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Introduction to NoSQL

BasicSkill Level
4.8+
732 reviews
Updated 12/2025
Conquer NoSQL and supercharge data workflows. Learn Snowflake to work with big data, Postgres JSON for handling document data, and Redis for key-value data.
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SQLData Engineering
4 hr
15 videos
59 Exercises
4,450 XP
9,684
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Course Description

In recent years, NoSQL databases have seen a surge in popularity due to their ability to handle large volumes of unstructured data. Learning to leverage NoSQL tools such as column-oriented, document, key-value, and graph databases allows data engineers, analysts, and scientists to interact with a wider breadth of data. These NoSQL databases bring additional functionality to data storage and retrieval that isn’t available in traditional relational databases.

Column-oriented databases

Dive into the world of column-oriented databases and their role in the evolution of data warehouses. Practice working with Snowflake to create and update column-oriented tables, as well as optimize table design for performance. Explore Snowflake’s advanced functionality to work with semi-structured data.

Document databases

Learn to use Postgres JSON as a tool to query document and object-oriented databases and the variety of data types that this technology supports. Hone your skills working with data stored in an array, as well as nested data.

Key-value and graph databases

Grow comfortable working with key-value databases and learn when this NoSQL data store is best applied. Practice using Redis to load and query data and experience the lightning-fast performance of in-memory databases. Explore graph databases and their use cases in data workflows.

Prerequisites

Database Design
1

Introduction to NoSQL databases

Explore the basics of NoSQL databases, and how they're used in a modern data architecture. Learn to identify the pros and cons of using column-oriented, document, key-value, and graph databases to make data available to downstream processes and data consumers.
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2

Column-oriented Databases

Dive into column-oriented databases with the help of Snowflake. Learn to populate Snowflake tables using a number of tools, and uncover the fundamentals of micro-partitioning and data clustering to optimize query performance with query pruning. Leverage common table expressions, materialized and non-materialized views to streamline analytics workflows. Step into semi-structured data with Snowflake's VARIANT types.
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3

Document Databases

Dive deep into the world of document data with Postgres JSON, and explore the intricacies of semi-structured data. Learn how to extract and transform top-level and nested document data using a number of built-in operators and functions.
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4

Key-value and Graph Databases

Take your NoSQL skills to the next level by mastering the fundamentals of key-value databases. Explore common use-cases for key-value data, and learn how to use Redis and Python to read and write data. Wrap up with a basic overview of graph databases, and their place in the modern data stack.
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Introduction to NoSQL
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FAQs

What NoSQL database types does this course cover?

You will work with column-oriented databases using Snowflake, document databases using Postgres JSON, key-value databases using Redis, and get an overview of graph databases.

Do I need SQL experience before taking this course?

Yes. Prerequisites include Introduction to SQL, Intermediate SQL, relational database design, and database design courses. You need solid SQL fundamentals before exploring NoSQL alternatives.

What hands-on Snowflake skills will I develop?

You will create and update column-oriented tables, optimize performance with micro-partitioning and data clustering, use materialized views, and handle semi-structured data with VARIANT types.

Does the course include working with Redis in Python?

Yes. Chapter 4 teaches you to use Redis with Python to read and write key-value data, experiencing the fast performance of in-memory databases firsthand.

How does the course handle document database queries?

You will use Postgres JSON operators and functions to extract and transform top-level and nested document data, learning to work with semi-structured data stored as JSON.

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