5 Essential .NET Libraries Every Developer Should Master


As .NET developers, we often find ourselves building applications that require PDF generation, mock data, Excel integration, performance tuning, or resilient networking. Instead of reinventing the wheel, we can rely on powerful, battle-tested libraries that make our jobs easier and our applications more robust.

In this blog post, we’ll explore five exceptional .NET libraries that help us achieve these goals:

  1. QuestPDF
  2. Bogus
  3. ClosedXML
  4. BenchmarkDotNet
  5. Polly
  • A description of the library
  • Key use cases
  • Pseudocode to get you started
  • A link to the full working code on GitHub

QuestPDF is a modern .NET library for creating PDF files using a fluent, code-first approach. Unlike traditional PDF libraries that require absolute positioning and tedious layout logic, QuestPDF provides a high-level, declarative API to structure documents with ease.

  • Generating invoices, quotations, certificates
  • Creating printable reports with tables and charts
  • Exporting dynamic content to PDF
  • Headers, footers, tables, grids, text styling
  • Image embedding and pagination
  • PDF preview during development
Create class : IDocument
Implement Compose()
Define page size, margins
Add header with title, date, logo
Add customer and invoice details
Add table of items with totals

Call: new QuotationDocument().GeneratePdf("Quote.pdf");
Working code on GitHub and Documentation

Bogus is a powerful .NET library to generate fake but realistic data for testing and development. It supports everything from names and emails to complex object graphs.

  • Seeding test databases
  • Generating demo data for UI/UX
  • Populating mock APIs
  • Localized data (e.g., Indian names, addresses)
  • Faker rules for nested data
  • Supports rules, formatters, ranges
Create Faker<Customer> with name, email, address
Generate 10 customers
Create Faker<Invoice> and assign to each customer
Create Faker<Payment> and link to each invoice
Working code on GitHub and Documentation

ClosedXML allows you to read and write Excel .xlsx files in .NET without requiring Microsoft Office. It’s built on top of OpenXML SDK with a simpler API.

  • Exporting reports or summaries to Excel
  • Importing structured data from Excel sheets
  • Replacing manual data entry with automation
  • Create, read, update .xlsx files
  • Tables, styling, formatting, validation
  • Multiple worksheets
  • Works in Windows, Linux, macOS
Export:
Create XLWorkbook
Add Worksheets: Customers, Invoices, Payments
InsertTable() for each list
workbook.SaveAs("Data.xlsx")

Import:
Load workbook
Read rows, map to model objects
Working code on GitHub and Documentation

BenchmarkDotNet is a gold-standard benchmarking library that helps you measure execution time and memory usage of code segments precisely.

  • Comparing algorithm efficiency
  • Measuring memory allocations
  • Analyzing hot paths in performance-critical apps
  • Mean, StdDev, Allocations, GC impact
  • RankColumn, memory diagnoser
  • Export to CSV, HTML, JSON
[Benchmark] ForLoop()
[Benchmark] LinqSum()
[Benchmark] ParallelSum()

Use [GlobalSetup] for test data
Run: BenchmarkRunner.Run<YourClass>()
Working code on GitHub and Documentation

Polly is a resilience and transient-fault-handling library that allows developers to define sophisticated policies such as Retry, Circuit Breaker, Fallback, and Timeout.

  • Retrying failed network/API calls
  • Implementing fallback logic for service downtime
  • Protecting APIs with circuit breakers
  • Managing bulkhead isolation
  • Retry, Timeout, Fallback, CircuitBreaker
  • Fluent, composable policies
  • Async and sync support
  • Integration with HttpClientFactory
retry = Policy.Handle<Exception>().Retry(3)
fallback = Policy.Fallback(() => return default)
timeout = Policy.Timeout(2 sec)

wrap = fallback.Wrap(retry).Wrap(timeout)
wrap.Execute(() => RiskyMethod())
Working code on GitHub and Documentation

These five libraries provide incredible value to any .NET project, helping you:

  • Build polished outputs (PDF, Excel)
  • Seed and simulate realistic data
  • Optimize performance
  • Handle real-world failures gracefully
🔗 Clone the GitHub Repository to get started immediately.