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How do Apple Pay and Google Pay handle sensitive card info? The diagram below shows the differences. Both approaches are very secure, but the implementations are different. To understand the difference, we break down the process into two flows. 1. Registering your credit card flow 2. Basic payment flow 1️. The registration flow is represented by steps 1~3 for both cases. The difference is: 𝐀𝐩𝐩𝐥𝐞 𝐏𝐚𝐲: Apple doesn’t store any card info. It passes the card info to the bank. Bank returns a token called DAN (device account number) to the iPhone. iPhone then stores DAN into a special hardware chip. 𝐆𝐨𝐨𝐠𝐥𝐞 𝐏𝐚𝐲: When you register the credit card with Google Pay, the card info is stored in the Google server. Google returns a payment token to the phone. 2️. When you click the “Pay” button on your phone, the basic payment flow starts. Here are the differences: 𝐀𝐩𝐩𝐥𝐞 𝐏𝐚𝐲: For iPhone, the e-commerce server passes the DAN to the bank. 𝐆𝐨𝐨𝐠𝐥𝐞 𝐏𝐚𝐲: In the Google Pay case, the e-commerce server passes the payment token to the Google server. Google server looks up the credit card info and passes it to the bank. In the diagram, the red arrow means the credit card info is available on the public network, although it is encrypted. Over to you: Apple needs to discuss the DAN details with banks. It takes time and effort, but the benefit is that the credit card info is on the public network only once. . . . #applepay #googlepay #security #system #programmer #coding #atozdebug

How does Redis persist data? Redis is an in-memory database. If the server goes down, the data will be lost. The diagram below shows two ways to persist Redis data on disk: 1. AOF (Append-Only File) 2. RDB (Redis Database) Note that data persistence is not performed on the critical path and doesn't block the write process in Redis. 🔹 AOF Unlike a write-ahead log, the Redis AOF log is a write-after log. Redis executes commands to modify the data in memory first and then writes it to the log file. AOF log records the commands instead of the data. The event-based design simplifies data recovery. Additionally, AOF records commands after the command has been executed in memory, so it does not block the current write operation. 🔹 RDB The restriction of AOF is that it persists commands instead of data. When we use the AOF log for recovery, the whole log must be scanned. When the size of the log is large, Redis takes a long time to recover. So Redis provides another way to persist data - RDB. RDB records snapshots of data at specific points in time. When the server needs to be recovered, the data snapshot can be directly loaded into memory for fast recovery. Step 1: The main thread forks the‘ bgsave’ sub-process, which shares all the in-memory data of the main thread. ‘bgsave’ reads the data from the main thread and writes it to the RDB file. Steps 2 and 3: If the main thread modifies data, a copy of the data is created. Steps 4 and 5: The main thread then operates on the data copy. Meanwhile ‘bgsave’ sub-process continues to write data to the RDB file. 🔹 Mixed Usually in production systems, we can choose a mixed approach, where we use RDB to record data snapshots from time to time and use AOF to record the commands since the last snapshot. . . . #RedisDataPersistence #AOFvsRDB #RedisInMemoryDatabase #DataRecovery #AOFLog #RDBSnapshot #RedisPerformance #MixedPersistenceApproach #atozdebug #programmer #developer #software

Top 8 Programming Paradigms - 🔹 Imperative Programming: Sequences steps altering program state. Used in C, C++, Java, Python. 🔹 Declarative Programming: Logic expressed without explicit control flow. Common in Functional Programming. 🔹 Object-Oriented Programming (OOP): Utilizes objects encapsulating data and behavior. Seen in Java, C++, Python. 🔹 Aspect-Oriented Programming (AOP): Modularizes system-wide concerns. Utilizes AspectJ for Java. 🔹 Functional Programming (FP): Focuses on mathematical functions, immutable data. Found in Haskell, Lisp, Erlang, JavaScript, Python, Scala. 🔹 Reactive Programming: Manages asynchronous data streams. Beneficial for event-driven, data streaming apps. 🔹 Generic Programming: Creates reusable, type-independent code. Widely used in libraries, frameworks. 🔹 Concurrent Programming: Executes multiple tasks concurrently. Used in multi-threaded servers, parallel processing, high-performance computing. . . . . . #ProgrammingParadigms #ImperativeProgramming #DeclarativeProgramming #ObjectOrientedProgramming #OOP #AspectOrientedProgramming #AOP #FunctionalProgramming #FP #ReactiveProgramming #GenericProgramming #ConcurrentProgramming #SoftwareDevelopment #Java #C++ #Python #Haskell #Lisp #Erlang #JavaScript #Scala #AspectJ #Concurrency #ParallelProcessing #HighPerformanceComputing #DevelopmentParadigms #atozdebug

