Simply put, Databricks is how Apache Spark is implemented on Azure. Large data workloads may be processed utilising fully managed Spark clusters, which are particularly helpful for data engineering, data exploration, and machine learning-based data visualisation.
As I worked on databricks, I discovered how flexible and developer-friendly this analytic platform is, with easy-to-use APIs like Python, R, etc. To further illustrate this, let's take the example of a Python data frame that you have developed. Using Azure Databricks, you can load this data into a temporary view, from which you can then utilise Scala, R, or SQL using a pointer referencing this temporary view. As a result, you can code in various languages simultaneously in the same notebook. One of its interesting features was simply this.
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How come Azure Databricks?
Evidently, there are a few reasons why using Databricks is becoming more important and relevant in the big data environment. In addition to supporting different languages, this service enables us to quickly interact with a variety of Azure services, including SQL Database, Blob Storage, Data Lake Store, and BI tools like Power BI, Tableau, etc. It is a fantastic tool for collaboration that enables data professionals to share workspaces and clusters, increasing productivity.