Overview

Data tools are the practical technologies used to store, process, and manage data in software systems. This section covers databases, query languages, processing frameworks, and pipeline tools.

What You’ll Learn

• Relational Databases - PostgreSQL, MySQL, and SQL fundamentals
• NoSQL Databases - MongoDB, Redis, Cassandra, and document/key-value stores
• Query Languages - SQL, GraphQL, and query optimization
• Data Processing - Apache Spark, Kafka, stream processing
• ETL Pipelines - Extract, transform, load workflows and tools
• Data Warehousing - Snowflake, BigQuery, data lake architectures

Learning Approach

The content is organized following the Diátaxis framework:

• Tutorials - Step-by-step guides to install, configure, and use data tools
• How-to Guides - Practical recipes for common data tool tasks
• Explanation - Understanding tool architectures, trade-offs, and when to use each
• Reference - Command references, configuration options, and tool comparisons

Start with tutorials to gain hands-on experience, or consult how-to guides for specific problems you’re trying to solve.