Data Pipelines Overview. Data pipelines are a fundamental component of managing and processing data efficiently within modern systems. These pipelines typically encompass 5 predominant phases: Collect, Ingest, Store, Compute, and Consume. 1. Collect: Data is acquired from data stores, data streams, and applications, sourced remotely from devices, applications, or business systems. 2. Ingest: During the ingestion process, data is loaded into systems and organized within event queues. 3. Store: Post ingestion, organized data is stored in data warehouses, data lakes, and data lakehouses, along with various systems like databases, ensuring post-ingestion storage. 4. Compute: Data undergoes aggregation, cleansing, and manipulation to conform to company standards, including tasks such as format conversion, data compression, and partitioning. This phase employs both batch and stream processing techniques. 5. Consume: Processed data is made available for consumption through analytics and visualization tools, operational data stores, decision engines, user-facing applications, dashboards, data science, machine learning services, business intelligence, and self-service analytics. The efficiency and effectiveness of each phase contribute to the overall success of data-driven operations within an organization. . . . . #DataPipelines #DataManagement #DataProcessing #DataAnalytics #DataScience #DataIntegration #DataWarehousing #DataVisualization #atozdebug #technology #developer

K-Means Clustering Algorithm. . . . . . . #KMeansClustering #DataClustering #MachineLearning #DataScience #UnsupervisedLearning #ClusterAnalysis #PatternRecognition #DataMining #ClusteringAlgorithm #Analytics #AI #DataAnalysis #DataInsights

Let's dive into unsupervised learning using hierarchical clustering! 📊🔍 This method helps organize data like a family tree, showing a helpful structure and uncovering natural groups. It's great when you don't know the number of clusters beforehand! Discover hidden patterns in complicated data with this #MachineLearning technique. 🧠📈 #DataScience #Clustering #Algorithms #AI #Analytics #BigData #Tech #Innovation #KnowledgeIsPower #atozdebug

Diving into the future of intelligent systems with Reinforcement Learning! 🤖⚽📚 Let's embrace the journey of machines that learn, adapt, and conquer challenges—one algorithm at a time. #Innovation #ArtificialIntelligence #MachineLearning #AITechnology #ReinforcementLearning #FutureOfWork #TechTrends #ai #technology #atozdebug

Embrace the fusion of numbers and algorithms! 📚🤖 Begin your journey to mastering Mathematics for Machine Learning with our comprehensive guide. Dive deep into the core concepts and enhance your analytical skills, paving the way for innovative solutions and advancements. 🧠💡 #MachineLearning #Mathematics #DataScience #AI #LearningIsPower #Innovation #TechEducation #ContinuousLearning #atozdebug

Embarking on an AI journey? 🚀 Let’s ensure you have the essentials in your toolkit! Mathematics, programming, and a pinch of curiosity can set the stage for a future of innovation.👓💡 Dive into the world of artificial intelligence with us. #AILearning #CareerGrowth #Innovation #TechSkills #FutureReady #atozdebug #web #developer #ai #innovation

Diving deep into the tech that's changing the game 🌐! AI, Machine Learning, & Deep Learning are more than just buzzwords—they’re the pillars of modern innovation.🚀 Stay tuned as we explore their unique roles and how they interconnect to drive progress forward. 🤖💡 #ArtificialIntelligence #MachineLearning #DeepLearning #TechnologyTrends #Innovation #TechTalks