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Data Engineering Guide

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

What is a Data Pipeline?

A data pipeline is a means of moving data from a source to a destination. Along the journey, data is transformed and optimized, arriving in an analyzable state.

Python for Data Engineering

In this post, we’ll dive into the world of data engineering with Python, discuss how it’s used, and share some of the libraries and data engineering use cases.

What is an ETL Pipeline?

ETL pipeline is extracting, transforming, and loading of data into a database. ETL pipelines are a type of data pipeline, preparing data for analytics and BI.

Machine Learning Libraries: Streamlining the Path from Development to Deployment

Explore machine learning libraries with Snowflake for streamlined development, training, and deployment, enhancing product innovation.

Machine Learning Frameworks: Streamlining ML Model Development

Explore what machine learning frameworks are, and learn about the five leading ML frameworks and how to choose the best one for your organisation.

ML-Based Anomaly Detection

Discover how ML enhances anomaly detection and Snowflake's role in making ML insights accessible for quick, efficient problem-solving.

LLMOps: a Structured Approach for Operationalizing Large Language Models

Discover LLMOps: specialized solutions for Large Language Models (LLMs) to overcome training and deployment challenges and maximize business value.

AI Models: What They Are and How They Work (with examples)

Dive into what AI models are and how they accelerate the process of understanding and interpreting data, highlighting six of the most popular models.

Explainable AI: Building Stakeholder Trust and Business Value

Explainable AI (XAI) seeks to shine a light on AI systems so stakeholders can understand why certain results were produced. Read more on what is explainable AI.