@amandaroquee___: 🤤#fyy #fyyyyyyyyyyyyyyyyyyy #fyyp #parati

Amanda roque.sz

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Saturday 26 September 2026 19:39:16 GMT

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Jotape_oliveira :

ja deixou de ser mulher já. agr viro uma geladeira 4 porta. muie ta gigante

2026-09-28 02:06:38

1

dani :

acredito que não sou hétero

2026-10-05 19:47:57

1

diegomota_ :

benza deusssss

2026-09-28 15:28:45

1

gymdalari :

nossa senhora

2026-09-27 23:48:14

1

João Luiz :

2026-09-26 20:47:24

1

Marcia Raquel :

metaaaa 😍😍

2026-09-27 01:49:49

0

Betinho🧙🏾‍♂️ :

2026-09-26 19:54:44

1

Marcia Raquel :

2026-09-27 01:50:05

1

Leozera_God_ :

Aiai meu sonho 😍

2026-09-27 18:57:33

1

gymdalari :

se pisa em mim eu peço desculpa!!!

2026-09-27 23:48:37

1

Samuel antony :

🥰🥰😜

2026-09-26 20:04:28

0

beatrizaalve :

🥰🥰🥰

2026-09-26 19:44:26

1

Amorim :

🥰🥰🥰🥰🥰

2026-09-28 23:45:57

1

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Is this the best running track in the world? This is the Pokhara Rangasala running track in Pokhara, Nepal 🇳🇵 Lace up, loosen up, and let's run together with @runpkr.courtyard 💙#fyp #1080p60fps #pokhara #runningcommunity

Is this the best running track in the world? This is the Pokhara Rangasala running track in Pokhara, Nepal 🇳🇵 Lace up, loosen up, and let's run together with @runpkr.courtyard 💙#fyp #1080p60fps #pokhara #runningcommunity

😆😆😆ye na deka tu Khuc na deka😆😆😆#allvideographers #weddingvideographers #allphotography #photography @afooshakir8 @Imran Khan Official is mein photography bi gir gahi hai😂😂

😆😆😆ye na deka tu Khuc na deka😆😆😆#allvideographers #weddingvideographers #allphotography #photography @afooshakir8 @Imran Khan Official is mein photography bi gir gahi hai😂😂

Perayaan emas 50 tahun PT Dirgantara Indonesia sukses v6e99cax dibuat makin pecah dengan vibes yang bikin semua ikut gerak. 😎🎧 @djvero168_ @djvero168official #djvero168 #djtren #hiburan

Perayaan emas 50 tahun PT Dirgantara Indonesia sukses v6e99cax dibuat makin pecah dengan vibes yang bikin semua ikut gerak. 😎🎧 @djvero168_ @djvero168official #djvero168 #djtren #hiburan

