Azure Databricks: Data Analytics Platform For Microsoft Azure cloud Services
The globalization of the business has led to a massive increase in the production and utilization of data across the world. These large quantities of data that are being acquired and analyzed by several global organizations are collectively termed as “Big Data”. But, while many organizations are analyzing this data and implementing the insights to increase productivity, several others still struggle with the process of extracting the required actionable analytical insights that are necessary for organizational growth. In this article, we will focus on Azure Databricks pricing and features as a data analytics platform.
In many cases, this inability works to the disadvantage of these organizations that fail to use artificial intelligence to its full potential. Microsoft has been one of the pioneers in providing organizations across the world, with the opportunity of managing and analyzing big data to extract valuable business insights and reach their full potential. One such introduction to the array of services is the Azure Databricks, which works based on one of its most important components named Apache Spark.
What is Azure Databricks: Meaning and Importance in high-performance analytics
Azure Databricks is a high-performance analytics platform developed by Microsoft in collaboration with the creators of Apache Spark. This implies that its service is built on Apache Spark being its key component. On the other hand, Apache Spark can be understood as a cluster of computing frameworks. The concept of Databricks evolved when the creators of Apache Spark decided to make Spark more productive for the users. To achieve its capability of increasing productivity, a collaborative web interface was added to this service along with the ability to integrate rapidly with several other services.
Azure Databricks is endowed with a variety of other features that have been developed by Microsoft or other third parties. To be specific, this feature is an optimized version of Databricks deployment that has been tailor-made for the Microsoft Cloud services. The service can provide further enhancements in features including security and authentication. Along with this, it can seamlessly integrate with other Microsoft services including Azure Data Factory, Azure Data Lake Analytics, Azure Data Lake Store, and Azure Data Lake Storage Gen2.
The Azure Data and AI with the cloud interface are creating unpredictable changes in the field of data analytics. The ability to utilize the raw organizational data into creating actionable insights is the foundation of Microsoft services as well as Azure Databricks.
What is the Azure Databricks Pricing Structure: Discussion including the licensing details
The features under the Azure Databricks pricing range are a result of the collaboration between Microsoft and the creators of Apache Spark. Databricks is designed to provide the best of data bricks and Azure services that can help customers in accelerating their innovativeness by enabling data science techniques and high-performance analytics. Some of the unique features of the service include the following:-
- Improved and enhanced Azure Active Directory integration.
- Presence of native data connectors
- Combined billing of the features used and
The interactive workspace helps data scientists, engineers, and data analysts to collaborate through streamlined workflows. The service offers three distinct kinds of workloads that are available in various virtual machine instances. These are tailor-made for the specific analytics workloads of the user organization. The workloads can be categorized in the following manner:-
- All-purpose compute workloads
- Jobs compute workload and
- Jobs light compute workload
The pricing model is structured into certain distinct plans based on which the billing is computed. These include the following:
- The pay-as-you-go model
- Databricks Unit pre-purchase plans are further divided into the 1year pre-purchase plan and 3year pre-purchase plan.
Data Analytics Clusters: Meaning
The features included in the pricing and licensing structure are available in two distinct pricing categories. These can be enumerated in the following manner:-
- Standard tier and
- Premium tier
The term ‘Azure Databricks Clusters‘ refers to a collection of computation resources and cluster configurations that form the basis of data engineering, data science, and data analytics workloads. Thus, it consists of a range of clusters based on which all analytical services are performed. The cluster types have been renamed by Microsoft on March 30, 2021. The Azure Databricks Workspace creates the environment where the pricing category can be chosen by the user. While both the categories have the same data clusters, the features of the standard tier clusters differ from those in the premium cluster tier.
The Azure Databricks Pricing page refers to the ‘Data Analytics Clusters’ as the interactive clusters. These are now termed All-purpose Compute after being renamed. These clusters can be created using the UI or the REST API. The Data Analytics clusters are also called interactive clusters as they are adept in tackling interactive workloads like running notebooks. These clusters can be further categorized into the following:-
- Standard Clusters – The user of Azure Databricks has the power to configure clusters according to the needs of the organization. But, the default cluster type is the Standard cluster. These clusters are recommended for single users and can run the workload that is developed in any language like Python, SQL, R and Scala.
- High Concurrency Clusters – A high concurrency cluster is a component of the cloud resource. The important benefits of this cluster can be classified in the following way:-
- Provides the ability of fine-grained sharing
- Helps in maximum utilization of resources and
- Decreased query latencies.
What kinds of Job Clusters are available in Azure Databricks:
The cluster configurations can be broadly classified into two types which are as follows:-
- Interactive clusters and
- Job clusters.
The Azure documentation uses the term ‘Job Clusters‘ collectively including the Data Engineering and Data Engineering Light clusters. The other name for job clusters is ‘Automated Clusters’. One of the main purposes of these clusters is to run a job and to terminate the same later.
Data Engineering cluster – The process of creating a data engineering cluster is to configure the cluster type to the same. The process of creating this type of job cluster can be accomplished by following the below-mentioned steps:
- Select the Edit link to open the cluster settings.
