What is Azure Databricks? Main Features and Ideal Use Cases for Your Business

Microsoft Azure stands at the forefront of the growing AI adoption and analytics market. In particular, nearly 60% of CIOs across industries plan to increase Azure spending over the next year, with 97% expecting to adopt Microsoft AI tools.

Azure supports advanced architectural solutions built on high-quality data management, offering a full suite of services for AI development, data orchestration, and analytics.

One such service is Azure Databricks, a comprehensive data analytics and AI platform that can help companies increase ROI by optimizing data management workflows and building resource-efficient architectures. For example, Databricks already helped one of the world’s largest telecommunications companies, AT&T, achieve a five-year ROI of 300%.

Thanks to a variety of functions that Azure Databricks can cover, the popularity of this service is booming, as Databricks globally spins up more than 10 million virtual machines a day.

In this article, we will describe Azure Databricks, the core capabilities of this cloud data platform, and use cases for businesses in more detail.

Azure Databricks is a unified, open data analytics platform. It is used for building, deploying, sharing, and maintaining enterprise-grade data, analytics, and AI solutions. The image below illustrates its core data intelligence capabilities.

Here are the core features of Azure Databricks:

• Unified analytics. Azure Databricks combines features for data engineering, analytics, and AI.
• Built-in ML and AI lifecycle. The platform provides a comprehensive suite of tools for building, training, and deploying models.
• MLflow integration. This feature allows Azure Databricks users to track ML experiments, organize models, and deploy them to production.
• Collaborative notebooks. Databricks provides developers with shared workspaces for coding and analysis.
• Delta Lake integration. Through this integration, Azure Databricks enables its users to create data lakes that comply with ACID transactions principles that ensure reliability and consistency of operations.
• Distributed data processing. By using the capabilities of Apache Spark, Azure Databricks supports scalable processing of large datasets.

Azure Databricks works efficiently as a part of a larger Azure-based ecosystem. For example, it can seamlessly integrate with a variety of Azure services, including:

• Microsoft Entra ID. Through the native integration with Microsoft Entra ID, Azure Databricks enables managed access control and authentication.
• Azure Data Lake Storage. Databricks can directly read and write data from the most up-to-date version of Azure Data Lake Storage (ADLS Gen2) to ensure efficient data processing and analytics. It also seamlessly integrates with other Azure Storage platforms, such as legacy Data Lake versions and Blob Storage.
• Azure Monitor and Log Analytics. Users can conveniently monitor Azure Databricks workflows with Azure Monitor and gain insights through Log Analytics.
• Power BI. Azure Databricks can serve as a data source for Power BI, enabling fast and efficient analytics. Users can publish Power BI reports while accessing Databricks data through single sign-on (SSO) using their Microsoft Entra ID credentials. With a Premium Power BI license, they can use Direct Publish from Databricks, creating datasets from Unity Catalog tables and schemas directly in the Databricks UI. Databricks also supports Direct Lake mode, a premium Power BI feature that lets users analyze very large data volumes by reading Parquet files directly from a data lake. All these features reduce manual data handling, speeding up business intelligence workflows.
• Azure OpenAI. Databricks provides built-in support for ML workflows. In particular, users can apply AI Functions to access large language models (LLMs) directly from SQL. As a result, they can apply a familiar SQL interface while experimenting with LLMs and turn the prompt quickly into a production pipeline using tools like Delta Live Tables or scheduled jobs.
• Microsoft Purview. This integration provides users with centralized data governance capabilities, enabling convenient discovery and cataloging for datasets used in Databricks workflows. They can also use Microsoft Purview to track data lineage, manage access policies, and maintain visibility over data storage and processing workflows.
• Azure Data Factory (ADF). Integration with ADF allows users to natively ingest data to the Azure cloud from over 100 different data sources. The service also provides features for graphical data orchestration and monitoring capabilities for efficient curation of data in data lakes and warehouses.

What is Azure Databricks used for? Here is an overview of the common Azure Databricks use cases that can bring maximum value to your business.

Integrating LLMs and generative AI into your system

Azure Databricks provides a set of tools for data science and ML engineering. In particular, the platform provides a Databricks Runtime for Machine Learning. It includes libraries, such as Hugging Face Transformers, that allow AI developers to integrate existing pre-trained models into their workflow. This eliminates the need for building the models from scratch and creates possibilities for fast experimentation with ML models. By using Hugging Face in combination with a framework for training models, such as DeepSpeed, teams can take a foundation LLM and efficiently train it with proprietary, company-specific data.

Integration with MLflow is another important property of Databricks. AI developers can use the MLflow tracking service with transformer pipelines, models, and processing components. Azure Databricks architecture also integrates seamlessly with OpenAI models, enabling fast and effortless deployment of the latest AI capabilities. For instance, Azure Databricks includes built-in AI functions that let SQL data analysts use large language models, such as those from OpenAI, directly inside their queries, data pipelines, and everyday workflows.

Finally, Databricks provides strong capabilities for data engineering. It integrates with Apache Spark, a big data engine for fast, distributed data processing and analytics, and Delta Lake, a storage layer that makes transactions to data lakes more reliable. Used in combination with custom tools, these integrations make Databricks an efficient solution for building reliable ETL pipelines. These pipelines simplify job orchestration, allowing scheduled deployments with just a few clicks. The platform also provides powerful tools for data ingestion from various sources in distributed infrastructures.

Overall, built-in AI integration and data engineering capabilities make Azure Databricks a core service for AI-powered infrastructures, supporting model integration, customization, and fast deployment.