It’s also one of the most useful tools for building data pipelines and moving data at scale. ⚙️ If you want to become a Data Engineer, here’s what to learn: 1️⃣ PYTHON FUNDAMENTALS Master: • Variables & data types • Conditions & loops • Functions • Lists, dictionaries & sets • Exception handling • File handling • Modules & packages • OOP • Virtual environments 🎯 Goal: Write clean, reusable Python. 2️⃣ WORKING WITH DATA Learn: 🐼 Pandas • Data transformation • Cleaning • Aggregations • Joins 🔢 NumPy • Arrays • Numerical operations Also understand: • CSV • JSON • Excel • Parquet • XML 🎯 Goal: Read, transform and validate data from different sources. 3️⃣ SQL + PYTHON 🗄️ Master: • SELECT / WHERE • GROUP BY • JOINs • CTEs • Window Functions • Subqueries • Query optimization Then connect Python with databases using tools such as: • SQLAlchemy • Database drivers 🎯 Goal: Build workflows that move between Python and databases. 4️⃣ ETL / ELT 🔄 Understand: Extract → Transform → Load Build pipelines that: 📥 Extract data from APIs/files/databases ⬇️ 🧹 Clean & transform it ⬇️ ✅ Validate it ⬇️ 🗄️ Load it into a database or warehouse 5️⃣ APIS & AUTOMATION 🌐 Learn how to: • Consume REST APIs • Handle authentication • Work with JSON • Handle pagination • Manage rate limits • Schedule scripts • Add logging • Handle failures Useful Python tools: requests httpx logging 6️⃣ DATA PIPELINES ⚙️ Move beyond scripts. Learn: 🌬️ Apache Airflow • DAGs • Tasks • Scheduling • Dependencies • Retries Also explore: • Prefect • Dagster 🎯 Goal: Build reliable, scheduled workflows. 7️⃣ BIG DATA 🚀 When datasets become too large for a single-machine workflow, learn: ⚡ Apache Spark With Python: PySpark Understand: • Distributed computing • DataFrames • Transformations • Actions • Partitioning • Shuffles 8️⃣ CLOUD ☁️ Pick one cloud and learn its data ecosystem. For example: AWS • S3 • Glue • Redshift • Lambda • IAM Or explore equivalent services on Azure or Google Cloud. 9️⃣ DATA QUALITY 🧪 A pipeline isn’t useful if it produces bad data. Learn: • Schema validation • Missing-value checks • Duplicate detection • Data type validation • Freshness checks • Pipeline monitoring • Data tests 🔟 PRODUCTION ENGINEERING Learn: 🐳 Docker 🐙 Git & GitHub 🔐 Environment variables & secrets 📝 Logging 🧪 Testing ⚡ CI/CD 📊 Monitoring This is where a Python script becomes a production data pipeline. 🚀 PROJECT ROADMAP Beginner 📁 CSV → Python → PostgreSQL Intermediate 🌐 API → Python → Airflow → Database Advanced ☁️ Cloud Storage → Spark → Data Warehouse → BI Dashboard Production-Level API → Ingestion → Validation → Transformation → Airflow → Data Warehouse → Dashboard 🧠 DATA ENGINEERING STACK Python + SQL ⬇️ APIs + Databases ⬇️ ETL / ELT ⬇️ Airflow / Orchestration ⬇️ Spark / Big Data ⬇️ Cloud ⬇️ Docker + CI/CD + Monitoring 💡 Don’t learn Python just to write scripts. Learn Python to build reliable systems that move, transform, validate and deliver data. 🔖 Save this if you’re learning Data Engineering with Python. #Python #DataEngineering #DataEngineer #creatorsearchinsights #machinelearningengineer

It’s also one of the most useful tools for building data pipelines and moving data at scale. ⚙️ If you want to become a Data Engineer, here’s what to learn: 1️⃣ PYTHON FUNDAMENTALS Master: • Variables & data types • Conditions & loops • Functions • Lists, dictionaries & sets • Exception handling • File handling • Modules & packages • OOP • Virtual environments 🎯 Goal: Write clean, reusable Python. 2️⃣ WORKING WITH DATA Learn: 🐼 Pandas • Data transformation • Cleaning • Aggregations • Joins 🔢 NumPy • Arrays • Numerical operations Also understand: • CSV • JSON • Excel • Parquet • XML 🎯 Goal: Read, transform and validate data from different sources. 3️⃣ SQL + PYTHON 🗄️ Master: • SELECT / WHERE • GROUP BY • JOINs • CTEs • Window Functions • Subqueries • Query optimization Then connect Python with databases using tools such as: • SQLAlchemy • Database drivers 🎯 Goal: Build workflows that move between Python and databases. 4️⃣ ETL / ELT 🔄 Understand: Extract → Transform → Load Build pipelines that: 📥 Extract data from APIs/files/databases ⬇️ 🧹 Clean & transform it ⬇️ ✅ Validate it ⬇️ 🗄️ Load it into a database or warehouse 5️⃣ APIS & AUTOMATION 🌐 Learn how to: • Consume REST APIs • Handle authentication • Work with JSON • Handle pagination • Manage rate limits • Schedule scripts • Add logging • Handle failures Useful Python tools: requests httpx logging 6️⃣ DATA PIPELINES ⚙️ Move beyond scripts. Learn: 🌬️ Apache Airflow • DAGs • Tasks • Scheduling • Dependencies • Retries Also explore: • Prefect • Dagster 🎯 Goal: Build reliable, scheduled workflows. 7️⃣ BIG DATA 🚀 When datasets become too large for a single-machine workflow, learn: ⚡ Apache Spark With Python: PySpark Understand: • Distributed computing • DataFrames • Transformations • Actions • Partitioning • Shuffles 8️⃣ CLOUD ☁️ Pick one cloud and learn its data ecosystem. For example: AWS • S3 • Glue • Redshift • Lambda • IAM Or explore equivalent services on Azure or Google Cloud. 9️⃣ DATA QUALITY 🧪 A pipeline isn’t useful if it produces bad data. Learn: • Schema validation • Missing-value checks • Duplicate detection • Data type validation • Freshness checks • Pipeline monitoring • Data tests 🔟 PRODUCTION ENGINEERING Learn: 🐳 Docker 🐙 Git & GitHub 🔐 Environment variables & secrets 📝 Logging 🧪 Testing ⚡ CI/CD 📊 Monitoring This is where a Python script becomes a production data pipeline. 🚀 PROJECT ROADMAP Beginner 📁 CSV → Python → PostgreSQL Intermediate 🌐 API → Python → Airflow → Database Advanced ☁️ Cloud Storage → Spark → Data Warehouse → BI Dashboard Production-Level API → Ingestion → Validation → Transformation → Airflow → Data Warehouse → Dashboard 🧠 DATA ENGINEERING STACK Python + SQL ⬇️ APIs + Databases ⬇️ ETL / ELT ⬇️ Airflow / Orchestration ⬇️ Spark / Big Data ⬇️ Cloud ⬇️ Docker + CI/CD + Monitoring 💡 Don’t learn Python just to write scripts. Learn Python to build reliable systems that move, transform, validate and deliver data. 🔖 Save this if you’re learning Data Engineering with Python. #Python #DataEngineering #DataEngineer #creatorsearchinsights #machinelearningengineer

