data
Practical resources about data for software engineers.
87 results
Understanding Data Processing in Software Engineering
Every application eventually deals with data — parsing user input, serializing objects for APIs, validating form submissions, or transforming records between formats. Getting data handling right is what separates fragile scripts from production-grade systems.
This collection covers practical recipes and patterns for working with JSON, CSV, XML, YAML, and custom formats. You will find copy-paste solutions for common tasks like flattening nested objects, sanitizing user input, generating slugs, and formatting dates across Python, Java, and JavaScript.
Parse JSON
How to parse JSON strings into native data structures across multiple programming languages.
Parse Log Files
How to parse and analyze server log files using Python, Java, and JavaScript.
Parse Markdown Files
How to parse Markdown to HTML and extract structured data in Python, Java, and JavaScript.
Parse PDF Files
How to extract text and metadata from PDF files in Python, Java, and JavaScript.
Parse XML Files
How to parse XML documents in Python, Java, and JavaScript with practical code examples.
Schedule and Monitor DAGs with Apache Airflow
How to define, schedule, and monitor Directed Acyclic Graphs in Apache Airflow with operators,...
Parallel DataFrame Operations with Dask
How to use Dask for parallel DataFrame operations on datasets larger than memory, covering lazy...
Validate DataFrame Schemas with Pandera
How to validate pandas and Polars DataFrame schemas with Pandera, covering column types,...
Transform Data in the Warehouse with dbt
How to use dbt for SQL-based data transformations with models, tests, materializations, macros, and...
Read and Write Excel Files with Python
How to read, write, and format Excel spreadsheets using openpyxl and pandas in Python.
Generate QR Codes with Python
Create QR codes for URLs, text, and contact cards using the qrcode library in Python.
Build an ETL Pipeline with pandas and Parquet
How to build an extract-transform-load pipeline using pandas for data processing and Parquet for...
High-Performance DataFrame Operations with Polars
How to use Polars for fast DataFrame operations with lazy evaluation, expression API, streaming,...
Large-Scale Aggregation with PySpark
How to perform group-by aggregations on large datasets with PySpark, covering window functions,...
Extract Data from HTML Pages with Python and BeautifulSoup
Parse HTML and extract data using BeautifulSoup. Covers CSS selectors, navigation, tables,...
Regular Expressions
How to use regular expressions for pattern matching, validation, and text extraction across Python,...
Serialize and Deserialize Data
How to serialize and deserialize data in JSON, XML, and YAML across Python, Java, and JavaScript.
Sort an Array
How to sort arrays and lists in ascending, descending, and custom order across multiple languages.
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