Azure Databricks: A Brief Introduction
Azure Databricks is a fast-growing and widely used AI and data service on Azure. Over two exabytes per month of data are processed, on Azure Databricks, with millions of server-hours spinning up every day. But before discussing Azure Databricks, we should mention Apache Spark- the open-source, big data framework.
For many years Apache Spark has been the platform of choice for building predictive analytics, AI, and real-time applications. It provided an extremely rapid cluster computing technology, designed for fast computation within a scalable, massively parallel, in-memory execution environment. But with advantages also came in challenges like-complex deployment, resource management, and scalability.
Enter Databricks. It removes all the troubles and complexities involved in accessing a Sparks cluster. Databricks is an end to end managed Apache Spark platform optimized for the cloud. It features a single-click-deployment, autoscaling, and an optimized Databricks Runtime that can enhance performance, making Databricks a simple and cost-effective way to run large scale workloads.
Azure Databricks brings out the best of both Databricks and Azure. It provides a seamless, zero-management, Spark experience on the cloud without being bogged down by technicalities and configurations.
Why more and more companies are choosing Azure Databricks?
1. Multi-Language Support
Although Spark-based, Azure Databricks supports commonly used languages like Python, Scala, R, Java, and SQL. These languages are modified in the backend through APIs such as spark.api.java (for Java) and Spark SQL (for SQL), to interact with Spark. Other common analytics libraries and data science frameworks such as TensorFlow, PyTorch, and scikit-learn are pre-installed to be used with Spark to derive data insights.
Multi-language support within the same notebook is an advantage for those who are not proficient programmers, where it is easy to switch between different languages. This feature comes in handy when functions from different languages are needed. A good example would be switching from Python to R to use Auto Arima and switching back again to Python.
2. Higher Productivity and Collaboration
Apache Spark is widely known for its performance and speed, and Azure Databricks enhances its performance by offering efficient processing and faster caching resulting in improved successive read speeds. Databricks also offers indexing, and advanced querying compared to other big Data SQL analytics platforms.
Also, in cases of any worker instance being revoked or crashed, the Databricks cluster manager transparently relaunches itself with zero human intervention.
The autoscaling and auto termination feature helps manage costs.
Notebooks on Databricks offers real-time collaboration based on individual access level while supporting multiple languages (R, Python, SQL, and Scala) and libraries of choice. With real-time authoring, commenting, and automated version control, collaboration is made seamless while being in control. Thus, multiple members can collaborate for data model creation, machine learning, and data extraction. Moreover, integration with Power BI allows for an interactive visualizing experience.
3. Integration with Microsoft Stack
As Databricks is a native service on Azure, other Azure services like SQL Data Warehouse, Cosmos DB, Data Lake Store, and Blob Storage, etc. can be easily integrated and managed using a single click directly from Azure console.
4. Enterprise Security
Azure Databricks is one of the safest big data analytics platforms with enterprise-level security and compliance features available to all other services on the Microsoft Azure platform. It is integrated with Microsoft Azure Active Directory (AAD) with no additional configuration requirements. Users can log into their Azure Databricks workspace with their login credentials through the URL without any trouble.
Azure Databricks maintains Audit logs, when integrated with the cloud provided activity logging can be a powerful tracking tool for security and admin teams.
Admins of azure Databricks workspace can add, manage, and delete users through the admin console, and invite other users for collaboration as long as they are registered to another AAD. To sum up it can be said that Azure Databricks provides the best of Apache Spark while seamlessly integrating with other open source libraries. It also enables seamless collaborations between data engineers, data scientists or business analysts on shared projects using the interactive workspace and notebook experience.