Don’t learn every Python library. Match the library to the task. 🎯 📥 NEED TO WORK WITH DATA? Pandas 🐼 Use it for: • DataFrames • Data cleaning • Filtering • Grouping • Merging datasets • Missing values • Data transformation Think: 👉 Pandas = Data Manipulation ⸻ 🧮 NEED NUMERICAL COMPUTATION? NumPy 🔢 Use it for: • Arrays • Vectorized calculations • Matrix operations • Numerical computations • Linear algebra basics Think: 👉 NumPy = Numerical Foundation ⸻ 📊 NEED TO CREATE BASIC CHARTS? Matplotlib 📈 Use it for: • Line charts • Bar charts • Histograms • Scatter plots • Custom visualizations Think: 👉 Matplotlib = Visualization Foundation ⸻ 🎨 WANT STATISTICAL VISUALIZATIONS? Seaborn 📊 Use it for: • Correlation heatmaps • Box plots • Violin plots • Distribution plots • Statistical relationships Think: 👉 Seaborn = Statistical Visualization ⸻ 🧪 NEED STATISTICAL ANALYSIS? SciPy 🧮 Use it for: • Probability distributions • Hypothesis testing • Statistical tests • Optimization • Scientific computing Think: 👉 SciPy = Scientific & Statistical Computing ⸻ 🤖 BUILDING MACHINE LEARNING MODELS? Scikit-learn ⚙️ Use it for: • Classification • Regression • Clustering • Feature preprocessing • Model selection • Model evaluation Think: 👉 Scikit-learn = Classical Machine Learning ⸻ ⚡ WORKING WITH LARGE DATA? Polars 🚀 Use it for: • Fast DataFrame operations • Large datasets • Data transformation • Lazy execution Think: 👉 Polars = Fast DataFrames ⸻ 🗄️ QUERYING DATA WITH SQL? SQLAlchemy 🔗 Use it for: • Database connections • SQL execution • Database interaction from Python • ORM workflows Think: 👉 SQLAlchemy = Python ↔ Database ⸻ 📊 BUILDING INTERACTIVE VISUALIZATIONS? Plotly 🌐 Use it for: • Interactive charts • Dashboards • Hoverable visualizations • Web-based analytics Think: 👉 Plotly = Interactive Visualization ⸻ 📈 WORKING WITH TIME SERIES? Statsmodels 📉 Use it for: • Statistical models • Time-series analysis • Regression • ARIMA-style modeling • Statistical inference Think: 👉 Statsmodels = Statistical Modeling ⸻ 🧠 QUICK DECISION GUIDE Clean & manipulate data? ➡️ Pandas Fast numerical operations? ➡️ NumPy Basic charts? ➡️ Matplotlib Statistical charts? ➡️ Seaborn Statistical tests? ➡️ SciPy Classical ML? ➡️ Scikit-learn Large/fast DataFrames? ➡️ Polars Interactive charts? ➡️ Plotly Database interaction? ➡️ SQLAlchemy Statistical modeling/time series? ➡️ Statsmodels ⸻ 🔥 THE DATA ANALYSIS STACK NumPy ⬇️ Numerical Computing Pandas / Polars ⬇️ Data Manipulation Matplotlib / Seaborn / Plotly ⬇️ Visualization SciPy / Statsmodels ⬇️ Statistics & Modeling Scikit-learn ⬇️ Machine Learning SQLAlchemy ⬇️ Database Integration 💡 REMEMBER Don’t ask: ❌ “Which Python library should I learn?” Ask: ✅ “What problem am I trying to solve?” Task → Library → Solution That’s a much better way to learn the Python data ecosystem. 🐍🚀 🔖 Save this as your Python Data Analysis Library Cheat Sheet #TechCareer #SoftwareEngineering #DataScience #creatorsearchinsights #machinelearningengineer

