Working with Semi-structured Data with Snowflake

Snowflake offers full support for semi-structured data. This course will teach you how to apply schema on read, loading, and writing to semi-structured file formats, working with the variant data type to interpret semi-structured fields and more.

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Course Insight

Suitable for intermediate learners. Works well as a continuation after mastering Data Science fundamentals. It bridges the gap toward advanced, production-level engineering.

Intermediate FriendlySelf-Paced LearningProject-Based

SKILLS TO
MASTER

Analytics
Exploratory Data Analysis
ModelingTrending
Predictive Machine Learning
SQL Querying
Relational Data Management
Pandas
Matplotlib
Statistics
Tableau
ETL
Careers:Cloud Engineer, DevOps Engineer, Solutions Architect.

Quick Facts

Below sections are verified from last major sync. For real-time updates and today's latest lectures, Check official page here.

What You’ll Learn

  • Course Overview : 1min.
  • Reading and Writing Semi-structured Data : 36mins.
  • Querying Semi-structured Files : 28mins.
  • Working with Semi-structured Fields : 30mins.
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Description

The Snowflake Cloud Data Platform has full support for semi-structured data stored in formats such as JSON, XML, parquet, and more. In this course, Working with Semi-structured Data with Snowflake, you'll learn to load, write, and query these data formats that are very common in data engineering projects.

First, you'll explore Snowflake's supported semi-structured file formats and the powerful and flexible variant data type. Next, you'll discover how to load and write in popular formats such as JSON, parquet, and more. Finally, you'll learn how to use Snowflake's SQL implementation and built-in functions for querying semi-structured data.

When you're finished with this course, you'll have the skills and knowledge of working with semi-structured data to apply on your next data engineering project.

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Working with Semi-structured Data with Snowflake
4(15+ learners)