Computers

Build confidence working with Excel, from your first workbook to clear, useful reports. This beginner-friendly guide explains how to enter and organize data, use formulas and functions, format cells and tables, sort and filter information, and create charts that make results easier to understand. Step-by-step examples help you practice everyday spreadsheet tasks and develop a foundation in data organization and analysis. Designed for students, office users, small-business owners, and anyone learning spreadsheet skills. No previous experience is required.

Everything a front-end developer needs, in one book, checked against the web platform as it stands in 2026. Front-End Web Development is the first volume of the Web Coding Series. It takes you from an empty editor to fast, accessible, well-ranked websites and web applications, and it shows the real result of almost every example: rendered browser screenshots, genuine console and compiler output, and diagrams of what happens under the hood. What you will learn Development environments: VS Code and AI coding assistants, browser DevTools, hosting and your own server with HTTPS, Node.js, npm and pnpm, Vite and other bundlers, ESLint, Prettier and Biome, Vitest, Jest and Playwright, Git, and a survey of React, Vue, Angular, Svelte and Solid. HTML: structure, text, images and media, forms and constraint validation, popovers and dialogs, Web Components and shadow DOM, security (CSP, SRI, Permissions-Policy, Trusted Types), accessibility with WCAG 2.2, and metadata. Bootstrap 5.3: the grid, every major component, utilities, Sass customization, the ecosystem and a preview of Bootstrap 6, finished with a responsive landing page project. CSS: the cascade, modern colour (OKLCH, color-mix, light-dark), selectors with :has(), @layer and @scope, container queries, Flexbox and Grid, anchor positioning, scroll-driven animations and view transitions, responsive design, Sass, Less, PostCSS and Lightning CSS. Tailwind CSS 4: how the engine works, theme variables, variants, dark mode, plugins and component libraries, and the Bootstrap landing page rebuilt in Tailwind with measured CSS sizes. JavaScript (ECMAScript 2026): scope and modules, objects and prototypes, closures, promises and the event loop, classes, iterators and generators, typed arrays, Temporal, regular expressions, Intl, Proxy, memory and performance in V8. TypeScript 6 and the native TypeScript 7 compiler: everyday types, narrowing, generics, conditional and mapped types, modules, declaration files and project configuration. The DOM and Web APIs: events, observers, Fetch and streams, WebSocket and WebRTC, IndexedDB and OPFS, service workers and PWAs, media and device APIs, passkeys and Web Crypto, Core Web Vitals, built-in AI APIs, and building a Manifest V3 browser extension. Search engine optimization: how crawling, rendering and ranking really work, which ranking factors Google confirms and which are myths, microformats and structured data, technical SEO for JavaScript sites, and Generative Engine Optimization for AI answers. Why this book is different Every version number, browser-support status and price was verified against official documentation at the time of writing. Hundreds of runnable listings, rendered demos, tables and SVG diagrams, with the output actually produced. Honest guidance: outdated techniques are modernized, experimental features are labeled, and SEO folklore is separated from documented fact. Works as a cover-to-cover course for beginners and as a desk reference for working developers.

