close

Azure Data Lake Integration for Advanced Analytics

Posted by Sas Chatterjee on Mar, 05, 2021 03:03

Power BI Data Flows and Azure Data Lake Storage Gen2 Integration:

The term ‘ Data Flow’ refers to a collection of datasets that are arranged into a tabular formation. The Power BI Services enable user organizations to create and manage these data flows within their workspaces using Azure Data Lake integration. 

Data Flows designs, aid in the following organizational scenarios-

  • Creating reusable logic that various datasets and reports can share amongst themselves. The Azure Data factory is generally used by the users to store the data. Moreover, this is later converted into data flows.
  • The organizational data can be exposed in the Azure Data Lake Gen 2 Storage. The other Azure Data Services can be connected to this raw data.
  • Forming only a single version of truth by forcing analysts to access the raw data rather than the underlying aspects. This gives the company proper control over the confidential data stored in the Azure Data Lake Storage.
  • The process of performing with huge volumes of data and information becomes easier with the data flows created through the Power BI Premium services. 
  • The user company can restrict the access of the analysts to certain sets of data through the Azure Active Directory. Access to the underlying aspects of the raw data is available to only a few individuals in the company. The other analysts can then use these data flows and create organized entities or tables with them.

The Azure Data Lake Storage Gen2 is a collection of capabilities that are designed and built-in in connection with the Azure Blob Storage. These capabilities with data flows of Power BI are dedicated to the field of big data analytics

This set of actions support several Azure services. You can use it to acquire data, creating analytic clusters with the data, creating visual representations along with providing enterprise-level security.

What is Data Silos: An Azure Data Lake Storage approach

In general, the concept of ‘data silos’ implies a collection of data or relevant information. It is created by one department in an organization that cannot be shared by other departments. As various departments in a company work with various kinds of data, there are several sets of data created. But, these are in reality inconsistent with one another. 

These data silos are usually created through the normal working pattern of companies. Later, they generally grow with the diversity and increase of workload. The cloud data lakes solve this existing problem for organizations by giving them secure and flexible access to the data stored in the silos. The Azure Data Lake Storage Gen2 is a similar kind of multi-protocol system providing various departments in the user company access to the same data. 

The Azure Blob Storage API and the Azure Data Storage Lake API permit companies to store all the relevant data in huge clusters within a cloud data lake. The companies can then create role-based access controls while all the departments use the data as required. In this way, the stored data can be used to perform other integrating functions which is the basic feature of data flows.

What is Common Data Model Folders:

A common data model folder is a kind of folder created with the data stored in a cloud data lake. The data in such folders are stored in conformation with the well-defined and standardized structures of metadata along with self-explanatory semantic models of data. 

In a company, when you store relevant data in the pattern of common data model folders, there comes about a certain consistency between the applications you use in the instant company’s workspace. This process of data increases the performance capabilities with the data flows being seamlessly created by the internal departments.

This standardised metadata format in these data model folders helps in increasing the discovery of metadata and interchange of data between various data producers and consumers, for instance, Azure Data Factory, Azure Machine Learning and others. 

Common Data Model

How to configure data flow storage for Azure Data Lake Gen2

In Power BI Services, the you generally use is stored within the internal storage provided with the service. But, after integrating the data flows with the Azure Data Lake Storage Gen 2, the user organization can store its relevant data within the account of Azure Data Lake Storage Gen 2 assigned to it. 

There is a specific prerequisite to be in the process of integrating data flows. The owner of the Azure Data Lake Storage Gen2 requires the permission of the owner of the workspace at the data storage level, resource group or subscription level. 

You can complete the integration of data flows in the following steps:

  1. Reach out to a workspace that is not connected to any data flows. Click on the Workspace Settings which further displays a new tab called Azure Connections. Select the tab and then click on the Storage option.
  2. The screen displays a ‘Use default Azure Connection’ option. In case the organization has already configured an Azure Data Lake Storage Gen 2 account, there are two options available. I) using the already configured account of Azure Data Lake Storage or click on Connect to Azure. This will then point to a new Azure storage account.
  3. After selecting Connect to Azure the user has access to certain Azure subscriptions. In this process, the user of the integrated account can later control the integration of data flows. 
  4. Finally selecting Save makes the user’s Azure Data Lake Storage Gen2 account integrated with that specific workspace.

