Understanding Data Cleaning

Data cleaning is an essential step in the data transformation process. It involves finding and correcting errors, inconsistencies, and inaccuracies in raw data.

Data Profiling

Analyze and review data to understand its quality and structure, identifying issues like missing values and inconsistencies.

Data Auditing

Verify and review the accuracy and completeness of data through manual or automated processes.

Data Validation

Check data accuracy and completeness against predefined rules and standards.

Enriching Your Data

Data enrichment adds value to your existing dataset by incorporating additional relevant information.

Data Augmentation

Add new variables and observations to enhance your dataset's comprehensiveness.

Data Integration

Combine data from multiple sources to create a more complete view.

Tools and Technologies

Spreadsheets

Excel and Google Sheets for basic data transformation

Programming

Python and JavaScript for advanced data processing

Cloud Platforms

AWS and Google Cloud for scalable solutions

Alteryx

Used for data preparation, blending, and analysis

Databases

SQL and NoSQL databases for data storage and retrieval

APIs

Cloud APIs for data acquisition, storage, and processing

Scripting

PowerShell, BASH, and other scripting languages for automation

File Formats

Handling of common formats like CSV and JSON

CSV/JSON Converter

Convert files between CSV and JSON formats.

Try the CSV/JSON Converter for free here