Posts

Introduction In the world of storage systems, data integrity is a non‑negotiable requirement. A single corrupted byte can cascade into file system corruption, application crashes, or even data loss. While traditional journaling filesystems protect against power failures and crashes by replaying a write‑ahead log (the journal), they often assume the journal itself is trustworthy. In practice, hardware faults, memory errors, or transmission glitches can corrupt journal entries before they are applied to the main file system structures. ...

Introduction File systems are the silent workhorses that make modern computing possible. While most users interact with them through simple operations—open a file, save a document, delete a folder—the underlying data structures are far more complex. One such complexity is the handling of directory entries, especially when a directory contains millions of files or tens of thousands of subdirectories. Historically, many file systems imposed hard limits on the number of subdirectories a single directory could contain. The reason? Traditional linear directory layouts required scanning the entire list of entries for every lookup, making large directories both slow and memory‑intensive. ...

Table of Contents Introduction Background 2.1. [Fraud in Financial Ledger Systems] 2.2. [Distributed Ledger Technologies (DLTs)] 2.3. [Traditional Fraud Detection Approaches] Representing Ledger Data as Graphs 3.1. [Node Types and Attributes] 3.2. [Edge Types and Temporal Information] 3.3. [Feature Engineering Example with NetworkX] Fundamentals of Graph Neural Networks 4.1. [Message‑Passing Framework] 4.2. [Popular GNN Architectures] 4.3. [Loss Functions for Anomaly Detection] Designing GNNs for Fraud Detection 5.1. [Supervised vs. Semi‑Supervised Learning] 5.2. [Handling Imbalanced Data] 5.3. [Temporal/Dynamic Graphs] 5.4. [Sample PyTorch Geometric Model] Case Study: Money‑Laundering Detection on a Permissioned Blockchain 6.1. [Dataset Overview] 6.2. [Graph Construction Pipeline] 6.3. [Training and Evaluation] 6.4. [Results & Interpretation] Practical Considerations for Production 7.1. [Scalability & Distributed Training] 7.2. [Privacy, Compliance, and Federated Learning] 7.3. [Model Explainability] Deployment Strategies 8.1. [Real‑Time Inference Architecture] 8.2. [Integration with AML/Compliance Suites] 8.3. [Monitoring & Model Drift] Future Directions Conclusion Resources Introduction Financial institutions are increasingly moving their transaction records onto distributed ledger technologies (DLTs)—public blockchains, permissioned ledgers, or directed‑acyclic‑graph (DAG) systems. While DLTs provide immutability, transparency, and auditability, they also introduce new attack surfaces. Fraudsters exploit the pseudonymous nature of many ledgers, creating complex, multi‑hop transaction patterns that evade classic rule‑based anti‑money‑laundering (AML) systems. ...

Introduction Autonomous agents—whether they are self‑driving cars, swarms of delivery drones, or collaborative factory robots—rely on real‑time machine‑learning inference to perceive the world, make decisions, and execute actions. As the number of agents grows and the complexity of models increases, a single on‑board processor quickly becomes a bottleneck. The solution is to distribute inference across a fleet of heterogeneous compute nodes (cloud GPUs, edge TPUs, FPGA accelerators, even spare CPUs on nearby devices) and to dynamically allocate those resources based on workload, latency constraints, and power budgets. ...

Table of Contents Introduction Why Choose Avro? Core Concepts and Benefits Avro Data Types & Schema Language Schema Evolution: Compatibility Rules in Practice Working with Avro in Java Working with Avro in Python Avro & Apache Kafka: The Perfect Pair Integrating with Confluent Schema Registry Performance & Storage Considerations Best Practices & Common Pitfalls Conclusion Resources Introduction In the modern data‑centric ecosystem, moving data efficiently and safely between services, storage layers, and analytics platforms is a daily challenge. Binary serialization formats—such as Protocol Buffers, Thrift, and Apache Avro—provide the backbone for high‑throughput pipelines, especially when dealing with terabytes of streaming events or batch‑oriented Hadoop jobs. ...