These days Data place an important roles in our day to day life and there are plenty of data available, which in terms becomes difficult to manage by Data Professionals. But we have smart tool to manage our large amount of data, which is called Azure Databricks. In this simple article, we will have a look on an Overview of Azure Databricks.
Before getting started, its always good to have fundamental knowledge in Azure Portal, Azure Fundamentals and also some good understanding of Azure Data Services.
First thing first, “What is Azure Databricks?.” Azure Databricks is a fully managed Apache Spark based could platform, that can be used for Big Data processing and Machine Learning. Microsoft and Databricks collaborated and mainly focused on Data and AI which was founded in the year 2013. They created some tools such as Apache Spark, Delta Lake and MLFlow which are main components that makes Databricks.
Azure Databricks provides an interactive workspace which allows for production workflow that can be automated and also important to know that this entire workspace is fully managed.
Features of Azure Databricks
- Leverage spark for Streaming, Machine Learning, Graph API and SQL.
- It support multiple languages as Scala, Python, Java, R and SQL
- It can be easily integrated with Azure Active Directory (AD).
- Easy integration with Azure Services
Components of Azure Databricks
The first component is Azure Databricks Workspace. Workspace is an interactive tool that can be used for exploring and visualization of the data. Within the workspace we have Apache Spark Cluster that can be created in seconds and auto scale and share across the Users.
The another component is Apache Spark Notebook, that can be used to read, write, query, explore, and visualize datasets. These Notebooks are connected to the Clusters.
ETL in Azure Databricks
The process of bringing data from one location, transforming the data and loading it into a destination. We can load data into Databricks which can be mounted up to an Azure Storage. In Azure, the ETL is done using the tool Azure Data Factory. This is an hybrid tool to integrate with data projects. Some of the benefits of Azure Data Factory are,
- Connect to more Data Sources
- Creating Pipelines
- Job Scheduling those Pipelines
- Visual Interface
Machine Learning in Azure Databricks
We can run Machine Learning projects in Azure Databricks using the Databricks Runtime. In simple terms, it automates the created clusters that are optimized for Machine Learning. We can also use Machine Learning libraries that are commonly used across.
Once is data models are prepared in the Notebook, we need to monitor and manage those models. Within Azure we have MLFlow for this, which helps to manage end to end ML lifecycle such as Deployment, tracking and etc.
In this article we discussed about the Azure Databricks with an overview. I hope this article will help many of us to get started with Azure Databricks. Lets explore more concepts in Azure Databricks in our upcoming articles. Please share your feedbacks in the comment section.
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