Ensuring centralized data governance

Gartner expects that by 2028, half of all organizations will shift to a zero-trust approach in data governance, driven by the growing volume of unverified AI-generated data. As the importance of data governance and data source visibility increases, the role of high-quality metadata becomes more critical. Azure Databricks addresses this challenge with Unity Catalog, a centralized governance layer that provides unified access control and data lineage for data, AI models, and other assets within the Databricks environment.

Meanwhile, if you need to ensure a broader, enterprise-level data governance across the entire Azure environment, you can seamlessly integrate Databricks with Microsoft Purview. This approach provides you with a cross-service visibility rather than workspace-level control that can be achieved with Unity Catalog.

In sum, these data governance features help organizations enforce consistent security policies across their assets, ensure regulatory compliance with built-in monitoring, and gain complete visibility of their data ecosystem.

Building an enterprise data lakehouse architecture

Azure Databricks is an efficient solution for enterprise-grade solutions because the platform can be used for building extensive data lakehouse infrastructures. Such an architectural approach combines the organization and reliability of data warehouses with the flexibility of data lakes. Data engineers, data scientists, analysts, and production systems can all use the data lakehouse as a single source of truth. It provides access to consistent data across teams. This approach also reduces the complexity of building, maintaining, and syncing multiple distributed data systems.

The Unity Catalog feature also ensures a unified and convenient data governance model for the lakehouse. Cloud administrators can configure and integrate coarse access control permissions, while Azure Databricks administrators can manage these permissions for teams and individuals.

This approach allows teams to build a unified, secure, and well-governed enterprise data platform that sets the foundation for advanced analytics and AI workflows.

Supporting efficient analytics and BI

Databricks provides a powerful platform for running analytical queries. The workflow is quite simple:

1. Administrators configure scalable compute clusters as SQL warehouses, allowing end users to execute queries.
2. SQL users can run queries against data in the lakehouse using the SQL query editor or Notebooks.
3. Users can embed analytical visualization thanks to Notebooks’ support for Python, R, Scala, and SQL.

The integration with tools like Power BI enables Databricks users to create detailed dashboards with visualizations and customizable analytical insights. To boost analytics, the teams can use the real-time data streaming capabilities of Azure Databricks. Incremental data changes and data streaming workflows are handled through the integration with Apache Spark Structured Streaming.

Finally, Databricks integrates with the Lakebase, an efficient online transactional processing (OLTP) database designed to handle lots of everyday transactions quickly and reliably. It allows organizations to run operational databases without managing infrastructure, which ultimately accelerates application development while keeping data seamlessly integrated for analytical workflows.

Azure Databricks provides capabilities for fast, flexible data streaming and transformation. These features make it an effective solution for supporting complex data analytics workflows.

Establishing reliable DevOps and task orchestration workflows

Azure Databricks is also an efficient solution for DevOps workflows. For instance, the service can be used for creating a single data source for all users, reducing duplicate efforts and out-of-sync reporting. The platform also provides a suite of tools for versioning, automating, scheduling, and deploying code and production resources.

Databricks Asset Bundles let teams define, deploy, and run resources like jobs and pipelines through code. Meanwhile, Git folders enable projects in Azure Databricks to stay synchronized with popular Git providers, helping maintain a consistent codebase.

Such capabilities help organizations manage data and AI environments with consistent and up-to-date DevOps practices. The critical business benefits of such an approach include greater reliability, speed of delivery cycles, and more convenient maintenance of data platforms.

Azure Databricks provides strong capabilities for building and managing complex data architectures. However, its configuration involves a complex setup across networking, security, cluster management, and integrations with other Microsoft Azure services. Misconfigurations can go beyond minor system inefficiencies, potentially leading you to significant performance issues, overspending, and security challenges. To implement strong data governance, optimize workloads, and ensure reliable and scalable data pipelines with Databricks, you will need strong technical experience.

At Leobit, Microsoft services at the core of our expertise. Our specialists have extensive experience with Microsoft business intelligence and data management technologies. We are skilled in designing flexible data architectures and working with services like Azure Databricks, Azure Data Factory, Azure Arc, and Microsoft Fabric.

We have shown consistent quality in helping our customer manage data across multiple environments, as well as building advanced analytics and AI solutions powered with the tools from the Azure stack. This expertise has granted us the status of a Microsoft Solutions Partner for Data & AI. We are also the Microsoft Solutions Partner for Digital and App Innovation, with extensive Azure development experience gained from over 45 successful projects.

Our specialists also have strong expertise in developing AI-powered solutions that leverage high-quality proprietary data. We can configure AI-powered analytics, integrate AI and machine learning into your Azure Databricks architecture, and develop custom large language models tailored to your needs.

By combining this technical depth with a client-focused approach, Leobit can help you build a reliable and flexible data architecture with Azure Databricks as its backbone, optimize the existing infrastructure, or expand it with reliable AI models.

Azure Databricks provides a variety of tools for building and running complex data architectures, including those that involve AI models and heavy analytics. The platform ensures unified data governance and seamlessly integrates with services for AI development, BI, DevOps, etc. With such capabilities, Azure Databricks can sit at the core of a modern data architecture. It fits companies that need to:

• Integrate LLMs and generative AI into their systems
• Keep data governance centralized and consistent
• Build a flexible and scalable data lakehouse
• Run efficient analytics and BI workflows
• Set up clean DevOps and orchestration processes

The platform is powerful, but not exactly simple. To configure all its features and integrations properly, you will need technical experience. Leobit has solid expertise with Microsoft services and complex data systems. Whether you want to use Azure Databricks for AI integrations or build full-scale data architectures, we can help you design everything properly and avoid costly mistakes.

Reach out to discuss your case and see how we can help you.