Data Engineering Guide

Articles about important cloud data engineering topics, including ETL, data integration, and JSON.

ETL vs ELT

Discover the key differences in ETL vs. ELT as well as some of the benefits in preparing data for analysis and additional processing..

Internet of Things (IoT) Architecture for Actionable IoT Analytics

In this article, we examine why IoT data is so valuable to companies across industries, components of an IoT architecture, and best practices for IoT analytics.

Python Applications: Accelerating Data-Centric App Development

In this article, we’ll explore the primary reasons driving Python’s rapid adoption and share examples of how it’s being used in application development.

AI Predictive Maintenance in Government and Public Sector

In this blog post, we look at why AI is so beneficial and how government organizations at the federal and local levels are using AI for predictive maintenance.

Sales Forecasting: Using ML to Improve Pipeline Forecasting Accuracy

Learn how businesses use sales forecasting in planning, risk management, financial performance optimization, and customer satisfaction.

Anaconda for Python: Unleashing the Power of Python for Data Science

Anaconda is a Python distribution system, optimizing package management and deployment. Explore its vital role in data science, AI and ML within this article.

What Is a Feature Store in Machine Learning?

The feature store plays a critical role in the application of ML — including real-time fraud detection and e-commerce product recommendations. Read more here.

What is ETL?

ETL (extract, transform, load) are the three processes that move data from one or multiple databases, or other sources to a unified repository. Learn more here.

What is Data Modeling?

Data modeling is the process of organizing and mapping data using simplified diagrams, symbols, and text to represent data associations and flow. Learn more here.