Managing data at scale on the cloud considerably opens up possibilities in regards to real-time applications, artificial intelligence, and predictive analytics. Apache Spark has been among the most popular platforms used by people in recent years to run robust analytics algorithms at scale while trying to drive business insights. However, trying to manage and deploy Spark at scale is not always easy, especially for use cases involving strong security requirements and a great number of users. Azure Databricks was essentially developed to meet these concerns. It facilitates a managed, end-to-end Apache Spark platform that is effectively optimized for the cloud. Having features like auto-scaling and one-click deployment, as well as optimized Databricks Runtime that can improve the overall performance of Spark jobs in the cloud on a massive scale, Databricks has proven to be a budget-friendly and simplified way to run large-scale Spark workloads.
Architecture of Azure Databricks
Azure Databricks launches and manages worker nodes in all Azure customer subscriptions at a high level, subsequently enabling them to effectively avail the varying management tools present with their account.
As a customer goes on to launch a cluster through Databricks particularly, a “Databricks appliance” tends to be deployed as an Azure resource in their subscription. The type and number of VMs to be used are then specified by the customer, while all other elements are competently managed by Databricks. Apart from this, a managed resource group tends to be deployed to the subscription of the customers that is populated with a storage account, a security group, and a VNet. Most of these concepts shall be familiar to typical Azure users. As these services are ready, the users can easily manage the Databricks cluster with the help of features like auto-scaling or simply through Azure Databricks UI. The diverse metadata, including scheduled jobs, essentially are stored in an Azure Database, while having geo-replication for the purpose of fault tolerance.
For any user, such a design basically implies two things, which are:
- They are able to connect Databricks to any storage resource in their account without much of a problem. This resource can range from a Data Lake to an existing Blob Store subscription.
- Azure control center manages Databricks centrally, and hence it does not need any extra set-up process.
Azure Databricks solution architecture examples
- Real-time analytics on big data architecture: Customers can capture data continuously from any IoT logs or device for website clickstreams and process it near real-time, while acquiring insights from live streaming data.
- Modern analytics architecture with Azure Databricks: This architecture allows customers to combine any data at any scale, and ultimately develop and deploy tailored machine learning models at scale. Azure Databricks makes it possible to leverage cutting-edge ML tools for transforming data into actionable insights.
- Machine learning lifecycle management: Managing and boosting the end-to-end machine learning life cycle becomes simpler with MLflow, Azure Databricks, and Azure Machine Learning. These solutions can be extensively used to develop, share, manage and deploy a host of machine learning applications.
Integrating Azure Databricks closely with all features of the Azure platform helps in making its optimal utilization.