How to connect workspaces with Azure Data Lake Gen2

In context with the Power BI data flows and Azure data lake integration, Microsoft released a new feature recently. Due to this feature, organizations have capabilities to assign different Azure Data Lake Storage Gen2 accounts to different workspaces. You can accomplish this with utilizing the Power BI tenant level settings. Moreover, you can complete this process of assigning these accounts by creating an Azure Blob Storage in the Azure Portal.

You can complete this through the following steps- 

  1. Open the Azure portal, find the storage account and then select Add. While forming a storage account certain things are to be let in mind, namely, 
  • The space for Power BI and the workspace should be the same.
  • The Azure Lak at Storage Gen2 should be enabled under Advanced Options, before creating a storage account. 
  1. After creating the storage account, the roles should be assigned under the Access Control 
  2. When the role assigning procedure is complete, the Power BI portal must be opened. The workspace for which a storage account is to be assigned must be selected. The subscription, resource group and the created storage account must be entered.
  3. The Save option must be selected.

What is Azure Synapse Analytics and its uses:

The Azure Synapse Analytics is a limitless analytics service that provides an array of functions. It include the integration of data, data warehousing of enterprises, and performing analytics functions with big data. The system gives the user the freedom to query and analyze data on the terms pre-determined by the user organization. 

The several functions related to data analytics can be performed under this system through both serverless and dedicated sources. The Azure Synapse thus brings all the worlds of performance data analytics together into a synchronizing system. 

The ultimate result is the seamless method of ingesting, exploring, preparing, analyzing, managing and serving data. It performs immediate BI functions and other machine learning activities.

The use of this analytics system can be made due to the following reasons:

  • The system is a unified platform for providing all kinds of services related to data analytics.
  • You can utilize the SQL services by the customers of Synapse Analytics on their terms.
  • Analytic functions of data can be performed with the Azure system within minutes of acquiring data from various sources. This can be done through a combination of Synapse Link and Cosmos DB analytical store.
  • You can integrate the Power BI workspaces into the Synapse which will increase the speed of performing data analytics.

Performing with CDM Folders: A Power BI approach

Microsoft made a recent approach to get rid of data silos by making the common data model or the CDM. These are standardized model or structure of storing and describing data. 

The CDM folders contain data in a basic pre-determined format which makes various departments of a user organization to access data easily and perform analytic functions with the uniform pattern of data. This model also enables the users to build semantic models upon their previously stored data. 

Along with this, the standardized method of storing data provides a great deal of security to the organizational data by restricting the access to the data by all kinds of analysts and professionals. Thus, you can influence the performance of Power BI substantially by this model of storing data leading to increased productivity of the services under this system. 

Data Lake Storage: Some basic FAQs

The Data Lake Storage Gen2 was made generally available on February 7, 2019, and has been subsequently evolving since then. Certain general facts are required to be known about the azure data lake integration.

Some of basic FAQ about Data Lake Storage:

  1. The Data Lake Storage Gen2 is built upon Azure Storage. As this is the foundation of the lake storage system, the customers now do not need to choose between the Data Lake Storage Gen2 and Azure Storage. As this was the general practice previous to the availability of Gen2, the customers now can reap the integrated benefits of the two systems.
  2. The Gen2 of Lake Storage system provided by Azure gives the benefits of a file system. It doesn’t compromise on the advantages of flexibility and cost-effectiveness provided by the method of object storage.
  3. In comparison to other storage systems, the Azure Data Lake system provides better performance and security standards for the analytic workloads of big organizations.
  4. As this storage system has recently been developed, some of the feature systems are still in the process of evolution. 
  5. Azure Data Lake Storage is the underlying storehouse of the data flows of Power BI.
  6. The security provided within the Data Lake is divided into two parts, namely, role-based access control and access control list.

