Amazon Kinesis
What Amazon Kinesis is, how it processes streaming data in real time, and when to use Kinesis versus other streaming options.
Real-Time
Apache Flink - Stateful Stream Processing Framework
Apache Flink is a distributed stream processing framework for stateful computations over unbounded and bounded data streams.
Apache Kafka - Distributed Event Streaming Platform
Apache Kafka is a distributed event streaming platform used for high-throughput, fault-tolerant real-time data pipelines and streaming …
Azure Data Explorer - Real-Time Analytics and Time Series Database
Azure Data Explorer is a fast, fully managed data analytics service optimized for real-time analysis of large volumes of streaming and time …
Azure Event Hubs - Big Data Streaming Ingestion
Azure Event Hubs is a fully managed real-time data streaming platform capable of ingesting millions of events per second for big data and AI …
Batch vs Real-Time Inference Patterns
Comparing batch and real-time inference patterns for ML models, covering architecture, cost, latency, and when to use each approach.
Case Pattern: AI Fraud Detection for a Regional Bank
Architecture and lessons from deploying a real-time AI fraud detection system processing 2 million transactions daily for a regional banking …
ClickHouse - High-Performance Columnar Analytics Database
ClickHouse is an open-source columnar database management system optimized for real-time analytical queries on large datasets.
Cloud Firestore - Serverless Document Database
Google Cloud Firestore is a serverless, scalable NoSQL document database with real-time synchronization, offline support, and strong …
Online Learning
Incremental machine learning approach that updates models continuously with streaming data rather than retraining from scratch.
Real-Time Data Pipelines for AI Workloads
Implementation guide for real-time streaming data pipelines: four-layer architecture, Flink feature computation, late-arriving data handling …
Real-Time Feature Computation Pattern
The architectural pattern for computing ML features from event streams: windowed aggregations, stream-table joins, dual-write to online and …
Real-Time Feature Serving
Sub-millisecond feature serving for online inference: architecture, caching strategies, precomputation patterns, and consistency guarantees.
Real-Time vs Batch AI Processing - Choosing the Right Pattern
Decision framework for choosing between real-time and batch AI processing. Latency requirements, cost tradeoffs, hybrid architectures, and …
Response Streaming Patterns for AI Applications
Implementing streaming responses from LLMs for improved perceived latency. Server-sent events, chunked processing, and progressive …
Stream Processing
What stream processing is, how Flink, Spark Streaming, and Kafka Streams enable real-time data transformation, and why streaming matters for …
Supabase - Open-Source Firebase Alternative
Supabase is an open-source backend-as-a-service platform providing a PostgreSQL database, authentication, real-time subscriptions, storage, …
WebSocket
What WebSockets are, how they enable real-time bidirectional communication, and why they are used for streaming LLM token delivery to …
AI Fraud Detection for Financial Services
Real-time transaction scoring, anomaly detection, behavioral biometrics, and investigation prioritization for financial fraud prevention.