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https://k12.instructure.com/eportfolios/1072745/entries/3681690

An analysis of the Hiper bet platforms core technology The article covers its software architecture data processing for realtime odds and security protocols for user wagers Hiper Bet Tecnologia An Inside Look at its Betting Architecture To achieve sub50 millisecond transaction latency structure your wagering platform around a microservices architecture This approach isolates critical functions like user authentication wallet management and odds calculation By containerizing these services using Docker and orchestrating them with Kubernetes you ensure independent scalability and fault tolerance preventing a failure in one component from crashing the entire system For the core data stream implement an eventsourcing pattern with Apache Kafka This handles the immense volume of concurrent stakes and market updates without data loss The processing logic for these events should be built with highconcurrency languages such as Go or Rust These are superior to interpreted languages for the critical path of stake placement and settlement significantly reducing server load and operational costs Integrate realtime machine learning directly into your odds generation pipeline Instead of relying on static models use live data feeds to continuously retrain your predictive algorithms A setup using Python with libraries like TensorFlow and Keras connected to a rapidaccess database like Redis allows for dynamic odds adjustments that reflect ontheground events within seconds This creates a more accurate and responsive market for participants Hyper Bet Tecnologia A Deep Dive into Modern Betting Platforms Adopt a microservices architecture for building scalable wagering platforms This approach isolates components like user authentication odds calculation and payment processing into independent services These services communicate via lightweight APIs typically REST or gRPC This structure allows development teams to update test and deploy the risk management module without affecting the user account service a clear advantage over monolithic system constraints Realtime odds generation demands a robust data processing pipeline Utilize Apache Kafka for ingesting highvolume data streams from sports information providers Process these streams using frameworks like Apache Flink for lowlatency calculations The system must update odds across thousands of markets in under 50 milliseconds to maintain a competitive advantage and manage liability effectively Employ a polyglot persistence strategy for data storage For transactional integrity in staking and payouts use a PostgreSQL or CockroachDB cluster as their ACID compliance is nonnegotiable for financial operations For session management and caching live event data an inmemory datastore like Redis provides submillisecond readwrite access User behavior analytics are best handled by a columnar database such as ClickHouse which is optimized for largescale analytical queries Security and regulatory compliance are foundational Implement endtoend encryption using TLS 13 for all data in transit and AES256 for data at rest Adherence to KYC Know Your Customer and AML AntiMoney Laundering regulations is mandatory Integrate with thirdparty verification services like Onfido or Jumio for automated identity checks Regular automated security audits and a dedicated Security Operations Center SOC are standard operational requirements not optional extras Implementing RealTime Data Processing for Live Odds Calculation Select a streamprocessing framework like Apache Flink or Apache Kafka Streams as the core of your architecture for calculating dynamic market prices This choice provides stateful processing capabilities and lowlatency throughput required for inplay events Core Architectural Components Data Ingestion Use a distributed message queue like Apache Kafka or Apache Pulsar to ingest raw event streams from multiple sources Target ingestion latencies below 50 milliseconds Stream Processing The Flink or Kafka Streams application consumes the data applies transformations and maintains the state of each live event State Management Utilize an embedded highperformance keyvalue store such as RocksDB integrated with the processing framework to hold the current state of a match eg score time remaining possession Model Execution The processing job applies pretrained mathematical or machine learning models to the event data and the current state to generate new coefficients Output Distribution Push updated coefficients to a lowlatency distribution system like Redis PubSub or a dedicated WebSocket server for immediate delivery to frontend interfaces Data Ingestion and Structuring Establish separate Kafka topics for distinct data sources official sport data feeds user placement activity and competitor pricing information Enforce a strict data schema using Apache Avro or Protocol Buffers Protobuf httpscassinopixpro prevents data quality issues and supports schema evolution without breaking downstream consumers Partition topics by a logical key such as eventid to guarantee that all data for a single match is processed by the same task manager ensuring ordered processing and state locality Stateful Processing in Apache Flink Configure the Flink application for eventtime processing to handle outoforder data from feeds correctly This ensures calculations are based on when the event happened not when it was processed Windowing Apply sliding window functions to aggregate data over short time intervals For