DATA ENGINEERING

Understand the power of Data Lakehouse Architecture for 𝗙𝗥𝗘𝗘 here

🚨𝗢𝗹𝗱 𝘄𝗮𝘆
• Complicated ETL processes for data integration.
• Silos of data storage, separating structured and unstructured data.
• High data storage and management costs in traditional warehouses.
• Limited scalability and delayed access to real-time insights.

✅𝗡𝗲𝘄 𝗪𝗮𝘆
• Streamlined data ingestion and processing with integrated SQL capabilities.
• Unified storage layer accommodating both structured and unstructured data.
• Cost-effective storage by combining benefits of data lakes and warehouses.
• Real-time analytics and high-performance queries with SQL integration.

The shift?

Unified Analytics and Real-Time Insights > Siloed and Delayed Data Processing

Leveraging SQL to manage data in a data lakehouse architecture transforms how businesses handle data.

Data Engineer Interview Questions: https://t.me/datascienceresourcestp/61

PySpark Concepts: https://t.me/datascienceresourcestp/63

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14 Uses Of Data Science

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When to use each model 👇
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The Mercedes Vision AVTR

Traditional :
Startup Idea → Plan → Design → Coding → Marketing→ Audience → 😣

Modern :
Audience → Problem → Idea → Validation → Waitlist → SEO → One Feature MVP → Iterate → Marketing → Success.

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What are crypto cards?

Crypto cards are an ingenious instrument allowing you to pay for goods with crypto anywhere that accepts credit cards: stores, gyms, transportation, and the internet. They work the same way as a traditional credit card issued by banks, but they’re connected to your crypto wallet instead of your bank.

This way, you can hold your assets on an exchange—e.g., USDT on Binance—while having the ability to pay for goods and services. What’s more, there are no network fees for these transactions.

The downside is, however, that crypto cards are only available in certain countries. If you are located in a country that accepts crypto cards, you can get your hands on one of these popular cards: Coinbase Card, Crypto.com Card, or Binance Card.

Always be wary of scammers and never enter your card information on suspicious sites or platforms.

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📊 Market Overview:

BTC : $102969
ETH : $2478.2
BNB : $641.24
SOL : $167.45

📈 Market Cap :

Total : 3.39T
DeFi : 104.5B
24hr Vol : 95.12B

⚡️ Sentiment :

FGI : Greed (74)
Open Interest : 66.02B
24h Liquidation : $288.8M

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🏝 Kotlin 2.1.21 is out (https://github.com/JetBrains/kotlin/releases/tag/v2.1.21)

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👉 Fix for working with XCode 16.3
🛠 Bug fixes

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⏰ How long it took bitcoin and businesses to reach $1 trillion capitalization:

Bitcoin: 12 years
Facebook: 17 years
Tesla: 18 years
Google: 21 years
Amazon: 24 years
Apple: 42 years
Microsoft: 44 years

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Data Science Life Cycle (Step-by-Step)

The Data Science Life Cycle describes the full process of solving a problem using data.

Here's how it goes:

1. Problem Understanding

Understand the business or research problem.

Example: “Can we predict customer churn?”


2. Data Collection

Gather data from CSV files, databases, APIs, web scraping, etc.

Tools: SQL, Python (requests, BeautifulSoup)


3. Data Cleaning & Preparation

Handle missing values, remove duplicates, fix data types, combine datasets.

Tool: Pandas


4. Exploratory Data Analysis (EDA)

Use statistics and visuals to understand patterns in the data.

Tools: Pandas, Seaborn, Matplotlib


5. Feature Engineering

Create or modify features to improve model performance.

Examples: encoding categories, scaling numbers

6. Model Building

Choose the right algorithm and train it on the data.

Tools: scikit-learn, XGBoost


7. Model Evaluation

Use metrics like Accuracy, Precision, Recall, F1-score to evaluate the model.

8. Deployment

Make the model available to users via APIs, web apps, dashboards, etc.

Tools: Flask, Streamlit, FastAPI

9. Communication & Reporting

Create dashboards or reports to share results clearly.

Tools: Power BI, Tableau, PPTs

10. Monitoring & Maintenance

Keep track of model performance in real-world use. Retrain if needed.


Uses Of Data Science: https://t.me/datascienceresourcestp/147

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📊 Market Overview:

BTC : $103907
ETH : $2504.59
BNB : $645.81
SOL : $171.32

📈 Market Cap :

Total : 3.42T
DeFi : 105.85B
24hr Vol : 77.1B

⚡️ Sentiment :

FGI : Greed (74)
Open Interest : 67.21B
24h Liquidation : $153.3M

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COOLEST TECH IN 2025: A VISUAL GUIDE

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What a crazy week in AI

- OpenAI’s Codex
- Google Coding Agent
- Windsurf SWE-1 models
- Notion’s new AI for work
- Tencent Multimodal Video
- ChatGPT 4.1 & PDF Exports
- Meta Collaborative Reasoner
- ElevenLabs Infinite Soundboard

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Excel vs Power BI: Key Differences

Excel:
- Purpose: Ideal for spreadsheet tasks, basic calculations, and small-scale data analysis.
- Best For: Creating simple reports, working with small datasets, and producing basic charts.
- Data Handling: Best suited for small to medium-sized datasets; performance can decline with larger data.
- Visualizations: Offers basic charts and graphs but lacks interactivity.
- Sharing: Usually shared via email or cloud storage (e.g., OneDrive); not ideal for real-time collaboration.
- Automation: Limited automation capabilities, with manual refreshes or basic macros.

Power BI:
- Purpose: Designed for advanced data analysis and creating interactive, visually rich reports.
- Best For: Handling large datasets, integrating data from multiple sources, and building dynamic dashboards.
- Data Handling: Efficient with very large datasets, maintaining high performance.
- Visualizations: Provides highly interactive visualizations with drill-down features and deep insights.
- Sharing: Allows real-time collaboration through online sharing and automatic report updates.
- Automation: Supports automatic data refreshes and real-time reporting capabilities.

React ❤️ for more

Tableau vs Power BI: https://t.me/dataanalysisresourcestp/157

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Hope it helps :)

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