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Data Engineering Guide

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

Scala vs. Java

Scala vs. Java: Discover why using Scala with Java can streamline big data workflows and offer numerous advantages over using Java alone.

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.

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.

The Pitfalls of ETL Processing

ETL processing has been around since the 1970s but 40 years later many organizations continue to suffer from the constraints of traditional batch processing

Is PySpark SQL Still Relevant?

Explore PySpark SQL for data extraction and analysis, and discover how Snowpark challenges its relevance and boosts efficiency for data engineers and developers

Big Data Engineering

Big data engineering is the process of managing the ingestion and transformation of high volume data sets from various disparate sources.

Data Integration

Data integration is the process of combining data from multiple sources into a unified view to provide users valuable and actionable information.

What is Machine Learning?

Machine learning is an application of artificial intelligence (AI) that enables systems to learn automatically and improve through experience without the assistance of explicit programming.

What is JSON?

JSON is an open-standard data format or interchange for semi-structured data. Unlike flat files such as CSVs, JSON files store data in nested objects and arrays