Databricks, offered on Azure and AWS, is a cloud-based data science and engineering platform. Here are some key concepts to understand how Databricks works:

Accounts and Workspaces:

• An account is your entry point to Databricks, and a workspace is your designated area within the platform for working with data. Think of your workspace as a collaborative environment where your team can access Databricks resources.

Databricks Runtime:

• This refers to the software environment that runs on clusters you create in Databricks. It includes Apache Spark, libraries, and other data processing tools. Databricks offers different runtime versions optimized for specific use cases, like machine learning.

Clusters and Jobs:

• Clusters are groups of virtual machines that provide the processing power to execute data jobs. You create and manage clusters within your workspace. Databricks offers different cluster configurations depending on your workload’s needs. Jobs are the tasks you run on clusters, such as data analysis scripts written in notebooks.

Data Management:

• Databricks allows you to import data from various sources, such as cloud storage, databases, and data lakes. You can then manage and organize this data within your workspace using tables. Databricks supports different data formats, such as Delta Lake, Parquet, and Avro.

Notebooks:

• Notebooks are web-based interfaces where data scientists and engineers write code, perform data analysis, create visualizations, and build machine learning models. Databricks supports notebooks in languages like Python, Scala, and R.

Other Concepts:

• Databricks offers features like SQL access for data querying, visualizations for data exploration, and collaboration tools for teamwork. Billing is based on Databricks Units (DBUs), a unit of processing power consumed per hour.