Best Azure Data Engineer Training | Best Data Engineer Course in Hyderabad

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Introduction To ADF? Important Concepts in Azure
Data Factory
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• Introduction to Azure Data Factory (ADF)
• Azure Data Factory (ADF) is a cloud-based data
  integration service provided by Microsoft. It
  enables users to create, schedule, and
  orchestrate data pipelines that move and
  transform data from various sources to various
  destinations.
• This makes ADF an essential tool for
  organizations looking to manage and manipulate
  data at scale.

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Key Features of Azure Data Factory

• Scalability ADF can handle large volumes of
  data, making it suitable for enterprises of all
  sizes.
• Flexibility It supports a wide range of data
  sources and destinations, including on-premises
  and cloud-based systems.
• Cost-Effective With a pay-as-you-go pricing
  model, ADF allows organizations to manage costs
  effectively.

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Important Concepts in Azure Data Factory

• Pipelines
• Pipelines are the core component of ADF. They are
  groups of activities that perform data movement
  and transformation tasks. Each pipeline can
  consist of multiple activities, allowing for
  complex workflows.
• Activities
• Activities define the actions that need to be
  performed within a pipeline. They can be data
  movement activities (such as copying data from
  one location to another) or data transformation
  activities (such as running a Spark job to
  process data).

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• Datasets
• Datasets represent the data structures within
  ADF. They define the schema and format of the
  data to be consumed or produced by activities.
  Datasets can be created for various data sources
  like SQL databases, Azure Blob Storage, and more.
• Linked Services
• Linked Services are connections to external data
  sources. They store the connection information
  needed for ADF to access and interact with data.

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• Triggers
• Triggers in ADF are used to start pipelines. They
  can be scheduled to run at specific times or be
  event-based, triggering pipelines in response to
  data changes or external events.

• Integration Runtime (IR)
• ADF supports three types of IR Azure IR
  (cloud-based), Self-hosted IR (on-premises or in
  a virtual network), and SSIS IR (for running SQL
  Server Integration Services packages).

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• Mapping Data Flows
• Mapping Data Flows provide a code-free way to
  design and execute data transformation logic.
• They offer a visual interface for building
  complex transformations, making it easier for
  users to design data processing workflows without
  needing extensive coding knowledge.

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• Conclusion
• By understanding its core conceptspipelines,
  activities, datasets, linked services, triggers,
  integration runtimes, and mapping data
  flowsusers can effectively manage and process
  their data.
• ADF's scalability, flexibility, and
  cost-effectiveness make it an ideal solution for
  modern data engineering and analytics needs.

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CONTACT
For More Information About Best Azure Data
Engineer Training Address- Flat no 205, 2nd
Floor, Nilgiri
Block, Aditya Enclave,
Ameerpet, Hyderabad-16 Ph No 91-9989971070
Visit www.visualpath.in E-Mail
online_at_visualpath.in
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