Data

/riˈækt ˈkwɛri/

n. “Data fetching without the drama.”

React Query is a data-fetching and state synchronization library for React applications. It simplifies the management of server state — that is, data that lives on a backend API or database — and keeps it in sync with the UI without the need for complex Redux setups or manual caching.

In typical React apps, fetching data from a REST or GraphQL endpoint involves writing boilerplate for loading states, error handling, caching, and refreshing. React Query abstracts all of that. When you request data, it automatically caches results, updates components reactively, refetches stale data in the background, and provides retry mechanisms for failed requests.

For example, consider a dashboard displaying user profiles from an API. Using React Query, you can call useQuery('users', fetchUsers) and immediately get an object containing data, isLoading, isError, and other properties. The library handles caching, background updates, and re-fetching when the window refocuses or network reconnects — all without you writing extra state logic.

React Query also supports mutations, which are actions that modify server data, like creating, updating, or deleting records. When a mutation occurs, queries depending on that data can be automatically invalidated and refetched to ensure the UI remains consistent.

One of the key benefits is declarative caching. Developers can control how long data stays fresh, when to refetch, and even share cached data between components. This reduces unnecessary network requests and improves performance while keeping the UI reactive.

The library integrates smoothly with other tools in the React ecosystem, including Redux, Context API, or React Router. It is particularly useful for SPAs where the same data is accessed across multiple components or pages.

In essence, React Query is not just a fetching library; it’s a data management solution. It reduces boilerplate, ensures consistency, and turns server data into a predictable, cache-friendly, and reactive source of truth — letting developers focus on building features rather than orchestrating network logic.

/ˈɛk.səl/

n. “Numbers, tables, and logic — tamed in cells.”

Excel, whether the classic desktop version from Microsoft or the cloud-based Google variant often called Google Sheets, is a spreadsheet application designed to organize, calculate, and visualize data. It turns rows and columns into a playground for formulas, charts, and structured analysis, allowing humans to impose order on numeric chaos.

At its core, a spreadsheet is a two-dimensional grid of cells, each capable of holding static data or dynamic formulas. Formulas allow one cell to compute its value based on others, forming networks of dependencies. This enables automatic updates: change one input, and all dependent cells reflect the new reality instantly.

Excel supports a rich library of functions for math, statistics, logic, and text manipulation. From simple sums and averages to conditional statements, lookup functions, and pivot tables, users can build surprisingly complex models without writing traditional code. When formulas reach their limits, macros or scripts — in VBA for Microsoft Excel or Apps Script for Google Sheets — provide programmatic control.

Visualization is another hallmark. Charts, conditional formatting, and sparklines allow users to see trends, outliers, and relationships at a glance. Financial analysts, scientists, and business intelligence professionals rely on these capabilities to make decisions quickly, using Excel as both a sandbox and a reporting tool.

Collaboration has evolved dramatically with the cloud. Google Sheets enables multiple users to edit a spreadsheet simultaneously, see changes in real time, and comment inline. Microsoft’s Office 365 mirrors this with cloud-hosted Excel files. Version control, change tracking, and permissions make it possible to coordinate even large teams without fear of overwriting each other’s work.

Excel also interacts with external data sources. It can import CSV files, query SQL databases, or pull from REST APIs. This makes it a bridge between static reporting and live data analytics. Businesses can refresh dashboards automatically, ensuring that decisions are made with current information rather than stale numbers.

Despite its power, Excel is not just for professionals. Students, hobbyists, and casual users find value in budgeting, planning, and simple data tracking. Its flexibility scales from a single-person task list to multi-million-row datasets with advanced formulas.

In essence, Excel abstracts complexity. It turns manual computation into automated calculation, transforms raw data into insights, and allows humans to manipulate numbers, logic, and text without writing full-scale software. Its ubiquity has made it a standard skill across industries, an indispensable tool for anyone who wrestles with information.

Whether building financial models, analyzing scientific data, or managing project schedules, Excel remains a foundational application — bridging human reasoning and machine calculation in a grid of cells that never sleeps.