Bending Spoons’ Post

🚀 Day 36/100 - Data Engineering Journey Hey 👋 !! Today, while I was working on a data pipeline, I found myself grappling with an important question: What are some of the best practices and considerations when extracting data from a URL using HTTP connectors in Azure. As data engineers, fetching data from online sources is a common task, but ensuring efficiency, reliability, and data integrity in this process requires careful consideration of various factors. Here are some key best practices and things to keep in mind when working on such a task 1. Use of HTTP Connectors: When utilizing HTTP connectors in Azure or any other platform, it's essential to leverage established connectors or libraries provided by the platform. These connectors often come with built-in functionality for handling authentication, rate limiting, and error handling. 2. Authentication and Authorization: Ensure that you properly authenticate with the target URL if authentication is required. This may involve using API keys, OAuth tokens, or other authentication mechanisms provided by the data source. 3. Respect Rate Limits: Many APIs impose rate limits to prevent abuse and ensure fair usage. Implementing exponential backoff strategies can help gracefully handle rate limit errors. 4. Data Validation: Perform thorough validation of the fetched data to ensure its integrity and accuracy. Check for missing or invalid values, unexpected data formats, and other anomalies that may affect downstream processes. 5. Error Handling: Implement robust error handling mechanisms to gracefully handle network errors, server errors, and other exceptions that may occur during data extraction. Log error messages and relevant metadata for debugging and troubleshooting purposes. 6. Monitoring and Alerting: Set up monitoring and alerting systems to track the performance and health of your data extraction process. Monitor metrics such as latency, throughput, and error rates, and configure alerts to notify you of any anomalies or issues. 7. Compliance and Legal Considerations: Ensure that your data extraction activities comply with the terms of service and legal requirements of the data source. Respect copyright laws, intellectual property rights, and any other relevant regulations. By following these best practices and considerations, we can ensure that our data extraction process is efficient, reliable, and compliant with the guidelines and policies of the data sources we interact with. Stay tuned for more insights and advancements as we continue our 100 Days of Data Engineering journey! [ Picture source and an engaging read : https://lnkd.in/gCApqW8B ] #DataEngineering #Azure #DataExtraction #HTTPConnectors #BestPractices #Day36 #100daysofdataengineering

Day 21 of my system design journey: Understanding Data Consistency and Consistency Levels Today, I explored the critical concept of data consistency in distributed systems. When data is spread across multiple nodes (often for better availability and fault tolerance), data consistency ensures that all nodes reflect the same view of the data. However, achieving this across a distributed architecture comes with challenges like network latency, node failures, and concurrent updates. Here are the key consistency models I learned about: 1. Strong Consistency: Every read operation reflects the most recent write, ensuring no stale data. Although it prioritizes data integrity, it can impact system performance and availability due to the need for synchronization across nodes. 2. Eventual Consistency: This model ensures that all replicas eventually converge to the same state, even if there's a delay. It’s often used in systems where high availability and partition tolerance are more important than immediate consistency, like in NoSQL databases. 3. Causal Consistency: Operations that are causally related are seen in the same order by all nodes. This model maintains a balance between strong consistency and the system’s scalability. 4. Read-Your-Writes Consistency: A client sees the effects of its own writes in subsequent read operations, providing an intuitive user experience. Data consistency is a fascinating topic because it’s all about trade-offs—between speed, availability, and accuracy. Different systems use different models based on their requirements for performance, availability, and fault tolerance. #SystemDesign #DistributedSystems #DataConsistency #LearningJourney

As developers, we know that robust data infrastructure is the foundation of any successful application. But what principles should guide us as we build, scale, and optimize these systems in today’s rapidly evolving landscape? 🛠️ In our latest blog post, we break down the core principles of modern data infrastructure—covering everything from scalability to resilience. Whether you’re working on cutting-edge projects or refining existing systems, these insights are essential for staying ahead. Check it out and let me know your thoughts! 👇 https://hubs.la/Q02M13Qw0 #DataInfrastructure #SoftwareDevelopment #TechInnovation #Developers #Scalability

