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Data Engineering Guide
Articles about important cloud data engineering topics, including ETL, data integration, and JSON.
A Modern ETL Process
Implementing a modern ETL process has significant benefits for efficiently building data applications and empowering data-driven decision-making.
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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.
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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.
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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.
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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.
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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.
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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.
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Lambda Architecture
Lambda architecture is a popular deployment model. In this post, we’ll explain how it works and the benefits and drawbacks of using this data architecture.
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What is Spark TensorFlow?
Spark Tensorflow: TensorFlow is an open-source project library from Google for machine learning
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