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AKHIL GUPTA edited this page Jan 24, 2024 · 7 revisions

Welcome to the AlgorithmsQuestions wiki!

1.๐Ÿš€ ๐€๐ฐ๐ž๐ฌ๐จ๐ฆ๐ž ๐ƒ๐š๐ญ๐š ๐„๐ง๐ ๐ข๐ง๐ž๐ž๐ซ๐ข๐ง๐  ๐๐ซ๐จ๐ฃ๐ž๐œ๐ญ๐ฌ ๐ญ๐จ ๐ฌ๐ญ๐š๐ซ๐ญ ๐›๐ฎ๐ข๐ฅ๐๐ข๐ง๐  ๐ฒ๐จ๐ฎ๐ซ ๐ฉ๐จ๐ซ๐ญ๐Ÿ๐จ๐ฅ๐ข๐จ ๐Ÿ‘จโ€๐Ÿ’ป

Here are 9 Projects that you can add to your portfolio

๐Ÿ“Œ 1. End-To-End From Web Scraping to Tableau https://lnkd.in/ePMw63ge

What you will learn: Python, SQL, Snowflake, Tableau

๐Ÿ“Œ 2. Building Data Model and Writing ETL Job https://lnkd.in/eq-e3_3J

What you will learn: Python, SQL, Building Data Models, Basics of DBMS, Writing ETL Job, Querying Data Programmatically, PostgreSQL

๐Ÿ“Œ 3. Data Modeling and Analysis using Semantic Web Technologies https://lnkd.in/e4A86Ypq

What you will learn: Python, SQL, Cloud Computing Basics, AWS Services (Athena, Glue, Redshift, S3, IAM), Creating Data Pipeline

๐Ÿ“Œ 4. ETL Project in Azure Data Factory https://lnkd.in/eP8huQW3

What you will learn: Azure Data Factory

๐Ÿ“Œ 5. ETL Pipeline on AWS Cloud https://lnkd.in/ebgNtNRR

What you will learn: Python, SQL, Cloud Computing Basics, AWS Services (Athena, Glue, Redshift, S3, IAM), Creating Data Pipeline

๐Ÿ“Œ 6. Covid Data Analysis Project https://lnkd.in/eWZ3JfKD

What you will learn: Python, SQL, Building Data Model, AWS Services (Athena, Glue, Redshift, S3, IAM), Creating Data Pipeline, PostgreSQL

๐Ÿ“Œ 7. YouTube Data Analysis (End-To-End Data Engineering Project) https://lnkd.in/eYJTEKwF

What you will learn: Python, PySpark, SQL, How to understand the business problem, AWS Services (Athena, Glue, Redshift, S3, IAM, Lambda, Quicksight), Building Data Pipeline and Scheduling it.

๐Ÿ“Œ 8. Twitter Data Pipeline using Airflow https://lnkd.in/eNxHHZbY

What you will learn: Python, Basics of Airflow, Working with Twitter Data and Package (Tweepy), Pandas, Writing ETL job and storing data on S3

๐Ÿ“Œ 9. Sentiment analysis Twitter: Kafka and Spark Structured Streaming https://lnkd.in/esVAaqtU