🚀 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐑𝐮𝐥𝐞𝐬 𝐨𝐟 𝐓𝐡𝐮𝐦𝐛 𝐟𝐨𝐫 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞𝐬 🚀 When it comes to scaling architectures, there are several key considerations to keep in mind to ensure optimal performance and cost-efficiency: 𝑪𝒐𝒔𝒕 𝒂𝒏𝒅 𝑺𝒄𝒂𝒍𝒂𝒃𝒊𝒍𝒊𝒕𝒚: Scaling an architecture often involves adding resources such as servers, bandwidth, or storage, which can quickly become expensive. It's crucial to balance the desired level of scalability with the available budget to avoid unnecessary expenses. 𝑬𝒗𝒆𝒓𝒚 𝑺𝒚𝒔𝒕𝒆𝒎 𝑪𝒐𝒏𝒄𝒆𝒂𝒍𝒔 𝒂 𝑩𝒐𝒕𝒕𝒍𝒆𝒏𝒆𝒄𝒌 𝑺𝒐𝒎𝒆𝒘𝒉𝒆𝒓𝒆: In any architecture, there's always a bottleneck waiting to be discovered. Identifying this bottleneck is the first step towards achieving effective scalability. It could be a particular component, database, or even a specific code segment that limits performance. 𝑺𝒍𝒐𝒘 𝑺𝒆𝒓𝒗𝒊𝒄𝒆𝒔 𝑷𝒐𝒔𝒆 𝑮𝒓𝒆𝒂𝒕𝒆𝒓 𝑪𝒉𝒂𝒍𝒍𝒆𝒏𝒈𝒆𝒔 𝑻𝒉𝒂𝒏 𝑭𝒂𝒊𝒍𝒆𝒅 𝑺𝒆𝒓𝒗𝒊𝒄𝒆𝒔: Slow services can be more detrimental to your system's performance than outright service failures. They can cause delays and timeouts for independent services, impacting the entire system. Users often prefer services that fail fast and gracefully, as it allows for quicker error recovery and ensures a better user experience. 𝑺𝒄𝒂𝒍𝒊𝒏𝒈 𝒕𝒉𝒆 𝑫𝒂𝒕𝒂 𝑻𝒊𝒆𝒓 𝑷𝒓𝒆𝒔𝒆𝒏𝒕𝒔 𝒕𝒉𝒆 𝑮𝒓𝒆𝒂𝒕𝒆𝒔𝒕 𝑪𝒉𝒂𝒍𝒍𝒆𝒏𝒈𝒆: Scaling the data tier, especially relational databases, can be one of the most challenging aspects of architecture. As data grows, managing databases and ensuring their performance becomes increasingly complex. Techniques like database sharding, replication, and caching can help address data tier scalability challenges. 𝑪𝒂𝒄𝒉𝒆 𝑬𝒙𝒕𝒆𝒏𝒔𝒊𝒗𝒆𝒍𝒚 𝒕𝒐 𝑶𝒑𝒕𝒊𝒎𝒊𝒛𝒆 𝑷𝒆𝒓𝒇𝒐𝒓𝒎𝒂𝒏𝒄𝒆: By storing frequently accessed data in memory, you can reduce the load on the data tier and improve response times. Caching can be applied at various levels, including application-level caches and content delivery networks (CDNs). 𝑬𝒇𝒇𝒆𝒄𝒕𝒊𝒗𝒆 𝑴𝒐𝒏𝒊𝒕𝒐𝒓𝒊𝒏𝒈 𝒊𝒔 𝑽𝒊𝒕𝒂𝒍 𝒇𝒐𝒓 𝑺𝒄𝒂𝒍𝒂𝒃𝒍𝒆 𝑺𝒚𝒔𝒕𝒆𝒎𝒔: Effective monitoring provides real-time insights into system performance, resource utilization, and potential issues. By employing monitoring tools and setting up alerts, you can proactively identify and address problems before they impact users. Implementing these rules of thumb can help you build scalable and efficient systems that meet the demands of a growing user base. #SolutionArchitecture #Scalability #TechInnovation #CostEfficiency #PerformanceOptimization

