Core Concepts in azure data factory

Azure Data Factory
2- Main Core Concepts in Azure Data Factory
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Top core concepts in azure data factory
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 Linked Services
 Data Sets
 Data Flows
 Activities
 Pipelines
 Trigger

Azure Data Factory
Linked Services:
Linked services are used to connect other resources with Azure Data Factory, Linked
service like connection string to connect any resources with azure data factory .A
linked service will store the connection string. Linked Service is a connection to a
data source and/or destination.

Azure Data Factory
Data Sets:
Datasets represent data structures within the data stores, which simply points the
data you want to use in your activities as inputs or outputs.
An activity takes a zero or more datasets as inputs and one or more datasets as
outputs.
Example, an Azure Blob dataset specifies the blob container and folder in the Azure
Blob Storage from which the pipeline should read the data. Or, an Azure SQL Table
dataset specifies the table to which the output data is written by the activity.

Azure Data Factory
Data Flow:
Data flows are visually designed data transformations in Azure Data Factory. Data
flows allow data engineers to develop data transformation logic without writing code
An activity takes a zero or more datasets as inputs and one or more datasets as
outputs.

Azure Data Factory
Activities:
The activities in a pipeline define actions to perform on your data.
For example, you may use a copy activity to copy data from SQL Server to an Azure
Blob Storage. Then, use a data flow activity or a Databricks Notebook activity to
process and transform data from the blob storage to an Azure Synapse Analytics pool
on top of which business intelligence reporting solutions are built.

Azure Data Factory
Pipelines:
A pipeline is a logical grouping of activities that together perform a task.
For example, a pipeline could contain a set of activities that ingest and clean log
data, and then kick off a mapping data flow to analyze the log data. The pipeline
allows you to manage the activities as a set instead of each one individually. You
deploy and schedule the pipeline instead of the activities independently.

Azure Data Factory
Trigger:
Trigger determines when a pipeline execution needs to be kicked off.

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Core Concepts in azure data factory

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