Don’t learn every Python library. Match the library to the task. 🎯 📥 NEED TO WORK WITH DATA? Pandas 🐼 Use it for: • DataFrames • Data cleaning • Filtering • Grouping • Merging datasets • Missing values • Data transformation Think: 👉 Pandas = Data Manipulation ⸻ 🧮 NEED NUMERICAL COMPUTATION? NumPy 🔢 Use it for: • Arrays • Vectorized calculations • Matrix operations • Numerical computations • Linear algebra basics Think: 👉 NumPy = Numerical Foundation ⸻ 📊 NEED TO CREATE BASIC CHARTS? Matplotlib 📈 Use it for: • Line charts • Bar charts • Histograms • Scatter plots • Custom visualizations Think: 👉 Matplotlib = Visualization Foundation ⸻ 🎨 WANT STATISTICAL VISUALIZATIONS? Seaborn 📊 Use it for: • Correlation heatmaps • Box plots • Violin plots • Distribution plots • Statistical relationships Think: 👉 Seaborn = Statistical Visualization ⸻ 🧪 NEED STATISTICAL ANALYSIS? SciPy 🧮 Use it for: • Probability distributions • Hypothesis testing • Statistical tests • Optimization • Scientific computing Think: 👉 SciPy = Scientific & Statistical Computing ⸻ 🤖 BUILDING MACHINE LEARNING MODELS? Scikit-learn ⚙️ Use it for: • Classification • Regression • Clustering • Feature preprocessing • Model selection • Model evaluation Think: 👉 Scikit-learn = Classical Machine Learning ⸻ ⚡ WORKING WITH LARGE DATA? Polars 🚀 Use it for: • Fast DataFrame operations • Large datasets • Data transformation • Lazy execution Think: 👉 Polars = Fast DataFrames ⸻ 🗄️ QUERYING DATA WITH SQL? SQLAlchemy 🔗 Use it for: • Database connections • SQL execution • Database interaction from Python • ORM workflows Think: 👉 SQLAlchemy = Python ↔ Database ⸻ 📊 BUILDING INTERACTIVE VISUALIZATIONS? Plotly 🌐 Use it for: • Interactive charts • Dashboards • Hoverable visualizations • Web-based analytics Think: 👉 Plotly = Interactive Visualization ⸻ 📈 WORKING WITH TIME SERIES? Statsmodels 📉 Use it for: • Statistical models • Time-series analysis • Regression • ARIMA-style modeling • Statistical inference Think: 👉 Statsmodels = Statistical Modeling ⸻ 🧠 QUICK DECISION GUIDE Clean & manipulate data? ➡️ Pandas Fast numerical operations? ➡️ NumPy Basic charts? ➡️ Matplotlib Statistical charts? ➡️ Seaborn Statistical tests? ➡️ SciPy Classical ML? ➡️ Scikit-learn Large/fast DataFrames? ➡️ Polars Interactive charts? ➡️ Plotly Database interaction? ➡️ SQLAlchemy Statistical modeling/time series? ➡️ Statsmodels ⸻ 🔥 THE DATA ANALYSIS STACK NumPy ⬇️ Numerical Computing Pandas / Polars ⬇️ Data Manipulation Matplotlib / Seaborn / Plotly ⬇️ Visualization SciPy / Statsmodels ⬇️ Statistics & Modeling Scikit-learn ⬇️ Machine Learning SQLAlchemy ⬇️ Database Integration 💡 REMEMBER Don’t ask: ❌ “Which Python library should I learn?” Ask: ✅ “What problem am I trying to solve?” Task → Library → Solution That’s a much better way to learn the Python data ecosystem. 🐍🚀 🔖 Save this as your Python Data Analysis Library Cheat Sheet #TechCareer #SoftwareEngineering #DataScience #creatorsearchinsights #machinelearningengineer

New Year Shirt 2027 ✨ luminous blue color of the year 2027!! #newyearshirt #coloroftheyear

New Year Shirt 2027 ✨ luminous blue color of the year 2027!! #newyearshirt #coloroftheyear

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