TechG KB14

Passionate about technology, growth and development. Runner, Dancer, Teacher, Volunteer, Global Servant Leader. My thoughts are my own. Enjoy!

[Vikas Rajput @vikasrajputin]

If you're into Backend/DevOps Engineering, then Linux knowledge is essential

Here's 5 Games to help you quickly learn Linux:

[Python Coding @clcoding]

While Vs Do While

[Nelson Djalo | Amigoscode @AmigosCode]

Here are some common microservices patterns:

▶️ API Gateway Pattern: This pattern involves having a single entry point for all client requests, which then routes those requests to the appropriate microservice.

▶️ Circuit Breaker Pattern: This pattern is used to handle failures in a microservice architecture. When a microservice fails or becomes unresponsive, the circuit breaker trips and redirects requests to a fallback service.

▶️ Service Registry Pattern: This pattern is used to keep track of all the services in a microservices architecture. The registry acts as a central directory for service discovery.

▶️ Service Mesh Pattern: This pattern involves adding a layer of infrastructure between microservices to handle cross-cutting concerns such as service discovery, load balancing, and security.

▶️ Event-Driven Architecture Pattern: This pattern involves using events to communicate between microservices. Each microservice can publish events and subscribe to events published by other microservices.

▶️ Saga Pattern: This pattern is used to manage transactions that span multiple microservices. It involves breaking the transaction into smaller, individual steps and using compensating actions to undo completed steps if an error occurs.

▶️ Bulkhead Pattern: This pattern is used to isolate failures in a microservices architecture. Each microservice is placed in a separate container, so if one microservice fails, it does not affect other microservices.

▶️ Sidecar Pattern: This pattern involves deploying a separate container alongside each microservice to handle cross-cutting concerns such as logging, monitoring, and security.

▶️ CQRS Pattern: This pattern involves separating the read and write models in a microservices architecture. The read model is optimized for querying data, while the write model is optimized for updating data.

▶️ Strangler Pattern: This pattern involves gradually replacing a monolithic application with microservices by gradually adding new microservices and removing functionality from the monolith.

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[Teneika Askew | Analytics & Automation @teneikaask_you]

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[Security Trybe @SecurityTrybe]

Linux File Systems

[Security Trybe @SecurityTrybe]

10 Top Cybersecurity Project Ideas with GitHub Repositories

*Decrypting Caesar Cipher
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[Security Trybe @SecurityTrybe]

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[Security Trybe @SecurityTrybe]

Best YouTube to Learn…

[Security Trybe @SecurityTrybe]

10 Fastest Growing Cybersecurity Skills to Learn

[Security Trybe @SecurityTrybe]

Basic Linux Commands

[freeCodeCamp.org @freeCodeCamp]

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[Security Trybe @SecurityTrybe]

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[Data Science Dojo @DataScienceDojo]

💥 Here is an amazing Python cheat sheet for you all!

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[Law of Attraction @LAWOFATTRACTlON]

Good things are coming your way. You have to be open to receive them by believing they are around the corner.

[TRÄW🤟 @thatstraw]

Linux ip Command Cheat sheet with Examples

[SeffSaid @SeffSaid]

Stay strong

[L.A.M.Z @OlamideTowobola]

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[Kirk Borne @KirkDBorne]

The Landscape of #MachineLearning Algorithms

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[Nelson Djalo | Amigoscode @AmigosCode]

Here’s description of 9 types of API testing:

➡️ 1. Unit Testing: Involves testing individual components or functions of an API in isolation to ensure they function correctly and produce the expected output.

➡️ 2. Functional Testing: Focuses on testing the overall functionality of the API, verifying that it performs as intended and produces accurate results.

➡️ 3. Integration Testing: Tests interactions between different components or services within an API to ensure they work together seamlessly and communicate effectively.

➡️ 4. Load Testing: Evaluates the API's performance under heavy load by simulating a large number of concurrent requests to identify potential bottlenecks and optimize resource usage.

➡️ 5. Security Testing: Aims to identify vulnerabilities in the API by testing for potential security risks, such as unauthorized access, data leakage, or injection attacks.

➡️ 6. Stress Testing: Pushes the API beyond its limits by applying extreme conditions, such as high traffic or limited resources, to determine how well it can handle such scenarios.

➡️ 7. Negative Testing: Involves deliberately sending invalid or incorrect inputs to the API to verify that it responds appropriately with error messages or other expected behaviors.

➡️ 8. Boundary Testing: Focuses on testing the limits of the API's inputs, such as minimum and maximum values, to ensure it handles these boundaries correctly.

➡️ 9. Usability Testing: Evaluates the user-friendliness of the API by assessing how easily developers can understand its documentation, endpoints, and responses, ultimately enhancing the developer experience.

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