31 posts
The traditional paradigm of backend engineering has long been rooted in deterministic logic: "If X, then Y." However, as we integrate Large Language Models (LLMs) and specialized ML agents into produc...
When a Senior Engineer approaches a system design problem, they focus on the "how"—the specific technologies, the schema, and the API endpoints. When a Staff+ Engineer approaches the same problem, the...
Designing distributed systems requires balancing multiple competing concerns. This article examines llm guardrails & safety (prompt injection, abuse), exploring architecture patterns that power succes...
Building systems that scale requires more than just knowing the technology—it demands understanding business requirements and engineering constraints. Here, we explore observability & golden signals (...
In the world of high-scale distributed systems, the dream of "strong consistency" often collapses under the weight of global latency and the inevitability of network partitions. As staff engineers, we...
Great system design combines theory with practical experience from real-world implementations. In this piece, we'll dive into secure multi-tenant saas (auth, isolation, limits), revealing the trade-of...
For decades, data engineering was bifurcated into two distinct worlds: the Data Warehouse and the Data Lake. Data Warehouses, like Snowflake or Teradata, offered high-performance SQL and ACID transact...
Building systems that scale requires more than just knowing the technology—it demands understanding business requirements and engineering constraints. Here, we explore defining slos, slis, and error b...
In the current economic climate, the "growth at all costs" mentality has been replaced by a rigorous focus on unit economics. For distributed systems engineers, this shift is most visible in how we ha...
In the world of high-scale distributed systems, the transition from a single-region architecture to an Active-Active multi-region setup represents a significant engineering milestone. For companies li...
In the modern ML landscape, the bottleneck for productionizing models has shifted from model architecture to data engineering. Companies like Uber, Netflix, and DoorDash have pioneered the concept of ...
In the early days of software engineering, a simple cron job on a single server was often sufficient to handle recurring tasks like database backups or report generation. However, as organizations tra...
In the traditional world of distributed systems, our primary concern was the deterministic flow of data: a request comes in, we query a relational database, apply business logic, and return a JSON res...
System design is often misunderstood as the art of drawing boxes and arrows on a whiteboard. However, for staff and senior engineers, the visual diagram is merely the byproduct of a much deeper cognit...
Building a production-grade system for Large Language Model (LLM) inference at scale represents a fundamental shift in distributed systems design. Unlike traditional microservices at companies like Ub...
In the modern era of microservices, the "you build it, you run it" mantra has reached a breaking point. As organizations scale from dozens to thousands of services, the cognitive load on individual de...
In modern distributed systems, the traditional request-response model often acts as a bottleneck for high-throughput applications. When a user clicks "Place Order," a synchronous system might attempt ...
Great system design combines theory with practical experience from real-world implementations. In this piece, we'll dive into secure api design (auth, rate limits, abuse prevention), revealing the tra...
In the world of Software-as-a-Service (SaaS), the database architecture is the most consequential decision a founding engineering team will make. At the scale of Shopify or Stripe, the challenge isn't...
In the lifecycle of a high-growth technology company, there is a definitive moment when "checking the logs" transitions from a manual task to a distributed systems challenge. As organizations like Net...
In the early stages of a startup, the mantra is "growth at all costs." Engineering teams prioritize velocity, shipping features to find market fit while treating cloud infrastructure as an infinite, a...
Designing a payment processing system is one of the most challenging tasks for a software engineer. Unlike a social media feed where a missed post is a minor inconvenience, a payment system deals with...
In the modern ML lifecycle, the bottleneck has shifted from model architecture to data engineering. At organizations like Meta, Uber, and Netflix, the challenge isn't just training a model with billio...
In the world of hyper-growth ride-sharing platforms like Uber and Lyft, data isn't just a byproduct of the business; it is the heartbeat of the operational engine. When you open an app and see "surge ...
In modern distributed architectures, the "noisy neighbor" problem is a constant threat to system stability. Whether it is a malicious DDoS attack or a misconfigured internal service making recursive c...
In the world of distributed systems, failure is not an elective; it is a fundamental property of the environment. As systems scale from single-node prototypes to global infrastructures like those mana...
In the era of hyper-scale applications, the dream of a "global database" that is simultaneously fast, always available, and perfectly consistent everywhere is the holy grail of engineering. However, a...
In the evolution of a technology company, there is a distinct "Maturity Gap" between a data scientist training a model in a Jupyter notebook and a software engineer deploying a high-availability distr...
In the early days of microservices, the industry leaned heavily on synchronous REST APIs. However, as organizations like Uber and Netflix scaled to millions of concurrent users, they hit the "Distribu...
In the world of distributed systems, the network is fundamentally unreliable. Packets drop, connections time out, and services crash at the most inopportune moments. In most domains, a retry is a harm...
In the modern distributed landscape, data is no longer a static asset sitting in a relational database; it is a continuous stream of pulses representing user behavior, system health, and financial tra...