Azure Databricks provides a notebook-oriented Apache Spark as-a-service workspace environment. It is the most feature-rich hosted service available to run Spark workloads in Azure. Apache Spark is a unified analytics engine for large-scale data processing and machine learning.
Suppose you work with Big Data as a data engineer or a data scientist, and you must process data that you can describe as having one or more of the following characteristics:
- High volume - You must process an extremely large volume of data and need to scale out your compute accordingly
- High velocity - You require streaming and real-time processing capabilities
- Variety - Your data types are varied, from structured relational data sets and financial transactions to unstructured data such as chat and SMS messages, IoT devices, images, logs, MRIs, etc.
These characteristics are oftentimes called the "3 Vs of Big Data".
When it comes to working with Big Data in a unified way, whether you process it real time as it arrives or in batches, Apache Spark provides a fast and capable engine that also supports data science processes, like machine learning and advanced analytics.
In this module, you will:
- Understand the architecture of an Azure Databricks Spark Cluster
- Understand the architecture of a Spark Job