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@lexus5779: #CapCut #explorepage حبيبي يا حبيبي حبيبي يا حبيبي #lexus360black #اغاني #خليجي #خليجي25 #طرب #اغاني_عراقيه #عراقيه #fo #طربيات #شعر #قصايد #l #EidMubarak #اكسبلور #foryourpage #foryou

LEXUS
LEXUS
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Region: BH
Sunday 29 June 2025 06:05:35 GMT
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lexus5779
LEXUS :
وترحل .. صرختي .. وتذبل في وادي لا صدى يوصل .. ولا باقي انين زمان الصمت ياعمر الحزن والشكوه ياخطوه ماغدت تقوى .. على الخطوه على هم السنين .. وترحل صرختي وتذبل حبيبي ياحبيبي .. كتبت اسمك على صوتي .. كتبته في جدار الوقت على لون السما الهادي .. على الوادي على موتي وميلادي .. حبيبي لو ايادي الصمت .. 😍😍😍
2025-06-29 06:06:03
4
happy.valentinesd2
lala mouhammed :
صباح النور والسرور وسعادة 🥰🥰🥰🥰
2025-06-29 08:58:44
1
meowkitty973
محمد الفهد :
ياحليبي كتبت اسمك علي صوتي 🌹🥰👍😍🌹🥰🥰👍🌹🥰🥰🥰🥰
2025-06-29 07:02:50
1
malek_25_1
ملاك 🌹 :
🥰🥰🥰
2025-07-05 15:45:52
1
user34511235213255
نسمة هدوء :
🥰🥰🥰🥰🥰🥰
2025-06-29 09:25:06
1
toto1480
🌷Toto🌷 :
🥰🥰🥰🥰🥰
2025-06-29 08:49:11
1
reel.reel98
راحت امي وانتهيت :
🌹🌹🥰🥰
2025-06-29 07:24:36
1
meowkitty973
محمد الفهد :
🥰🥰🥰
2025-06-29 06:44:37
1
secret.heart.16
س͆ر͆ 🦋 :
😢😢😢
2025-06-29 06:37:25
1
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Trong một kiếp sống khác, con ước rằng mẹ sẽ có được những điều mẹ từng muốn, sẽ thực hiện được giấc mơ dang dở mà mẹ chưa thể, và được yêu thương theo cách mà mẹ xứng đáng, ngay cả khi kiếp sống đó con không còn được làm con của mẹ.#hanoi #me #maiamgiadinhviet #xuhuong #xuhuongtiktok
Trong một kiếp sống khác, con ước rằng mẹ sẽ có được những điều mẹ từng muốn, sẽ thực hiện được giấc mơ dang dở mà mẹ chưa thể, và được yêu thương theo cách mà mẹ xứng đáng, ngay cả khi kiếp sống đó con không còn được làm con của mẹ.#hanoi #me #maiamgiadinhviet #xuhuong #xuhuongtiktok
someone has to be first #s650 #ess #mustang #boost #wengerdperformance
someone has to be first #s650 #ess #mustang #boost #wengerdperformance
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Have you ever wondered how to manage a Data Pipeline efficiently? This detailed visual breaks down the architecture into five essential stages: Collect, Ingest, Store, Compute, and Use. Each stage ensures a smooth and efficient data lifecycle, from gathering data to transforming it into actionable insights. Collect: Data is gathered from a variety of internal and external sources, including: -- Mobile Applications and Web Apps: Data generated from user interactions. -- Microservices: Capturing microservice interactions and transactions. -- IoT Devices: Collecting sensor data through MQTT protocols. -- Batch Data: Historical data collected in batches. Ingest: In this stage, the collected data is ingested into the system through batch jobs or streaming methods: -- Event Queue: Manages and queues incoming data streams. -- Extracting Raw Event Stream: Moving data to a data lake or warehouse. -- Tools Used: MQTT for real-time streaming, Kafka for managing data streams, and Airbyte or Gobblin for data integration. Store: The ingested data is then stored in a structured manner for efficient access and processing: -- Data Lake: Storing raw data in its native format. -- Data Warehouse: Structured storage for easy querying and analysis. -- Technologies Used: MinIO for object storage, Iceberg, and Delta Lake for managing large datasets. Compute: This stage involves processing the stored data to generate meaningful insights: -- Batch Processing: Handling large volumes of data in batches using tools like Apache Spark. -- Stream Processing: Real-time data processing with Flink and Beam. -- ML Feature Engineering: Preparing data for machine learning models. -- Caching: Using technologies like Ignite to speed up data access. Use: Finally, the processed data is utilized in various applications: -- Dashboards: Visualizing data for business insights using tools like Metabase and Superset. -- Data Science Projects: Conducting complex analyses and building predictive models using Jupyter notebooks. -- Real-Time Analytics: