What 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.

What is Databricks?

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.

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What is Azure Databricks?

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 computing courses to learn more about Databricks and Azure Databricks best practices.

Azure’s Databricks Feature

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 :

1. Collaborative & Interactive Workspace - With Azure Databricks you can quickly explore data and share insights, build models collaboratively.
2. Native integration with Azure services - Microsoft Azure Databricks can be integrated seamlessly with native Azure services such as Azure Data Factory, Azure Data Lake Storage, Azure Machine Learning, and Power BI.
3. Machine Learning runtime - Azure Databricks provides easy access to preset learning environments with just one click for enhanced machine learning using popular and cutting-edge frameworks like sci-kit-learn, TensorFlow, and PyTorch.
4. MLflow - It lets you collaboratively manage models, replicate runs, and track and share experiments from a common repository.
5. Delta Lake - With Delta Lake, an open-source transactional storage layer built for the whole data lifecycle, you can scale and improve the data dependability of your current data lake.

Advantages 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 :

1. Automated Machine Learning - The Databricks platform on Azure has automated machine learning capabilities that help to streamline ML processes such as model selection, hyperparameter tuning, etc.
2. Enterprise-grade security - Azure Databricks creates a secure, private, compliant, and isolated analytics workspace across users and datasets to protect data.
3. Optimized Spark engine - Azure Databricks uses the latest highly optimized version of the Spark engine to perform simplified data processing on autoscaled infrastructure.
4. Choice of Language - As mentioned in the Databricks overview, Azure Databricks supports languages such as R, Python, Scala, Spark SQL, and .NET. So, you can choose any language you want for data processing.
5. Deep Learning Support - Azure Databricks supports various deep learning frameworks like Tensorflow and PyTorch.
6. Integration with Azure DevOps - Data engineering and data science workflows can be integrated into an organization's complete development lifecycle with the help of Azure Databricks' seamless interaction with Azure DevOps for version control, continuous integration, and continuous delivery.
7. Interactive Workspaces - Azure Databricks enables seamless collaboration between engineers, analysts, and data scientists.

Create an Azure Databricks service

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 :

• Sign in and navigate to the Azure portal home page. Click on Create a resource and type Databricks in the search box.

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• Click on the Create button.

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• Now you will get a form like shown in the image below. It has the following fields:\

1. Subscription – Select your subscription.
2. Resource group – Create a new resource group by clicking on the Create button. The name will automatically appear here.
3. Workspace name – Pick any name for the Databricks service.
4. Location – Select the region where you want to deploy your Databricks service.
5. Pricing Tier – Select a suitable pricing tier for your service.

• After filling out all the details click on Review + Create button to review the values filled in the form. After reviewing click on the Create button to create the service.
• Now you'll get a message on the screen - "Deployment Succeeded" in case your deployment is successful. Click on the Go to Resource option to open the service that you have recently created. 

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• Now you will see all the details of the service that you have created. Click on Launch Workspace to open the Azure Databricks portal. Now you will have to sign in again to access the Databricks portal.

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• On the Workspace tab, you can create notebooks and manage your documents. The Data tab lets you create tables and databases. You can also work with various data sources like Cassandra, Kafka, Azure Blob Storage, etc.

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• After creating Databricks service we need to create a spark cluster. Click on Clusters in the left menu. Click on Create Cluster to create a cluster.

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• Use the below image to fill up the configurations of the cluster. And finally, click on Create Cluster

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• Now you will see the status of the creation of the cluster as Pending until it is created.
• Once it is active and running you will see the status as Running.

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• Now you can create a Notebook in a Spark cluster. A Notebook is a web-based code and visualization platform built to interact with Spark in various languages.
• Now to create a notebook, click on the Workspace option in the left menu. Click on Create and select the Notebook option.
• Provide the Notebook name, select Language and Cluster, and click on Create. This will create a Notebook.

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You have successfully created Azure Databricks service.

Databricks SQL

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:-

1. SQL Dialect Support - Databricks SQL supports ANSI SQL to allow users to write standard SQL queries and supports Spark SQL to handle complex data types.
2. Data Exploration and Visualization - It allows users to easily visualize their data using SQL queries.
3. Collaborative Notebooks - Users can create and share their code, and SQL queries ensuring collaboration between team members.
4. Performance Optimization - Databricks SQL uses Spark engine which is optimized for distributed computing and efficient processing of large datasets.
5. Connectivity to various data sources - Databricks SQL supports connectivity to various data sources, including data lakes, databases, and external file systems hence introducing flexible data integration.
6. Optimization and Tuning - Users can optimize and tune their SQL queries using the Databricks platform. This includes leveraging features such as query optimization, indexing, and caching to enhance the performance of SQL-based analytics.

Databricks Machine Learning

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:

1. Since Databricks ML is built on an open architecture with a foundation on Delta Lake, it simplifies all aspects of Data for ML and AI. It can turn features into production pipelines without much hassle.
2. The MLflow component of Databricks helps automate experiment tracking and governance. Once you have identified the best version of a model for production you can register it to the Model Registry to simplify handoffs along the deployment lifecycle.
3. It provides the capability to deploy ML models at scale and at low latency.
4. Databricks allows you to use Large Language Models (LLMs) which can be extended using techniques such as parameter-efficient fine-tuning (PEFT) or standard fine-tuning.
5. It can manage the full model lifecycle from data to production and back with model versions and other components.

Limitations of Azure Databricks

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:-

1. Dependency on Azure - Since Azure Databricks is a service provided by Microsoft Azure, any issues or outages in Azure can reflect the impact on Databricks workloads.
2. Versioning Tool Integration - Azure Databricks does not integrate with Git or any other versioning tool at the moment.
3. Limited control over infrastructure - Azure Databricks is a managed service and hence user has little control over its infrastructure.
4. Costs - Azure Databricks can prove to be expensive, especially when dealing with large-scale data processing and compute-intensive workloads.

Final Words

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.