What you will learn: Spark, Kafka

#python #sql #cloud #data

2. System design:

  1. AWS Scalability 101: https://lnkd.in/eU736g9Q

  2. Actor Model at PayPal: https://lnkd.in/eqcb7MpP

  3. Distributed Counter: https://lnkd.in/eGwaA62J

  4. User Online Status Indicator: https://lnkd.in/eW77FXAh

  5. Scalability 101 from LinkedIn: https://lnkd.in/eEKMn28h

  6. Microservices vs Monolith: https://lnkd.in/e3EBtg_v

  7. Rate Limiting at Stripe: https://lnkd.in/g2aM5jxG

  8. Gaming Leaderboard: https://lnkd.in/edfTDq5R

  9. Real-Time Live Comments: https://lnkd.in/e8g3dZ2i

  10. How Uber Computes ETA: https://lnkd.in/eVKV2ePC

  11. URL Shortener: https://lnkd.in/evFTZVQq

  12. Slack Architecture: https://lnkd.in/eATMDjrK

  13. Zoom Architecture: https://lnkd.in/edidhxZw

  14. Pastebin: https://lnkd.in/eZpfaVjc

  15. Virtual Waiting Room Architecture: https://lnkd.in/eD3kPiPv

  16. How Does Netflix Work?: https://lnkd.in/ehqyWMiW

  17. How Uber Finds Nearby Drivers: https://lnkd.in/eeqH9Hjh

  18. AWS Scalability 101: https://lnkd.in/eU736g9Q

  19. Slack Architecture: https://lnkd.in/eATMDjrK

  20. Distributed Counter: https://lnkd.in/eGwaA62J

  21. Real Time Presence Platform: https://lnkd.in/eW77FXAh

  22. Gaming Leaderboard: https://lnkd.in/edfTDq5R

  23. URL Shortener: https://lnkd.in/evFTZVQq

  24. Live Comments: https://lnkd.in/e8g3dZ2i

  25. How Cloudflare Scales PostgreSQL: https://lnkd.in/eEQP6Apw

  26. How Uber Computes ETA: https://lnkd.in/eVKV2ePC

========= when to use which Design Pattern:

๐Ÿญ. ๐—–๐—ฟ๐—ฒ๐—ฎ๐˜๐—ถ๐—ผ๐—ป๐—ฎ๐—น ๐—ฃ๐—ฎ๐˜๐˜๐—ฒ๐—ฟ๐—ป๐˜€ ๐Ÿ”น Singleton: Use when a single instance of a class is needed. Some examples are logging and database connections. ๐Ÿ”น Factory Method: Decouple object creation from usage. For example, you create different types of database connections based on configuration. ๐Ÿ”น Abstract Factory: Create families of related objects. For example, I build parsers for different file formats (e.g., JSON, XML, CSV). ๐Ÿ”น Builder: Constructing complex objects step by step. For example, if you need to create a complex domain object. ๐Ÿ”น Prototype: Creating duplicate objects and reusing cached objects to reduce database calls.

๐Ÿฎ. ๐—ฆ๐˜๐—ฟ๐˜‚๐—ฐ๐˜๐˜‚๐—ฟ๐—ฎ๐—น ๐—ฃ๐—ฎ๐˜๐˜๐—ฒ๐—ฟ๐—ป๐˜€ ๐Ÿ”น Adapter: Make incompatible interfaces compatible. For example, it integrates a new logging library into an existing system that expects a different interface. ๐Ÿ”น Composite: Represent part-whole hierarchies. For example, graphic objects in a drawing application can be grouped and treated uniformly ๐Ÿ”น Proxy: Control access to objects. For example, lazy loading of a high-resolution image in a web application. ๐Ÿ”น Decorator: Dynamically add/remove behavior. For example, we are implementing compression or encryption on top of file streams. ๐Ÿ”น Bridge: Decouple abstraction from implementation. For example, I am separating platform-specific code from core logic.

๐Ÿฏ. ๐—•๐—ฒ๐—ต๐—ฎ๐˜ƒ๐—ถ๐—ผ๐—ฟ๐—ฎ๐—น ๐—ฃ๐—ฎ๐˜๐˜๐—ฒ๐—ฟ๐—ป๐˜€ ๐Ÿ”น Strategy: Define a family of algorithms. For example, they allow users to choose different sorting or compression algorithms. ๐Ÿ”น Observer: Maintain a consistent state by being notified of changes and, for example, notifying subscribers of events in a messaging system. ๐Ÿ”น Command: Encapsulate a request as an object. For example, I implement undo/redo functionality in text or image editor. ๐Ÿ”น State: Encapsulate state-specific behavior. For example, we are handling different states of a user interface element (e.g., enabled, disabled, selected). ๐Ÿ”น Template Method: Define the skeleton of an algorithm in operation, deferring some steps to subclasses and implementing a base class for unit testing with customizable setup and teardown steps.

eDiscovery Products: https://github.com/ICIJ/extract?tab=readme-ov-file (Using Apache Tika)

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