instance calculate the frequency of specific actions eg corner kicks in football over the last 90 seconds Feature Engineering Develop custom Flink operators in Java or Scala to extract features from the raw data stream These features become inputs for the pricing models Example features include ball possession percentage shots on target or playerspecific performance metrics Stateful Recalculation The applications state for a given event is updated with every new piece of information This state change triggers a recalculation of all associated market propositions For example a goal being scored instantly modifies the state which in turn triggers the execution of the pricing model to adjust all related odds Distribution and Latency Targets Calculated odds must be broadcasted immediately Avoid writing to a traditional relational database as the primary distribution path due to its higher latency Publish coefficient updates to specific Redis channels eg oddsupdateevent12345 Backend services subscribe to these channels to receive the new prices Use a dedicated WebSocket server to push these updates directly to active user browsers or mobile applications eliminating the need for polling Measure and optimize the endtoend or glasstoglass latency The target from a realworld event occurring to the updated price appearing on a users screen should remain consistently below 500 milliseconds Building a Personalized Betting Feed Using User Behavior Analytics Implement granular event tracking for every user interaction with market odds including timeonpage for specific matches stake amounts and the sequence of leagues browsed This raw data forms the foundation for user profiling Collect explicit data points such as favorite teams or followed leagues during onboarding to establish an initial preference baseline before behavioral data accumulates Construct a hybrid recommendation engine combining collaborative filtering with a contentbased approach Collaborative filtering identifies users with similar staking patterns suggesting markets based on the actions of likeminded individuals A contentbased model analyzes event attributesteam form player statistics league tierto suggest similar opportunities which is particularly effective for new users with limited history Structure the personalized feed into distinct dynamic modules A Top Picks for You module should surface 35 highconfidence predictions from the engine A Trending in Your Leagues module can show popular markets within the users preferred competitions derived from aggregated anonymized platform data Include a Quick Stake feature next to each recommendation prepopulating the slip with a default amount based on that users average stake size The recommendation model must rescore user preferences in near realtime A click on a specific team or player within the feed should immediately trigger a reranking of displayed opportunities For instance interacting with a Total Goals Over 25 market should elevate similar markets for other upcoming matches Track ignored suggestions as negative signals to refine future outputs and prevent showing stale or irrelevant content To prevent the filter bubble effect where a user only sees their established preferences allocate a small percentage eg 510 of the feed to discovery items These can be highpopularity events outside the users typical profile or opportunities from a completely different sport category This introduces novelty and gathers data on latent interests continually enriching the user profile Integrating Blockchain for Transparent and Secure Transaction Audits Implement a permissioned distributed ledger for instance one based on the Hyperledger Fabric framework to create an immutable record of all financial movements within the gaming system Each wager deposit and withdrawal is assigned a unique transaction hash The data committed to each block should include a cryptographic timestamp the users public identifier the wager amount the specific event ID and the confirmed outcome This method establishes a nonrepudiable chronological log of all platform activities accessible to authorized parties Utilize smart contracts to automate the settlement process based on verified external data An oraclea secure thirdparty data feedprovides official event results directly to the smart contract Once the predefined conditions are met eg a final score is confirmed the contract automatically executes the payout to the users linked wallet address This removes manual intervention minimizes payment delays and prevents disputes arising from settlement errors or manipulation The contracts code is itself auditable on the ledger For regulatory compliance and internal review provide auditors with a dedicated node granting readonly access to the transaction ledger This allows them to independently query and verify the entire history of transactions without requiring direct access to the platforms operational database Audits shift from sampling data sets to verifying the cryptographic chain of the entire ledger This approach reduces the time required for financial verification and provides mathematical certainty of data integrity Store all Personally Identifiable Information PII offchain in a separate encrypted database The onchain records should only reference a pseudonymous user ID or public key This separation ensures that transaction transparency does not compromise user privacy The link between the pseudonymous onchain ID and the realworld user identity is maintained securely within the operators private infrastructure accessible only on a needtoknow basis