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HomeBlogCloud ComputingWhat is Azure Databricks? Features, Advantages, Limitations
As this digitalized world is rapidly moving towards Artificial Intelligence, the generation of humongous data has become an integral part of our daily lives. The data has been and will continue to grow exponentially. With increasing data, the need to process and accumulate these large datasets becomes very critical. Hence, the organizations have started to leverage Apache Spark to handle Big Data and the processing of these large datasets. The Apache Spark tech stack helped organizations execute data engineering, data science, and machine learning on single-node machines or clusters. Databricks is a web-based platform for working with Apache Spark. It provides end-to-end automated data engineering and ML solutions. Azure Databricks is a managed Databricks platform on Azure. Let's dive deeper into what Microsoft Azure Databricks has to offer.
The creators of Apache Spark founded Databricks. Azure Databricks Spark is a managed Spark service that lets you simplify and streamline the process of data processing and data analytics. It provides a unified data analytics platform for data engineers, data analysts, data scientists, and machine learning engineers. Databricks have become popular among organizations dealing with large-scale data processing and analytics challenges. Databricks's ability to simplify and accelerate the development of big data and machine learning applications has made it a first choice for businesses.
Azure Databricks is a managed version of Apache Spark on Azure. Microsoft and Spark engineers worked together to build a managed Spark platform on Azure. To put the definition simply, the implementation of Apache Spark on Azure is a service which is called Azure Databricks and that’s what Databricks is used for. You can learn more about Azure via Azure learning.
With Azure Databricks you can set up your Apache Spark environment within minutes. You can autoscale your workloads and collaborate on shared projects in an interactive Azure Databricks workspace. When I started working with Azure Databricks, I found it very simple and flexible to use. I know Databricks for beginners can seem daunting so you can checkout KnowledgeHut Cloud omputing courses to learn more about Databricks and Azure Databricks best practices.
Azure Databricks helps you to start quickly with an optimized Apache Spark environment. It allows your workloads to integrate seamlessly with open-sourced libraries. Azure Databricks supports Python[GU5], Scala, R, Java, and SQL. It also supports data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn. With Azure Databricks you can spin up clusters quickly. It provides global scalability and availability which ensures reliability and performance. Below are some features of Azure Databricks :
Now that we have learned about Azure Databricks features, let's dive deeper into the advantages of using Spark on Azure. Below are several advantages of using Microsoft Azure Databricks :
A Microsoft Azure subscription is a must for using any service on the Azure platform. If you don't already have one, you can get one for free by going to the Azure portal.
Follow the below steps to create a Databricks service on Azure :
You have successfully created Azure Databricks service.
Just like any other data residing in a database can be queried via SQL, the same is true for the datasets handled by Databricks. Databricks SQL is a feature that allows users to perform SQL queries and analytics on their data. It extends the capabilities of the Apache Spark SQL module and helps data analysts and engineers to collaborate effectively in a unified environment. Using Databricks SQL on the data stored in the data lake makes it easier for the users to create dashboards to be consumed by business users. Below are certain key aspects of Databricks SQL:-
Databricks Machine Learning (DBML) is a Databricks component in the unified Databricks platform which provides an integrated and collaborative environment for developing, training, streamlining ML workflows, and deploying machine learning models. It leverages the power of Apache Spark and combines it with powerful machine-learning libraries to prepare a production-ready machine-learning solution. It provides below key aspects below:
While Azure Databricks is a powerful and versatile platform to process and manage large data and analytics workloads it has certain limitations that a user must be aware of:-
In a data-driven world where insights are retrieved from large datasets that redefine business strategies, Azure Databricks seems like a compelling solution. It is a robust, collaborative, and scalable platform that lets data engineers, data analysts, and data scientists collaborate well and build end-to-end production-ready data processing and ML solutions. With all Azure Databricks components and Azure Databricks Storage, Azure Databricks becomes a great comprehensive platform to provide features that continue to harness the potential of big data to derive business successes. To learn more on Azure databricks Spark and Azure databricks components apart from the Azure Databricks example above you can checkout KnowledgeHut Azure certification courses.
Azure Databricks is an Azure cloud-based unified data and analytics platform that is built over Apache Spark. It simplifies the deployment of large-scale data engineering and ML solutions. Azure Databricks seamlessly integrates with other Azure services like Azure Storage, Active Directory, Azure DevOps, Azure Data Factory, Azure SQL Data Warehouse, etc.
Azure Databricks is a cloud-based managed service while traditional Apache Spark is an on-premises framework or self-managed. Azure Databricks is simple to deploy, manage, and provision whereas traditional Apache Spark requires users to manually configure clusters. Azure Databricks integrates with other Azure services seamlessly while for traditional Apache Spark, the integrations have to be manually set up.
Azure Databricks is designed to handle a wide variety of data types and formats such as structured data, unstructured data, semi-structured data, streaming data, graph data, machine learning data, geospatial data, time-series data, transactional data, etc.
To set up Azure Databricks for your organization you need to take an Azure subscription, log in to the Azure portal, navigate to Databricks, create a workspace, and create a cluster to host your workloads. Additionally, you can create a notebook to interact with the data stored in Azure data lake.
On Azure Databricks, users are billed on both storage and computing on a pay-as-you-go basis. Users can choose various combinations of storage and computing. Additionally, Databricks also offers auto-termination and auto-scaling features to optimize cost. Azure Databricks can also integrate with Azure Cost Management service allowing users to monitor and manage their spend.
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