Data Engineering Guide

Data wrangling aims to eliminate faulty or incomplete data before analysis. By cleaning up and augmenting existing data, business teams can make better decisions.

IoT architecture enables IoT data to transit through a network, ultimately arriving in a data platform for processing, analysis, and storage.

Semi-structured data, generated from sources such as IoT devices, mobile apps, and webpages, holds exceptional value if businesses can mine it effectively.

Learn what is ETL (extract, transform, load) process and how it compares against more modern data movement processes.

Learn about the different types of ingestion and how they relate to data integration and its methods, including batch and streaming ingestion.

Learn about CI/CD pipelines, which is an end-to-end software delivery process used to deploy new and updated software safely.

Feature engineering is the process of using domain knowledge to transform data into features that machine learning algorithms can understand. Learn more here.

Learn about the benefits, characteristics, and elements of the modern data pipeline as well as its relationship to ETL.

In this guide page we look at what is IoT, how it works, as well as the different types of IoT data and its business value.