Storage of dataflow data in Azure Data Lake Storage: Advantages

As per the Azure consultants, the benefits of storing data flow data in the Azure Data Lake Storage are manifold. They can be categorized as follows:-

  • Designed for big data analytics: The storage of data or data flows in theAzure Data Lake is specifically designed to facilitate the analytic functions of organizations working with big data. As the Data Lake Storage is built on Blob Storage, it increases the capacity of managing the data and the level of analytic performance of the company along with providing enterprise-grade security. 
  • High-level security: The Data Lake storage is designed to provide enterprise-level security. It uses the POSIX- compliant access control lists which restrict the access of data by people beyond the list.
  • Cost-efficient: The Azure Data Lake Storage provides a high storage capacity. It comes at low price which makes it easier for organizations to maintain a single storage platform at comparatively low expenditure.
  • Support of open source platforms: Among big advantages of the Data Lake Storage system is that it has support from various open-source platforms. 
  • Flexibility: The storage format in the Azure Data Lake is extremely flexible. You can later access this through both the Data Lake Storage Gen2 or the Blob Storage accounts. 
  • The capacity of storing huge amounts of data: Finally, the basic function of storing huge quantities of data is fulfilled by the Data Lake storage system. This makes it a perfect storage option for a big organization that use storage options such as Azure SQL Data Warehouse and others.

Functioning with Data Sources:

The pattern of functioning with data sources are as follows- 

  1. With Data Lake – In the context of data lakes, the working pattern of the user organization includes the process of acquiring data from heterogeneous sources. This can later move into the data lake. It is a pool of raw data, in the original form which helps in scaling the data within less time.
  2. With Azure Databricks – Some data sources like the Image, JSON file and others have direct support in the Azure Databricks. But other sources like the Azure Blob Storage, the Azure Data Lake Storage Gen2 and others need configuration.
  3. With Azure Data Factory – lastly, in Azure Data Factory, the user can initiate visual integration of data with the help of 90 connectors. They are both built-in and maintenance free.

EPCGroup is Microsoft Certified Gold Partner. We provide Microsoft Azure and Microsoft Power BI consulting and support services for Azure data lake integration.

EPC Group Microsoft Gold Partners

Conclusion:

Summarizing all the aspects of the Azure Data Lake Integration system. It can be said that the Azure Data Lake Storage Gen2 is evolving into becoming the future of data storage solutions for organizations of all sizes and workloads.

More and more organizations are looking for a Power BI consultant who can create and execute a roadmap for Azure and Power BI integration. The seamless system of forming data flows, storing the subsequent data flow, and the analysis of the same assists organizations to increase their productivity in the competitive business market.

[gravityforms id=41 title=”true” description=”false”]
<div class='gf_browser_chrome gform_wrapper exit_intent_popup_wrapper' id='gform_wrapper_41' ><form method='post' enctype='multipart/form-data' id='gform_41' class='exit_intent_popup' action='/azure-data-lake-integration/'> <div class='gform_heading'> <h3 class='gform_title'>Exit Intent</h3> <span class='gform_description'></span> </div> <div class='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' ><input name='input_1' id='input_41_1' type='hidden' class='gform_hidden' aria-invalid="false" value='https://www.epcgroup.net/azure-data-lake-integration/' /></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></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></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></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></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></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><script type='text/javascript'> jQuery(document).bind('gform_post_render', function(event, formId, currentPage){if(formId == 41) {if(typeof Placeholders != 'undefined'){ Placeholders.enable(); }jQuery('#input_41_7').mask('(999) 999-9999').bind('keypress', function(e){if(e.which == 13){jQuery(this).blur();} } );} } );jQuery(document).bind('gform_post_conditional_logic', function(event, formId, fields, isInit){} );</script><script type='text/javascript'> jQuery(document).ready(function(){jQuery(document).trigger('gform_post_render', [41, 1]) } ); </script>