What Is Azure Databricks: Uses, Features, And Architecture

Organizations accumulate massive amounts of data pertaining to operations, marketing, sales, and more. To utilize the full potential of such data, it’s essential to leverage the power of data integration and analytics. With data integration, you can seamlessly integrate data from diverse sources and load it into a destination system. However, with data analytics, you can uncover meaningful insights and patterns within the dataset. Azure Databricks provides an integrated environment to help meet these requirements for streamlined data management.

In this article, you will understand what is Azure Databricks, its features, architecture, and the various applications it offers. 

What is Azure Databricks?

Azure Databricks, an analytics platform developed in collaboration with Microsoft, is optimized for the Microsoft Azure cloud services ecosystem. It is built on Apache Spark, an open-source distributed computing framework, to provide scalable data processing capabilities, interactive analytics, and streamlined machine learning tasks. Azure Databricks provides a collaborative environment for data scientists, engineers, and analysts to generate dashboards and visualizations, share insights, and optimize data workflows. 

Azure Databricks Features

Azure Databricks offers a range of features designed to scale business activities, thereby enhancing collaboration and efficiency in data processing and analytics. Let’s look at some of the key features:

Unified Platform Experience

It is an easily accessible first-party Azure service that is entirely managed on the Azure interface. Azure Databricks is natively linked with other Azure services, allowing access to a wide range of analytics and AI use cases. This native integration helps unify workloads, reduces data silos, and supports data democratization. Data analysts and engineers can collaborate across various tasks and projects efficiently.

Perform Seamless Analytics

Azure Databricks SQL Analytics allows you to execute SQL queries directly on the data lake. This feature includes a workspace where you can write SQL queries, visualize the results, and create dashboards similar to a traditional SQL workbench. Additional tools include query history, a sophisticated query editor, a catalog, and capabilities to set up alerts based on SQL query outcomes.

Flexible and Open Architecture

Azure Databricks supports a diverse range of analytics and AI workloads with its optimized lakehouse architecture built on an open data lake. This architecture allows the processing of all data types. 

Depending on the workload, you can leverage a range of endpoints, such as Apache Spark on Azure Databricks, Azure Machine Learning, Synapse Analytics, and Power BI. The platform also supports multiple programming languages, including Scala, Python, R, and SQL, in addition to libraries such as TensorFlow and PyTorch.

Efficient Integration

Azure Databricks integrates seamlessly with numerous Azure services such as Azure Blob Storage, Azure Event Hubs, and Azure Data Factory. This enables you to effortlessly create end-to-end data pipelines to ingest, manage, and analyze data in real time.

Azure Databricks Architecture

It is essential to understand the underlying architecture of Azure Databricks to perform efficient integrations and ensure a streamlined workflow. Azure Databricks is designed around two primary architectural components—the Control and Compute Plane. Let’s explore these components in detail:

Control Plane

It is a management layer where Azure Databricks handles the workspace application and manages notebooks, configurations, and clusters. This plane includes the backend services operated by Azure Databricks within your account. For example, the web application you interact with is part of the Control Plane.

Compute Plane

It is where your data processing tasks occur in Azure Databricks. The Compute Plane is subdivided into two categories based on the usage:

• Classic Compute Plane: In the classic compute plane, you can utilize Azure Databricks computing resources as part of your Azure subscription. Computing resources are generated within the virtual network of each workspace located in the customer’s Azure subscription. This ensures that the Classic Compute Plane operates with inherent isolation, as it runs within the customer’s controlled environment.‍
• Serverless Compute Plane: In the serverless model, Azure Databricks manages the compute resources within a shared infrastructure. This plane is designed to simplify operations by eliminating the need to manage underlying compute resources. It features multiple layers of security to protect data and isolate workspaces. This helps ensure the infrastructure is shared while each customer’s data and resources remain private.

What is Azure Databricks Used For?

Azure Databricks is a versatile platform that serves multiple data processing and analytics needs. Here are some of the primary uses of the platform:

ETL Data Processing

Azure Databricks offers a robust environment for performing extract, transform, and load (ETL) operations, leveraging Apache Spark and Delta Lake. You can build ETL logic using Python, SQL, or Scala and then easily orchestrate scheduled job deployment. This ensures your data is efficiently processed, cleaned, and organized into models that enable efficient discovery and utilization.

