Capacity: This is like the amount of resources available for doing stuff at any given time. Different tasks need different amounts of capacity. Fabric provides this capacity through things like Fabric SKU and Trials.
Experience: Imagine this as a bundle of tools designed for a specific job. Fabric offers different experiences like Synapse Data Warehouse, Synapse Data Engineering, etc., each tailored for a particular task.
Item: An item is basically a set of tools within an experience. You can create, edit, or delete these items. Each item type offers different tools. For example, in Data Engineering, you have items like lakehouse, notebook, and Spark job definition.
Tenant: This is like a single instance of Fabric for a company, tied to their Microsoft Entra ID.
Workspace: A workspace is like a folder where you can put all your stuff related to a project. It helps organize things and controls who can access what.
Now, let’s dive into specific areas:
Synapse Data Engineering:
- Lakehouse: This is where all your data lives, organized neatly into files, folders, and tables. It’s hosted within OneLake.
- Notebook: Think of this as a tool for writing and running code. It’s great for exploring and processing data, and even building machine learning experiments.
- Spark application and job: These are parts of a program that run in parallel to process data quickly. A job is made up of tasks that do specific parts of the work.
Data Factory:
- Connector: This lets you connect to different data stores so you can move and transform data.
- Data pipeline: It’s used for moving and changing data around.
- Dataflow Gen2: This is like a tool for ingesting and transforming data, but it’s more advanced compared to the older version.
Synapse Data Science:
- Data Wrangler: An easy-to-use tool for exploring and cleaning up data.
- Experiment: This is where you organize and control all your machine learning tests.
- Model: A file that’s trained to recognize patterns in data.
Synapse Data Warehouse:
- SQL analytics endpoint: It lets you query data in a lakehouse using SQL.
- Synapse Data Warehouse: Acts like a traditional data warehouse, supporting all the usual SQL capabilities.
Synapse Real-Time Analytics:
- KQL database: Holds data for running KQL queries.
- KQL Queryset: Used for running, viewing, and manipulating queries on your data.
- Event stream: Captures, transforms, and sends real-time events to where they need to go.
OneLake:
- Shortcut: A quick way to connect to existing data without moving it around. It’s like a reference within OneLake.