@aimnuaimnu: #ال شبل ال حوير #عشائر_الشبل_اهل_العج_الاصفر #ال شيمر

اعلام ال عباس 🫡⚔️
اعلام ال عباس 🫡⚔️
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Region: IQ
Wednesday 07 October 2026 17:20:07 GMT
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50h24
ابو سرحان🕴️🖤 :
الله حيو عيال الشايب ونعم ولله ❤️
2026-10-07 18:38:07
0
aons52
يونس دايخ ال عباس :
وثلث تنعام من عيال الشايب
2026-10-07 17:35:08
0
user1672287657739
عباس ال خيري :
ونعم والله
2026-10-07 19:34:02
0
yvv_2_la1
عماد الشبلاوي :
عزالله ماكو مبارز
2026-10-07 18:19:38
0
userjk0fy9tog3
علي نجم ال كريم :
ونعم من اعيال الشايب
2026-10-07 18:45:55
0
.hn72661
الغربه صعبه :
كففو من ال شبل العج الأصفر
2026-10-08 04:26:18
0
m120s_m120s
ابو ترمن الشبلاوي :
الف نعم من عمامي الشيمر
2026-10-07 17:55:16
0
2007_a2008a
ايمن الشبلاوي :
ونعم والله من أعيال شايب
2026-10-07 17:41:10
0
cr6g_1
مبدر محمد :
2026-10-07 21:41:58
0
musilm222
مسلم فارس :
والنعم من عيال الشايب
2026-10-07 18:06:13
0
dc.1w
حيدر جليل 🗽⚖️ :
ونعم والله
2026-10-07 19:12:32
0
husseinalshablawi35
حسين الشبلاوي :
والله ونعم
2026-10-07 22:37:35
0
jskhxk.ofhksbjk
محمد :
♥️♥️♥️
2026-10-07 19:16:23
0
sajjad_thabit
سجاد ثابت :
❤️❤️
2026-10-07 18:16:42
0
x_k_f_m_x
( 𝑀𝑈𝑄𝑇𝐴𝐷𝐴 𝐹𝐴𝑅𝐸𝑆) :
❤️❤️❤️
2026-10-07 18:15:41
0
user2666591484558
مقتدى👑💞 :
❤️❤️❤️
2026-10-07 22:05:12
0
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Data Scientists can build powerful models, but first they need reliable data. That’s where Data Engineering comes in. 🔄 THE DATA ENGINEERING PIPELINE 📥 1. DATA SOURCES Data comes from: 🌐 APIs 🗄️ Databases 📄 CSV / Excel files 📱 Applications ☁️ Cloud systems 📡 IoT devices ⬇️ 2️⃣ DATA INGESTION 📦 Collect and move data into your data platform. Common approaches: ⚡ Batch processing 🔴 Real-time streaming ⬇️ 3️⃣ DATA STORAGE 🗄️ Store raw and processed data. 🏞️ Data Lakes 🏢 Data Warehouses 🗄️ Databases ⬇️ 4️⃣ DATA PROCESSING ⚙️ Transform raw data into usable data. 🧹 Cleaning 🔄 Transformation 🔗 Joining 📊 Aggregation ✅ Validation Tools include: ⚡ Apache Spark 🐍 Python 🗄️ SQL ⬇️ 5️⃣ DATA PIPELINES 🔄 Automate the movement and transformation of data. Popular tools: 🔹 Airflow 🔹 Dagster 🔹 Prefect ⬇️ 6️⃣ DATA QUALITY ✅ Reliable data requires checks. 🔍 Accuracy 🧩 Completeness ⏱️ Freshness 📏 Consistency 🚨 Anomaly Detection ⬇️ 7️⃣ ANALYTICS & ML 📊🤖 Clean, reliable data becomes the foundation for: 📈 Dashboards 📊 Business Analytics 🤖 Machine Learning 🧠 AI Applications 🧠 SIMPLE WAY TO REMEMBER Raw Data ⬇️ 📥 Ingest ⬇️ 🗄️ Store ⬇️ ⚙️ Process ⬇️ 🔄 Pipeline ⬇️ ✅ Validate ⬇️ 📊 Analyze ⬇️ 💡 Insights 🛠️ DATA ENGINEERING STACK 🐍 Python → Data processing 🗄️ SQL → Querying data ⚡ Spark → Large-scale processing 🔄 Airflow → Workflow orchestration ☁️ Cloud → Scalable infrastructure 🏢 Data Warehouse → Analytics 🏞️ Data Lake → Large-scale raw data 💡 Data Engineering is the bridge between raw data and usable data. Without reliable pipelines, even the smartest ML model can end up eating garbage for breakfast. 🥣🤖 Good data → Good analysis → Better decisions. 📌 Save this Data Engineering workflow. #DataEngineering #DataScience #BigData                  #creatorsearchinsights #datascience
Data Scientists can build powerful models, but first they need reliable data. That’s where Data Engineering comes in. 🔄 THE DATA ENGINEERING PIPELINE 📥 1. DATA SOURCES Data comes from: 🌐 APIs 🗄️ Databases 📄 CSV / Excel files 📱 Applications ☁️ Cloud systems 📡 IoT devices ⬇️ 2️⃣ DATA INGESTION 📦 Collect and move data into your data platform. Common approaches: ⚡ Batch processing 🔴 Real-time streaming ⬇️ 3️⃣ DATA STORAGE 🗄️ Store raw and processed data. 🏞️ Data Lakes 🏢 Data Warehouses 🗄️ Databases ⬇️ 4️⃣ DATA PROCESSING ⚙️ Transform raw data into usable data. 🧹 Cleaning 🔄 Transformation 🔗 Joining 📊 Aggregation ✅ Validation Tools include: ⚡ Apache Spark 🐍 Python 🗄️ SQL ⬇️ 5️⃣ DATA PIPELINES 🔄 Automate the movement and transformation of data. Popular tools: 🔹 Airflow 🔹 Dagster 🔹 Prefect ⬇️ 6️⃣ DATA QUALITY ✅ Reliable data requires checks. 🔍 Accuracy 🧩 Completeness ⏱️ Freshness 📏 Consistency 🚨 Anomaly Detection ⬇️ 7️⃣ ANALYTICS & ML 📊🤖 Clean, reliable data becomes the foundation for: 📈 Dashboards 📊 Business Analytics 🤖 Machine Learning 🧠 AI Applications 🧠 SIMPLE WAY TO REMEMBER Raw Data ⬇️ 📥 Ingest ⬇️ 🗄️ Store ⬇️ ⚙️ Process ⬇️ 🔄 Pipeline ⬇️ ✅ Validate ⬇️ 📊 Analyze ⬇️ 💡 Insights 🛠️ DATA ENGINEERING STACK 🐍 Python → Data processing 🗄️ SQL → Querying data ⚡ Spark → Large-scale processing 🔄 Airflow → Workflow orchestration ☁️ Cloud → Scalable infrastructure 🏢 Data Warehouse → Analytics 🏞️ Data Lake → Large-scale raw data 💡 Data Engineering is the bridge between raw data and usable data. Without reliable pipelines, even the smartest ML model can end up eating garbage for breakfast. 🥣🤖 Good data → Good analysis → Better decisions. 📌 Save this Data Engineering workflow. #DataEngineering #DataScience #BigData #creatorsearchinsights #datascience

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