Articles

Coordination Services 🇺🇸

September 13, 2026
Category: Backend Engineers Guide

In large-scale distributed systems, many processes, microservices, or nodes must work together while running on different machines. These systems need a reliable way to agree on shared state, detect failures, elect leaders, coordinate ownership, and distribute configuration...

Data Warehousing 🇺🇸

September 13, 2026
Category: Backend Engineers Guide

Data warehousing unifies large volumes of information from different sources into a centralized repository that supports analytics, reporting, and strategic decision-making. By collecting operational data, transforming it, and then loading it into one or more specialized databases, data warehouses a...

Replication 🇺🇸

September 13, 2026
Category: Backend Engineers Guide

Replication is a method of maintaining copies of data across multiple nodes in distributed systems, making it useful for improving availability, reducing latency, and distributing load. Below are detailed notes, organized in bullet points, each containing one highlighted word in the middle to emphas...

Indexes 🇺🇸

September 13, 2026
Category: Backend Engineers Guide

Indexing is one of the most effective ways to optimize database queries. By maintaining auxiliary data structures that map certain key values to their physical or logical locations, indexes allow a database to rapidly locate rows that match a search condition. This reduces the number of full-table s...

Isolation Levels 🇺🇸

September 13, 2026
Category: Backend Engineers Guide

Isolation levels in relational-database systems govern how simultaneously running transactions perceive one another’s changes. They sit on a spectrum that trades consistency guarantees—how “correct” every read is—against concurrency—how many transactions can safely overlap. Choosing the right level ...

Types of Databases 🇺🇸

September 13, 2026
Category: Backend Engineers Guide

Databases store and organize data so that applications and users can retrieve, manage, and manipulate information efficiently. The choice of database often depends on data structure requirements, scale, performance expectations, and the nature of the workload. Over the years, numerous types of datab...

Transactions 🇺🇸

September 13, 2026
Category: Backend Engineers Guide

Database transactions are a cornerstone of reliable data management. They let an application bundle multiple low-level reads and writes into a single, all-or-nothing unit so the database moves cleanly from one consistent state to another—even when dozens of users race to change the same rows or hard...

Halloween Problem 🇺🇸

September 13, 2026
Category: Backend Engineers Guide

The Halloween Problem is a database execution-plan issue where an UPDATE operation could theoretically update the same row more than once if the database scans rows through an access path that is changed by the update itself...

Caching Strategies 🇺🇸

September 13, 2026
Category: Backend Engineers Guide

Caching is a technique used to speed up data retrieval by placing frequently accessed or computationally heavy information closer to the application or the end user. Below is an expanded set of notes on caching, presented with ASCII diagrams and bullet points that emphasize key considerations. Each ...

Memcached 🇺🇸

September 13, 2026
Category: Backend Engineers Guide

Memcached is a high-performance, distributed, in-memory key-value cache. It is designed for one main purpose: storing small pieces of frequently accessed data in RAM so applications can avoid repeatedly querying slower systems such as relational databases, APIs, or disk-backed storage...

Database Caching 🇺🇸

September 13, 2026
Category: Backend Engineers Guide

Database caching stores frequently requested database query results, rows, aggregates, or relational lookup data in a faster layer so the application does not repeatedly execute the same expensive database operations. The main goal is to reduce database read load, lower query latency, and protect th...

Redis 🇺🇸

September 13, 2026
Category: Backend Engineers Guide

Redis is a high-performance, in-memory data store commonly used as a cache, message broker, session store, rate limiter, leaderboard engine, and fast key-value database. It is often described as a data structure server because it supports rich built-in data types such as strings, lists, sets, sorted...

Application Level Caching 🇺🇸

September 13, 2026
Category: Backend Engineers Guide

Application-level caching stores computed results or frequently accessed objects directly inside the running process or in a dedicated in-process store. Because data never leaves the application's memory space, reads are limited only by CPU and memory bandwidth — no network hop, no serialisation, an...

Memory Map 🇺🇸

September 13, 2026
Category: Backend Engineers Guide

mmap, short for memory map, is an operating system mechanism that maps a file or device directly into a process’s virtual memory address space. Instead of reading file data explicitly with read() into a buffer, the application can access the file as if it were an array in memory. The operating syste...

Http Caching 🇺🇸

September 13, 2026
Category: Backend Engineers Guide

HTTP caching is the process of storing copies of HTTP responses so that future requests can be served without contacting the origin server. It operates at multiple layers — the browser, intermediate proxies, reverse proxies, and CDN edge nodes — and is controlled primarily through standardised HTTP ...

Content Delivery Networks 🇺🇸

September 13, 2026
Category: Backend Engineers Guide

A Content Delivery Network (CDN) is a geographically distributed system of servers that deliver web assets such as images, videos, stylesheets, and scripts to users based on their proximity to the nearest server. By placing cached copies of content at strategic locations around the world, CDNs drast...

Multiple Comparisons 🇺🇸

September 13, 2026
Category: Statistics Notes

When conducting multiple hypothesis tests, the probability of making at least one Type I error—falsely rejecting a true null hypothesis—increases. This is known as the multiple comparisons problem or, in some contexts, the look-elsewhere effect...

