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Data Engineering Guide

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

DataOps for Data Speed and Quality

Data Operations (DataOps) is a methodology based on the agile model that’s designed to reduce the time between data need and insight. Learn more.

Data Science Pipeline

Learn how data science pipelines automate the processes of data validation; extract, transform, load (ETL); machine learning and modeling.

Continuous Integration 101

Continuous integration (CI) is a DevOps best practice designed to make an asynchronous style of workflow possible.

Scala vs. Python for Data Engineering

Explore Scala and Python differences for data engineering, and see how Snowpark accelerates data engineering workflows with both. Read more.

Python and SQL for Data Science

Python and SQL are two of the most popular programming languages. In this article, we’ll explore these two languages and how they work together in data science applications.

How a Data Ingestion Framework Powers Large Data Set Usage

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

Structured Data Versus Semi-Structured Data

This guide article explains the difference between structured and semi-structured data and why your data platform must support both for your organization to be fully data-driven.

Modern Data Processing

In this post, we’ll explain what data processing is and explore the changes that have created new demands on data processing.

Feature Engineering

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