Streaming Analytics

Azure Databricks utilizes Apache Spark Structured Streaming to manage streaming data and incremental data updates. The platform processes incoming streaming data in near real-time, continuously updating outputs as new data arrives. This capability makes Azure Databricks suitable for real-time data ingestion, processing, and analysis, as well as for deploying ML and AI algorithms on streaming data.

Data Governance

Azure Databricks supports a strong data governance model through the Unity Catalog, which integrates seamlessly with its data lakehouse architecture. Once the cloud administrators have configured and integrated coarse-grained access controls, the Azure Databricks administrators can fine-tune permissions for your team at a more granular level. Additionally, complete access control lists (ACLs) are available and are managed through user-friendly UIs or SQL syntax to secure data access and control.

Azure Databricks vs Azure Data Factory

Till now, you have understood the basics of Azure Databricks, its features, and its benefits. While it is a robust data analytics platform, some people often confuse it with another Microsoft platform, the Azure Data Factory. Each platform offers different services and is tailored to suit specific business requirements. In this section, let’s look into the key differences between Azure Databricks and Azure Data Factory:

Azure Databricks vs Data Factory: Focus

Azure Databricks is a cloud-based platform for big data processing and analytics. It allows your business analysts and engineers to leverage machine learning models to analyze datasets.

On the other hand, Azure Data Factory is a fully managed data integration service. It employs ETL and ELT approaches to extract data from multiple sources using its extensive set of built-in connectors.

Azure Databricks vs Data Factory: Data Integration

Azure Databricks is actively integrated with other Azure services for analytics but doesn’t primarily handle data integration tasks. This empowers you to focus less on combining data and spend more time on data analysis and visualization.

In contrast, Azure Data Factory provides 90+ built-in connectors for various data sources and destinations, facilitating pipeline orchestration. You can utilize its intuitive interface or write custom code to perform processes like ETL and ELT.

Azure Databricks vs Data Factory: Ease of Use

Both platforms are tailored to work efficiently and flexibly, with minor differences. Azure Databricks offers a flexible environment where you can work with multiple programming languages such as Python, R, Java, Scala, or SQL.

In comparison, Azure Data Factory provides a user-friendly interface with a drag-and-drop feature to create, schedule, and monitor data integration workflows.

Optimizing Data Integration with Azure Databricks Using Airbyte

Now that you’ve seen how Azure Databricks excels at performing complex data analytics, you might want to know how to integrate data from disparate sources to leverage this analytical capability. Consider using Airbyte for this purpose.

Used by 40,000+ engineers, Aibyte is a self-hosted ELT platform that allows you to seamlessly gather data from various sources such as flat files, databases, and SaaS applications. Once the data is collected, you can load it into a data lake or warehouse. Airbyte offers a rich library of 350+ pre-built connectors that facilitate automated pipeline creation within minutes. If you can’t find a connector of your choice, you can also build custom connectors using CDK or request one by contacting their support team.

Beyond integration capabilities, Airbyte also possesses data replication features such as Change Data Capture. This allows you to identify and capture changes made at the source, ensuring that data is consistently replicated and up-to-date in the target system.

Some of the unique features of Airbyte are:

• Developer-Friendly UI: Airbyte recently launched its open-source Python library, PyAirbyte. This library allows you to quickly design and create data pipelines using Python.‍
• Data Scheduling: It provides various scheduling methods, such as scheduled, cron-based, and manual syncing. You can use cron and scheduled methods to sync connections at a specified time. Manual syncing allows you to perform scheduling at your own pace.‍
• Security Features: To ensure data integrity, Airbyte employs various security measures. These include audit logs, credential management, encryption, access controls, and authentication mechanisms.‍
• Community Support: You can collaborate with Airbyte’s large, vibrant community of 15,000+ members. The platform encourages collaboration, allowing you to discuss best integration practices, share articles or resources, and resolve data-ingestion queries.

Final Thoughts

Azure Databricks is a robust platform with impressive data management and analytic capabilities. With its diverse set of features, it empowers businesses to uncover hidden patterns, identify trends, and make data-driven decisions. Additionally, its integration with the Azure ecosystem helps you leverage advanced analytics and streamline data workflows. However, it would be advantageous to store your data in a centralized repository for effective analytics and visualization tasks.

To fulfill diverse data integration needs, we suggest using Airbyte since it follows a modern ELT approach for extracting data from varied sources. Sign in to the platform today and navigate its various features.