- The cluster configuration page opens
- Here, click on the new job cluster within the cluster type option.
This automatically creates a Data engineering cluster type unless the Databricks Runtime is not selected to ‘light’.
Data Engineering Light cluster – This is one of the most basic versions of clusters and is devoid of several new features that are provided within the other cluster types. While this is a cluster type lacking in few new details, it is still used by some organizations. The process of creating a Data Engineering Light cluster can be completed in the following manner:-
- Click on the Edit link to open the cluster configuration page.
- Then select the new job cluster option to open.
- Here in cluster type select the Data engineering light option from the Databricks runtime version dropdown.
Various purchase models under the Azure Databricks Pricing Structure:
The various pricing models within the Azure Databricks price structure can be categorized in the following tabular format:-
Pay-as-you-go model –
|Workload||DBU Prices – Standard Tier||DBU Prices – Premium Tier|
|All-purpose compute||$0.40 /DBU-hour||$0.55 /DBU-hour|
|Jobs compute||$0.15 /DBU-hour||$0.30 /DBU-hour|
|Jobs light compute||$0.07 /DBU-hour||$0.22 /DBU-hour|
|SQL Compute (preview)||$0.22 /DBU-hour|
Databricks Unit pre-purchase plans –
The users of Azure Databricks can achieve up to 37% savings when they choose to pre-purchase in the form of Databricks Commit Units also known as the DBCU. The period of these purchases is categorized into two parts: 1year pre-purchase plan and 3year pre-purchase plan. The DBCU is designed to generalize the use of Azure Databricks workloads in the tiers into a single purchase bill.
1-year pre-purchase plan –
The 1year pre-purchase plan can be described through the following table format:-
|Databricks Commit Unit||Price (without discount)||Discount|
Azure Databricks Premium v. Standard: A discussion of the features
The Azure Databricks Pricing range can be categorized in the following manner:-
- Standard Tier and
- Premium tier
The features of these tiers can be explained as follows:-
Standard Tier Features –
The standard tier features differ based on Standard Data Engineering capabilities and Standard Data Analytics capacities. The Standard Data Engineering capacities are the following:
- Inclusive of the Apache Spark Clusters.
- It is a schedule for running notebooks and libraries
- Alerts and monitors workflows
- Performs notebook workflows
- Produces streamlining and monitoring facilities.
The Standard Data Analytics workload includes all of the above features including a few others. These can be stated as follows:
- Apache Spark clusters are persistent towards data analytics
- Auto-scaling of data is a constituent feature.
- The cluster provides the facility for multi-user usage.
- Enhances collaboration
- Consists of one-click visualizations, the ability to create interactive dashboards and revising history.
Premium Tier Features –
The premium tier consists of all the features that are included in the standard tier. But, the security and authentication procedure included within the premium tier is the differentiating factor in the standard and premium tiers. There are two features related to security and authentication. These include the role-based access feature and the JDBC/ODBC endpoint authentication features.
In a multi-user environment, it is sensible that the user organization will need to provide certain access features to some individuals for performing some specific options. In such scenarios the premium is best suited for organizations, other than this, the standard tier is enough to get started with Azure Databricks.
What are different Types of Workloads:
The workloads in the Azure Databricks pricing module can be divided into the following categories:-
- Data Engineering workload and
- Data Analytics workload
Both these workloads are similar and perform the same features. But the Data Engineering workload is designed for the scheduled engineering operations and the data analytics workload is designed for the ad-hoc operations which include analytics.
Azure Databricks Consultation: An EPC Group Approach
The EPC Group is one of the pioneers in the field of consulting organizations in the process of understanding and implementing the various data management and analytics tools. The company has over two decades of experience in tackling customer issues regarding the implementation of data analytics tools and subsequently helping them extract the full potential of these analytics tools.
Being a Microsoft gold certificate partner, the EPC Group has been consistently creating customized training programs that can help Microsoft service users to utilize the tools and services of Power BI and Azure to their fullest advantage.
In connection to Azure Databricks, the training programs developed by the company can help organizations to implement and scale the data engineering methods and venture into the area of collaborative data science that creates an interactive analysis of data. The machine learning capabilities can be used to create intelligent insights which can help the user get leverage over other organizations in the market. With the dedicated team of experts at the disposal, along with having round to clock customer support, EPC Group can prove to be one of the best consultation partners for Azure Consulting for Azure Databricks.
The organizations opting to utilize the capabilities Databricks are offered with the new age technology of a unified platform. This service makes the process of analyzing big data and utilizing artificial intelligence in regular business patterns way easier than others. The data science and data engineering departments of organizations can now use this quick, easy and collaborative platform that is built on spark-based analytics.
The service can also natively integrate with several other Microsoft Azure services in a varied number of ways. These integrating ways range from a single start button to the process of creating a unified billing. The service provides authentic Microsoft Azure security while seamlessly integrating with other Azure services like the Azure Active Directory and the Azure Synapse Analytics.
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