Data

/ˈiː-tiː-ɛl/

n. “Move it. Clean it. Make it useful.”

ETL, short for Extract, Transform, Load, is a data integration pattern used to move information from one or more source systems into a destination system where it can be analyzed, reported on, or stored long-term. It is the quiet machinery behind dashboards, analytics platforms, and decision-making pipelines that pretend data simply “shows up.”

The first step, extract, is about collection. Data is pulled from its original sources, which might include databases, APIs, flat files, logs, or third-party services. These sources are rarely uniform. Formats differ. Schemas drift. Timestamps disagree. Extraction is less about elegance and more about endurance.

The second step, transform, is where reality is negotiated. Raw data is cleaned, normalized, filtered, enriched, and reshaped into something coherent. Duplicates are removed. Types are corrected. Units are converted. Business rules are applied. This is the step where assumptions become code — and where most bugs hide.

The final step, load, places the transformed data into its destination. This is often a data warehouse, analytics engine, or reporting system, such as BigQuery. The destination is optimized for reading and querying, not for the messy business of data collection.

Traditional ETL emerged in an era when storage was expensive and compute was scarce. Data was transformed before loading to minimize cost and maximize query performance. This design made sense when every byte mattered and batch jobs ran overnight like clockwork.

Modern systems sometimes invert the pattern into ELT, loading raw data first and transforming it later using scalable compute. Despite this shift, ETL remains a useful mental model — a way to reason about how data flows, where it changes shape, and where responsibility lies.

ETL pipelines often operate on schedules or triggers. Some run hourly, some daily, others in near real time. Failures are inevitable: a source goes offline, a schema changes, or malformed data sneaks through. Robust ETL systems are designed not just to process data, but to fail visibly and recover gracefully.

Consider a practical example. An organization collects user events from a website, sales data from a CRM, and billing records from a payment provider. Each system speaks a different dialect. An ETL pipeline extracts this data, transforms it into a shared structure, and loads it into a central warehouse where analysts can finally ask questions that span all three.

Without ETL, data remains siloed. Reports disagree. Metrics cannot be trusted. Decisions are made based on partial truths. With ETL, data becomes comparable, queryable, and accountable — not perfect, but usable.

ETL does not guarantee insight. It does not choose the right questions or prevent bad interpretations. What it does is establish a repeatable path from chaos to structure, turning raw exhaust into something worth examining.

In data systems, ETL is not glamorous. It is plumbing. And like all good plumbing, it is only noticed when it fails — or when it was never built at all.

/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.