XML, data formats, analytical SQL, Spark, Kafka, Airflow and the lakehouse: the whole path of data from where it is born to the figure on a dashboard, with every command run for real. Data Engineering is the seventh and final volume of the Web Coding Series. You transform a publisher's XML catalog with XPath, XQuery and XSLT, write the same orders as JSON, CSV, Avro, Parquet and Arrow, model and query them in PostgreSQL and DuckDB with dbt, process them at scale with Apache Spark, stream them through Apache Kafka, schedule the whole pipeline with Airflow, Dagster and Prefect, and land it in an Apache Iceberg lakehouse with data quality checks, lineage and governance. One business, BookNest, a small online bookshop, runs through all eight chapters, and the final capstone runs its entire data platform in a single pipeline. Every example was run. The commands were executed on one modest 4-CPU machine in Ubuntu 26.04 under WSL2 and Docker, with Saxon-HE 12.10, PostgreSQL 18.6, DuckDB 1.5.5, Spark 4.2.0, Kafka 4.3.1, Airflow 3.3.2, Iceberg 1.12.0 and Trino 483, and the listings show their actual output. Managed cloud warehouses, Spark platforms, Kafka services and lakehouses are compared from their documentation with verified prices and marked as not run here; BookNest's order events also ran on Aiven's free Kafka plan, which needs no credit card. What you will learn Foundations: the data engineering lifecycle, OLTP and OLAP, batch and streaming, Lambda and Kappa, the modern data stack, warehouses, lakes, lakehouses, data mesh, data fabric and the real cost of tools. XML: namespaces, XPath 3.1, XQuery 3.1 with Full Text and Update, XSLT 3.0 with streaming, DTD, XSD 1.1, RELAX NG and Schematron, parsers, web services and XML security. Data formats: JSON pitfalls, JSON Schema, jq, CSV, Avro, Protocol Buffers, Parquet, ORC and Arrow internals, codecs and schema evolution. Analytical SQL: column stores, PostgreSQL 18 and DuckDB for analytics, dimensional modeling, slowly changing dimensions, dbt, ClickHouse, semantic layers and the cloud warehouses compared. Apache Spark: how a job runs, PySpark DataFrames, Spark SQL, UDFs and Arrow, the Spark UI, tuning, Spark on Kubernetes, and Polars, DuckDB and Dask as alternatives. Apache Kafka: the log on disk, KRaft, producers, transactions and exactly-once, consumer and share groups, Connect, Schema Registry, Kafka Streams, Flink, security, tiered storage, Confluent Cloud, Amazon MSK, Azure Event Hubs, Aiven and Redpanda. Orchestration: idempotent pipelines, Airflow 3 in Docker Compose, dbt with Cosmos, Dagster, Prefect, change data capture with Debezium, Airbyte and dlt. The lakehouse: MinIO and the S3 API, Iceberg and its catalogs, Delta Lake and Hudi, Trino, streaming Kafka into Iceberg, data quality with GX Core, Soda and dbt tests, contracts, lineage and governance. Why this book is different The commands were executed, not imagined. Where the documentation and the real behavior disagreed, the book reports what actually happened. One BookNest data set is carried from an XML catalog to a lakehouse, where files, Spark, Trino and DuckDB agree on its sales total to the cent. It explains what happens under the hood, from XML parsers and Parquet pages to Spark's planner, Kafka's replicated log and Iceberg's metadata tree. Honest comparisons of open-source and commercial alternatives, with licences and verified prices. Hundreds of listings, diagrams and screenshots, reference appendices, interview questions and an index of open-source tools. Ten "Test Yourself!" questions end every chapter, and every answer was produced by running the code.

Master SQL from the fundamentals to advanced querying, analysis, and real-world problem solving. SQL is one of the most important skills in data, analytics, software engineering, and modern business intelligence. But learning SQL is not just about memorizing commands. You need to understand how queries work, how tables relate to each other, how to write efficient solutions, and how to think through real data problems. This book takes you from the foundations of SQL to advanced concepts in a structured, practical way. Inside, you’ll learn: • SQL fundamentals and relational database concepts • Filtering, sorting, grouping, and aggregations • Joins and subqueries • Common Table Expressions (CTEs) • Window functions and advanced analytical queries • Conditional logic and date/time operations • Set operations and advanced query techniques • Data cleaning and transformation with SQL • Working with real-world analytical problems • Query optimization and performance considerations • Best practices, common mistakes, and important SQL gotchas Whether you're learning SQL for the first time, preparing for interviews, working in data analytics, or looking to strengthen your existing skills, this book is designed to be both a learning guide and a practical reference. The goal is simple: help you understand SQL deeply enough to write better queries, solve real problems, and work confidently with data.