3. azure data factory components | Azure data factory
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In this video we will discuss about azure data factory components
Data Flows: Take a deep dive into Azure Data Factory's Data Flows and discover how to visually design and execute complex data transformations. From cleansing and aggregating to joining and enriching data, learn how Data Flows empower you to prepare your data for analytics and insights effortlessly.
Pipelines: Unleash the potential of Azure Data Factory's Pipelines as the backbone of your data integration workflows. Explore how to orchestrate activities seamlessly, ensuring smooth data movement, transformation, and processing across diverse sources and destinations. From simple to intricate pipelines, master the art of orchestrating data with ease.
Activities: Explore the versatility of Azure Data Factory's Activities and learn how they serve as the building blocks of your data pipelines. From copying and transforming data to executing custom scripts and managing dependencies, discover how Activities empower you to accomplish a myriad of data integration tasks efficiently.
Datasets: Discover the significance of Datasets in Azure Data Factory and learn how they define the structure and characteristics of your data sources and destinations. From relational databases to cloud storage, explore how to configure Datasets to seamlessly ingest and process data from various environments.
Linked Services: Delve into the world of Linked Services and understand how they establish connections to external data sources and destinations in Azure Data Factory. From authentication to connectivity details, learn how to configure Linked Services to access and interact with your data wherever it resides.
Triggers: Automate your data workflows with Azure Data Factory's Triggers and ensure timely execution based on predefined conditions or schedules. Explore the different trigger types, from schedule-based to event-driven, and discover how to schedule, monitor, and manage your pipelines effortlessly.
Integration Runtimes: Gain insights into Azure Data Factory's Integration Runtimes and learn how they provide the necessary compute resources for executing data integration tasks. From configuring environments to optimizing performance, explore how Integration Runtimes enable seamless data processing across diverse infrastructures.
Monitoring and Management Tools: Learn how to effectively monitor and manage your data integration solutions with Azure Data Factory's built-in tools. From tracking pipeline performance to troubleshooting issues and optimizing resources, discover how these tools empower you to ensure the reliability and efficiency of your data workflows.
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AWS DATA ENGINEER : AWS DATA ENGINEER
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what we have covered in this video:
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Hope you liked this video and learned something new :)
See you in next video, until then Bye-Bye!
➖➖➖➖➖➖➖➖➖➖➖➖➖
In this video we will discuss about azure data factory components
Data Flows: Take a deep dive into Azure Data Factory's Data Flows and discover how to visually design and execute complex data transformations. From cleansing and aggregating to joining and enriching data, learn how Data Flows empower you to prepare your data for analytics and insights effortlessly.
Pipelines: Unleash the potential of Azure Data Factory's Pipelines as the backbone of your data integration workflows. Explore how to orchestrate activities seamlessly, ensuring smooth data movement, transformation, and processing across diverse sources and destinations. From simple to intricate pipelines, master the art of orchestrating data with ease.
Activities: Explore the versatility of Azure Data Factory's Activities and learn how they serve as the building blocks of your data pipelines. From copying and transforming data to executing custom scripts and managing dependencies, discover how Activities empower you to accomplish a myriad of data integration tasks efficiently.
Datasets: Discover the significance of Datasets in Azure Data Factory and learn how they define the structure and characteristics of your data sources and destinations. From relational databases to cloud storage, explore how to configure Datasets to seamlessly ingest and process data from various environments.
Linked Services: Delve into the world of Linked Services and understand how they establish connections to external data sources and destinations in Azure Data Factory. From authentication to connectivity details, learn how to configure Linked Services to access and interact with your data wherever it resides.
Triggers: Automate your data workflows with Azure Data Factory's Triggers and ensure timely execution based on predefined conditions or schedules. Explore the different trigger types, from schedule-based to event-driven, and discover how to schedule, monitor, and manage your pipelines effortlessly.
Integration Runtimes: Gain insights into Azure Data Factory's Integration Runtimes and learn how they provide the necessary compute resources for executing data integration tasks. From configuring environments to optimizing performance, explore how Integration Runtimes enable seamless data processing across diverse infrastructures.
Monitoring and Management Tools: Learn how to effectively monitor and manage your data integration solutions with Azure Data Factory's built-in tools. From tracking pipeline performance to troubleshooting issues and optimizing resources, discover how these tools empower you to ensure the reliability and efficiency of your data workflows.
Want more similar videos- hit like, comment, share and subscribe
❤️Do Like, Share and Comment ❤️
❤️ Like Aim 5000 likes! ❤️
➖➖➖➖➖➖➖➖➖➖➖➖➖
➖➖➖➖➖➖➖➖➖➖➖➖➖
AWS DATA ENGINEER : AWS DATA ENGINEER
Azure data engineer playlist : Azure Data Engineer
SQL PLAYLIST : SQL playlist
PYSPARK PLAYLIST -
Pyspark Tutorial
➖➖➖➖➖➖➖➖➖➖➖➖➖
📣Want to connect with me? Check out these links:📣
Join telegram to discuss https://t.me/+Cb98j1_fnZs3OTA1
➖➖➖➖➖➖➖➖➖➖➖➖➖
what we have covered in this video:
➖➖➖➖➖➖➖➖➖➖➖➖➖
Hope you liked this video and learned something new :)
See you in next video, until then Bye-Bye!
➖➖➖➖➖➖➖➖➖➖➖➖➖
3 ماه پیش
در تاریخ 1403/01/07 منتشر شده
است.
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