Introduction
Performing data analysis is important as it helps you understand and make sense of raw data. Whatever your use case is, you can use data analysis to find patterns, spot unusual trends, and understand relationships within their data. Analyzing your data helps in making better decisions, improving operations, and achieving goals.
To perform data analysis, you need to ingest data into Azure Databricks and explore the data within the platform. You can ingest data you stored in sources like an Azure Data Lake, Azure SQL Database, or Azure Cosmos Database as Azure Databricks supports various data ingestion methods.
Once data is ingested, Databricks provides powerful tools for data exploration, including collaborative notebooks that support Python, Scala, SQL, and R. These notebooks enable teams to perform exploratory data analysis (EDA) efficiently, allowing for the visualization, manipulation, and examination of data to uncover patterns, anomalies, and correlations.