𝐈𝐭 𝐭𝐨𝐨𝐤 𝐬𝐨𝐦𝐞𝐨𝐧𝐞'𝐬 7 𝐲𝐞𝐚𝐫𝐬 𝐚𝐧𝐝 𝐬𝐞𝐯𝐞𝐫𝐚𝐥 𝐟𝐚𝐢𝐥𝐮𝐫𝐞𝐬 𝐭𝐨 𝐠𝐞𝐭 𝐭𝐡𝐞𝐬𝐞 30 𝐬𝐲𝐬𝐭𝐞𝐦 𝐝𝐞𝐬𝐢𝐠𝐧 𝐜𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝘠𝘰𝘶 𝘤𝘢𝘯 𝘨𝘦𝘵 𝘵𝘩𝘦𝘮 𝘪𝘯 7 𝘴𝘦𝘤𝘰𝘯𝘥𝘴 𝘣𝘺 𝘴𝘢𝘷𝘪𝘯𝘨 𝘵𝘩𝘪𝘴 𝘱𝘰𝘴𝘵. 1. Use autoscaling for traffic spikes 2. design for scalability from the start 3. plan for and implement fault tolerance 4. prioritize horizontal scaling for scalability 5. implement data partitioning and sharding 6. use data lakes for analytics and reporting 7. employ CDNs for global latency reduction 8. make operations idempotent for simplicity 9. use event-driven architecture for flexibility 10. employ blob/object storage for media files 11. embrace tradeoffs; perfection is unattainable 12. implement Data Replication and Redundancy 13. Implement rate Limiting for system protection 14. use a read-through cache for read-heavy apps 15. utilize write-through cache for write-heavy apps 16. opt for NoSQL Databases for unstructured data 17. use Heartbeat Mechanisms for failure detection 18. adopt WebSockets for real-time communication 19. employ Database Sharding for horizontal scaling 20. clearly define system use cases and constraints 21. consider microservices for flexibility and scalability 22. design for flexibility; expect requirements to evolve 23. utilize Database Indexing for efficient data retrieval 24. understand requirements thoroughly before designing 25. Utilize asynchronous processing for background tasks 26. consider denormalizing databases for read-heavy tasks 27. avoid over-engineering; add functionality only as needed 28. prefer SQL Databases for structured data and transactions 29. use Load Balancers for high availability and traffic distribution 30. consider message queues for asynchronous communication Credit: Sumit L. P.S.: Repost♻️ to help your network and someone who may need it

🟢DAY 6/30 🚀COA Series : Cache Block Replacement Techniques in Computer Architecture In the realm of computer organization and architecture, cache memory plays a critical role in enhancing system performance. One of the most significant challenges we face is determining how to efficiently manage cache blocks when the cache reaches its limit. This is where cache block replacement techniques come into play. Let’s explore some of the most prominent strategies, their advantages, and their trade-offs. 1. Least Recently Used (LRU) Overview: LRU keeps track of cache usage over time, evicting the block that hasn't been accessed for the longest period. 📌Advantages: - Generally provides better performance for temporal locality, as it prioritizes recently used data. Challenges: - Implementation can be complex, requiring additional data structures (like linked lists or counters) to track access patterns. 2. First-In, First-Out (FIFO) Overview: FIFO replaces the oldest cache block first, regardless of how often it has been accessed. 📌Advantages: - Simple to implement with a queue structure, making it easy to track the order of entries. Challenges: - Can lead to suboptimal performance since it doesn't consider usage patterns; frequently accessed data may be evicted prematurely. 3. Random Replacement Overview: This method randomly selects a cache block for replacement. 📌Advantages: - Very easy to implement and can perform surprisingly well in certain scenarios, especially in workloads with a uniform access pattern. Challenges: - The random nature can lead to poor performance if certain blocks are consistently accessed more than others. 4. Least Frequently Used (LFU) Overview: LFU prioritizes cache blocks based on how often they have been accessed, replacing the least frequently used block. 📌Advantages: - Effective for data that exhibits varying access frequencies, as it retains blocks that are more likely to be reused. Challenges: - Complexity in tracking access counts can lead to higher overhead, especially in dynamic workloads. 5. Adaptive Replacement Cache (ARC) Overview: ARC is a hybrid approach that adapts between LRU and LFU, dynamically adjusting to the workload characteristics. 📌Advantages: - Offers a balanced strategy by retaining both frequently and recently accessed data, making it highly effective for diverse access patterns. Challenges: - More complex to implement and manage, requiring careful tuning to optimize performance. Conclusion: The choice of cache block replacement technique significantly impacts the performance and efficiency of systems. Each method has its strengths and weaknesses, making it crucial to understand the specific requirements and access patterns. #ComputerArchitecture #CacheManagement #LRU #FIFO #PerformanceOptimization #TechCommunity #ComputerScience

Perfect checklist #systemdesign #infrastructure #architecture #software #security #devops #design #compliance #scalability #latency #eventdriven #microservices #api #iaac

Great concepts and most of them can be applied in Embedded Software as well

🚀 Design A Key-value Store: Part 2 The blog post elaborates on system components topics including handling failures, system architecture diagram, and read/write paths. It explains failure detection in a distributed system, handling temporary and permanent failures, and data centre outage. The architecture of the key-value store is also explained. Read the full blog post by Aryan Agarwal at https://lnkd.in/ezNV7yAF #SystemComponents #Failures #SystemArchitectureDiagram #KeyValueStore #ReadAndWritePaths #Codedash