Type i and Type Ii Errors 🇺🇸

September 13, 2026
Category: Statistics Notes

Hypothesis testing allows researchers to evaluate claims about a population using sample data. We begin with a null hypothesis, $H_0$, which represents the claim being tested, and an alternative hypothesis, $H_a$, which represents a competing claim. Because decisions are based on sample data, errors...

Analysis of Categorical Data 🇺🇸

September 13, 2026
Category: Statistics Notes

The chi-square ($\chi^2$) test is a family of statistical tests for categorical count data. These tests compare observed frequencies with the frequencies expected under a null hypothesis...

Confidence Intervals 🇺🇸

September 13, 2026
Category: Statistics Notes

A confidence interval (CI) is a range of plausible values for a population parameter, such as a mean or proportion, calculated from sample data. It supplements a point estimate by showing the uncertainty around that estimate...

Hypothesis Testing 🇺🇸

September 13, 2026
Category: Statistics Notes

Hypothesis testing is a statistical tool used to draw conclusions about populations based on sample data. It is widely applied in scientific research, from evaluating new treatments in clinical trials to studying customer behavior in business analytics...

Null Hypothesis 🇺🇸

September 13, 2026
Category: Statistics Notes

Statistical hypothesis testing is a method for using sample data to make inferences about a population. Understanding null and alternative hypotheses, along with how p-values are calculated and interpreted, is essential for applying hypothesis tests correctly...

Point Estimation 🇺🇸

September 13, 2026
Category: Statistics Notes

A parameter is an unknown feature of a population or statistical model, such as a mean $\mu$, variance $\sigma^2$, probability $p$, or regression coefficient $\beta$. A point estimator is a rule or statistic computed from sample data and used to estimate that parameter...

Stationarity 🇺🇸

September 13, 2026
Category: Statistics Notes

Stationarity describes which probabilistic features of a time series remain stable when the time origin is shifted. Weak stationarity focuses on a constant mean and variance together with an autocovariance that depends only on lag, while strict stationarity requires the full joint distribution to be...

Forecast Evaluation 🇺🇸

September 13, 2026
Category: Statistics Notes

Forecast evaluation measures how an entire forecasting procedure performs on observations that were genuinely unknown when each prediction was made. It is therefore different from in-sample fit: transformations, feature construction, parameter estimation, predictor values, and model selection all ha...

Autoregressive Models 🇺🇸

September 13, 2026
Category: Statistics Notes

Autoregressive models describe a time series whose current value depends linearly on its own recent history plus a new innovation. The lag coefficients determine how strongly past observations persist into the present and whether the process tends to decay smoothly, alternate, or oscillate...

Frequency Domain Analysis 🇺🇸

September 13, 2026
Category: Statistics Notes

Frequency-domain analysis describes a time series by the cycle lengths that contribute to its variation rather than by relationships at individual lags. The periodogram and spectral density provide a complementary view to the ACF, translating repeated temporal structure into peaks at corresponding f...

Stochastic Processes and White Noise 🇺🇸

September 13, 2026
Category: Statistics Notes

A stochastic process is a collection of random variables indexed by time, and an observed time series is one realized path from that process. This viewpoint separates the data we actually see from the probabilistic mechanism used to describe possible paths and future uncertainty...

Dynamic Regression 🇺🇸

September 13, 2026
Category: Statistics Notes

Dynamic regression combines external predictors with time-series structure so that the conditional mean can respond to explanatory variables without assuming the remaining errors are independent. Lagged predictors can represent delayed effects, while ARMA or ARIMA errors capture serial dependence le...

Invertibility 🇺🇸

September 13, 2026
Category: Statistics Notes

Invertibility is the condition that lets a moving-average or ARMA model recover its unobserved innovations from the observed series in a stable way. It turns the shock representation of the model into a usable past-based filter whose coefficients decay rather than grow without bound...

Multivariate Time Series 🇺🇸

September 13, 2026
Category: Statistics Notes

Multivariate time-series models describe several evolving variables jointly so that cross-lag feedback, shared shocks, and predictive relationships can be represented within one system. They are useful when separate univariate models would ignore information carried by the histories of related serie...

Random Walk 🇺🇸

September 13, 2026
Category: Statistics Notes

A random walk models a level that changes by accumulating new shocks over time. Each innovation is temporary as an increment but permanent in the level, so the process can drift far from its starting point even when the expected one-step change is zero...

Difference Equations 🇺🇸

September 13, 2026
Category: Statistics Notes

A difference equation describes how a discrete-time quantity evolves from its earlier values. Rather than specifying every term directly, it gives a recursive rule, so the behavior of the sequence is determined by the recursion together with its initial conditions...

Financial Time Series Models 🇺🇸

September 13, 2026
Category: Statistics Notes

Financial time series often separate naturally into a price level, a return process, and a time-varying scale of uncertainty. Returns may show little predictable movement in their conditional mean while their magnitude clusters over time, producing periods of calm and periods of elevated volatility...

State Space Models 🇺🇸

September 13, 2026
Category: Statistics Notes

State-space models separate a time series into an unobserved state that evolves through time and an observation process that measures that state with noise. This framework is flexible enough to represent latent levels, trends, seasonal components, regression effects, missing observations, and many f...