Introduction
In this article, you will learn about Azure Data bricks and its services.
Prerequisites
Before we start with the overview on Azure Databricks, we should have a:
Background
I would like to explain the short introduction of ‘Apache Spark-based analytics platform before jumping into Azure data bricks.
Apache Spark-based analytics platform:
- It is an open-source parallel processing framework and fast-clustering computing system.
- It is leading platform large scale SQL data source, batch processing, stream processing, and machine learning (ML)
- It is great platform for big data distributed processing frameworks.
- Spark can be deployed in a variety of ways.
- It has native bindings for Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning, and graph processing.
Azure Databricks
- This is an enhanced platform of ‘Apache Spark-based analytics’ for Azure cloud meaning data bricks works on the ‘Apache Spark-based analytics’ which is most advanced high-performance processing engine in the market now.
- It also provides a great platform to bring data scientists, data engineers, and business analysts together.
- It provides end-to-end solution for all types of data, analytics and build the artificial intelligence (AI).
- Azure data brick Apache Spark environment set-up takes a few minutes only.
- It supports Python, Scala, R, Java and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch and scikit-learn.
Image Source – Microsoft Docs
- Most of the time, the raw/structured data is pushed using Azure Data Factory or real-time with any other technique such as Kafka to the Azure.
- This data is stored in the Azure storage like blob or data lake etc.
- Azure data bricks this data from one or multiple data stores in Azure and turn in to insights using Spark.
- Azure data bricks have tight integration with Azure data stores like ‘SQL Data Warehouse, Cosmos DB, Data Lake Store, and Blob Storage’ as well as the BI tool like Power BI to view and share the impactful insights.
Image Source – Microsoft docs
Azure Data Factory Tangible Benefits
- Fully managed Apache Spark clusters in the cloud:
- It has secured and reliable production environment in the Azure cloud.
- Environment is managed and supported by Spark experts in the Azure cloud.
- We can create clusters in seconds, auto scale the clusters.
- Use secure data integration capabilities on top of Spark.
- We can access the clusters using REST APIs.
- Databricks Runtime - With the Serverless option data scientists iterate quickly as a team.
- It is tightly integrated with Azure and Spark.
- It is collaborative and integrated environment, Azure Databricks streamlines the process of exploring data, prototyping, and running data-driven applications in Spark.
- It has enterprise security, such as integration with Azure Active Directory, role-based access etc.
Reference Links
- https://azure.microsoft.com/en-in/services/databricks/
- https://azure.microsoft.com/en-in/resources/videos/azure-databricks-overview/
- https://docs.microsoft.com/en-us/azure/azure-databricks/what-is-azure-databricks
- https://databricks.com/blog/2017/11/15/introducing-azure-databricks.html
- https://databricks.com/blog/2017/11/15/a-technical-overview-of-azure-databricks.html
Conclusion
In this article, we have seen an overview of Azure Data bricks and its services.