Providing immediate insights for decision-making. -- ML Services: Deploying machine learning models to provide AI-driven solutions. Key supporting functions such as: -- Orchestration: Managed by tools like Airflow to automate and schedule tasks. -- Data Quality: Ensuring the accuracy and reliability of data throughout the pipeline. -- Cataloging: Maintaining an organized inventory of data assets. -- Governance: Enforcing policies and ensuring compliance with frameworks like Apache Atlas. This comprehensive guide illustrates how each component fits into the overall pipeline, showcasing the integration of various tools and technologies. Check out this detailed breakdown and see how these elements can enhance your data management strategies. How are you currently handling your data pipeline architecture? Let's discuss and share best practices! #data #ai #datapipeline #dataengineering #theravitshow
Have you ever wondered how to manage a Data Pipeline efficiently? This detailed visual breaks down the architecture into five essential stages: Collect, Ingest, Store, Compute, and Use. Each stage ensures a smooth and efficient data lifecycle, from gathering data to transforming it into actionable insights. Collect: Data is gathered from a variety of internal and external sources, including: -- Mobile Applications and Web Apps: Data generated from user interactions. -- Microservices: Capturing microservice interactions and transactions. -- IoT Devices: Collecting sensor data through MQTT protocols. -- Batch Data: Historical data collected in batches. Ingest: In this stage, the collected data is ingested into the system through batch jobs or streaming methods: -- Event Queue: Manages and queues incoming data streams. -- Extracting Raw Event Stream: Moving data to a data lake or warehouse. -- Tools Used: MQTT for real-time streaming, Kafka for managing data streams, and Airbyte or Gobblin for data integration. Store: The ingested data is then stored in a structured manner for efficient access and processing: -- Data Lake: Storing raw data in its native format. -- Data Warehouse: Structured storage for easy querying and analysis. -- Technologies Used: MinIO for object storage, Iceberg, and Delta Lake for managing large datasets. Compute: This stage involves processing the stored data to generate meaningful insights: -- Batch Processing: Handling large volumes of data in batches using tools like Apache Spark. -- Stream Processing: Real-time data processing with Flink and Beam. -- ML Feature Engineering: Preparing data for machine learning models. -- Caching: Using technologies like Ignite to speed up data access. Use: Finally, the processed data is utilized in various applications: -- Dashboards: Visualizing data for business insights using tools like Metabase and Superset. -- Data Science Projects: Conducting complex analyses and building predictive models using Jupyter notebooks. -- Real-Time Analytics: Providing immediate insights for decision-making. -- ML Services: Deploying machine learning models to provide AI-driven solutions. Key supporting functions such as: -- Orchestration: Managed by tools like Airflow to automate and schedule tasks. -- Data Quality: Ensuring the accuracy and reliability of data throughout the pipeline. -- Cataloging: Maintaining an organized inventory of data assets. -- Governance: Enforcing policies and ensuring compliance with frameworks like Apache Atlas. This comprehensive guide illustrates how each component fits into the overall pipeline, showcasing the integration of various tools and technologies. Check out this detailed breakdown and see how these elements can enhance your data management strategies. How are you currently handling your data pipeline architecture? Let's discuss and share best practices! #data #ai #datapipeline #dataengineering #theravitshow
#cs2 #csgo #update
#cs2 #csgo #update
How to bring a girl home? #streetinterview #publicinterview #dating #girls
How to bring a girl home? #streetinterview #publicinterview #dating #girls

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