1.Azure databricks in telugu | Azure databricks introduction|#azuredatabricks #pyspark #azure

Azure Databricks provides the latest versions of Apache Spark and allows you to seamlessly integrate with open source libraries. Spin up clusters and build quickly in a fully managed Apache Spark environment with the global scale and availability of Azure. Clusters are set up, configured, and fine-tuned to ensure reliability and performance without the need for monitoring. Take advantage of autoscaling and auto-termination to improve total cost of ownership (TCO).

Azure Databricks is optimized for Azure and tightly integrated with Azure Data Lake Storage, Azure Data Factory, Azure Synapse Analytics, Power BI and other Azure services to store all your data on a simple, open lakehouse and unify all your analytics and AI workloads.

50x performance for Apache Spark™ workloads:
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Deploy auto-scaling compute clusters with highly optimized Spark that perform up to 50x faster.

Ease of use:
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Start with a single click in the Azure Portal, natively integrate with Azure security and data services, and boost productivity by up to 25% with collaborative data engineering and data science.

Azure Databricks has a support for Python, Scala, R and SQL and some libraries for deep learning like Tensorflow, Pytorch and Scikit-learn for building big data analytics and AI solutions. In Azure Databricks notebooks, the user can easily switch between different programming languages with just simple language commands to make use of more languages in one notebook.

Running a job on the cluster in Azure Databricks, means running a notebook, either manually or by scheduling it to run at a specific time. Azure Databricks provides different users in the organization the possibility to collaborate on shared projects in one workspace.