in the Azure can be enumerated in the following manner:-

  • It is easy to start and within a few clicks, it turns into a hybrid architecture of connected devices. It also uses Azure Blob for ingesting historical data.
  • The service can increase the productivity of programmers by analyzing data in motion.
  • It is a completely managed service offered by Azure.
  • The Azure Stream Analytics’ features can be run on the cloud service or the IoT Edge Device.
  • The Azure real-time analytics stack is maintainable at an optimum cost.
  • There is cluster provisioning available for the data encrypted by the Azure that provides the required level of security to the organizational data.

Consultation: An EPC Group Approach

The EPC Group employs a group of highly qualified individuals who are Microsoft-certified consultants on Power BI and Azure consultants for Azure Stream analytics products and services. The organization is dedicated to helping companies broaden their business capacity, improve their productivity and achieve the desired enterprise goals. The existing Azure services are designed to help organizations evolve their brand image and meet global standards. Moreover, the services included within the Azure Stream Analytics pricing range are formulated to aid companies to create consistent streaming jobs based on the data that enters the streaming pipelines.

This data is then analyzed in real-time to create actionable insights. The process of creating stream processing jobs can prove to be very useful in forming visual dashboards and setting up improved operational systems. It is well understood that the features provided under the Azure Analytics range are very beneficial for companies irrespective of their core industry or size.

This is why it is imperative for organizations to have a proper consultation partner that stays dedicated to providing training sessions and later stays connected through round-the-clock customer service. The EPC Group provides all of these and more. As a gold-certified partner of Microsoft, the company provides specially curated training programs on cloud infrastructure services for different organizations according to their workloads.

Conclusion:

The services made available by Microsoft under Azure are designed to provide advanced data analytics using built-in machine learning capabilities. The analyzed data is then utilized to create real-time dashboards. The service is quite useful in organizations in order to perform stock trade analysis, fraud detection analysis, and recognizing embedded sensors.

In addition to this, the process of ingesting data from Azure Event Hubs and ingesting the results into the Azure Data Lake Store makes the method easier. All these beneficial factors combine well to form the Azure Stream Analytics pricing range that proves to be a lightning dream of the company and can help several other user organizations reach their goal.

[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='/azure-stream-analytics-for-real-time-analytics-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/azure-stream-analytics-for-real-time-analytics-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>