Azure Databricks is a fast-growing and widely\nused AI and data service on Azure. Over two exabytes per month of data are\nprocessed, on Azure Databricks, with millions of server-hours spinning up every\nday. But before discussing Azure Databricks, we should mention Apache Spark-\nthe open-source, big data framework. <\/p>\n\n\n\n\n\n\n\n
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. <\/p>\n\n\n\n
Enter\nDatabricks. It removes all the troubles and complexities involved in accessing\na Sparks cluster. Databricks is an end to end managed Apache Spark platform\noptimized for the cloud. It features a single-click-deployment, autoscaling,\nand an optimized Databricks Runtime that can enhance performance, making\nDatabricks a simple and cost-effective way to run large scale workloads.<\/p>\n\n\n\n
Azure\nDatabricks brings out the best of both Databricks and Azure. It\nprovides\na seamless, zero-management, Spark experience on the cloud without being bogged down\nby technicalities and configurations. <\/p>\n\n\n\n
<\/p>\n\n\n\n
Although\nSpark-based, Azure Databricks supports commonly used languages\nlike Python, Scala, R, Java, and SQL. These languages are modified in the backend\nthrough APIs such as spark.api.java (for Java) and Spark SQL (for SQL), to\ninteract with Spark. Other common analytics libraries and data science\nframeworks such as TensorFlow, PyTorch, and scikit-learn are pre-installed to\nbe used with Spark to derive data insights. <\/p>\n\n\n\n
Multi-language\nsupport within the same notebook is an advantage for those who are not\nproficient programmers, where it is easy to switch between different languages.\nThis feature comes in handy when functions from different languages are needed.\nA good example would be switching from Python to R to use Auto Arima and\nswitching back again to Python.<\/p>\n\n\n\n
Apache Spark is widely known for its performance and speed, and\nAzure Databricks enhances its performance by offering efficient processing and\nfaster caching resulting in improved successive read speeds. Databricks also\noffers indexing, and advanced querying compared to other big Data SQL analytics\nplatforms.<\/p>\n\n\n\n
Also, in cases of any worker instance being revoked or crashed,\nthe Databricks cluster manager transparently relaunches itself with zero human\nintervention.<\/p>\n\n\n\n
The autoscaling and auto termination feature helps manage costs.<\/p>\n\n\n\n
Notebooks on Databricks offers real-time collaboration based on\nindividual access level while supporting multiple languages (R, Python, SQL,\nand Scala) and libraries of choice. With real-time authoring, commenting, and\nautomated version control, collaboration is made seamless while being in\ncontrol. Thus, multiple members can collaborate for data model creation,\nmachine learning, and data extraction. Moreover, integration with Power BI\nallows for an interactive visualizing experience.<\/p>\n\n\n\n
As Databricks is a native service on Azure, other Azure services\nlike SQL Data Warehouse, Cosmos DB, Data Lake Store, and Blob Storage, etc. can\nbe easily integrated and managed using a single click directly from Azure\nconsole.<\/p>\n\n\n\n
Azure Databricks is one of the safest big data analytics\nplatforms with enterprise-level security and compliance features available to\nall other services on the Microsoft Azure platform. It is integrated with\nMicrosoft Azure Active Directory (AAD) with no additional configuration\nrequirements. Users can log into their Azure Databricks workspace with their\nlogin credentials through the URL without any trouble.<\/p>\n\n\n\n
Azure Databricks maintains Audit logs, when integrated with the\ncloud provided activity logging can be a powerful tracking tool for security\nand admin teams.<\/p>\n\n\n\n
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. <\/p>\n\n\n\n
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