Data Gibberish

Data Gibberish

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Data Gibberish
Data Gibberish
The Ultimate Database Guide: Choosing Relational, Document, or Time Series to Drive Success
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The Ultimate Database Guide: Choosing Relational, Document, or Time Series to Drive Success

Explore the strengths and use cases of relational, document, and time series databases—and learn how to select the best option to power your project's success.

Yordan Ivanov's avatar
Yordan Ivanov
Oct 30, 2024
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Data Gibberish
Data Gibberish
The Ultimate Database Guide: Choosing Relational, Document, or Time Series to Drive Success
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Last week, I promised you to discuss how you can leverage AI to speed up your coding. This turned into a series. I need one more week to polish that. Today, we have something else from my pipeline.

A female data engineer thinkering on a database server

Greetings, curious reader,

Picking the right database can make or break a project. Especially when dealing with analytics. You've got plenty of options—relational databases, document stores, time series databases, data lakes, and data warehouses.

Each one has its strengths, and each has its limits. Choosing wisely can mean faster queries, lower costs, and a solution that scales with you instead of holding you back.

In this guide, I will walk you through 5 database types, how they structure data, and where they shine or fall flat. By the end, you'll have a solid idea of which fits your needs best—and why.

Also, check at the end of the article. I created an interactive questionnaire to help you pick the best solution for your next project.

Reading time: 8 minutes

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Option #1: Relational Databases

Note: Here, I mean traditional OLTP databases like MySQL and PostgreSQL.

Relational databases organise data in tables with rows and columns, where each table has a fixed schema. Tables can reference each other through primary and foreign keys, letting you define relationships between data entities.

This structure works best for data with clear relationships. That approach is reliable for applications where data consistency matters. Plus, relational databases support SQL, which is powerful and familiar to most people.

An-ecommerce database structure; customers places orders; orders contain order items; order items include products
A sample e-commerce database. Beware! I ask job candidates to design this on interviews.

Imagine an e-commerce system. You'd have tables for customers, orders, and products. Each table has a specific schema. You can link them up through relationships. Here's a simple layout:

Story: Why I Chose PostgreSQL Over a Data Warehouse

Here's a real-world example: I worked on a project where the stakeholders thought they needed a full-blown data warehouse. Their research showed that big companies use data lakes or warehouses. They were convinced this was the way to go.

They only had a few gigabytes of data and no plans to grow too fast. So, I convinced them to go with PostgreSQL.

My solution proved to be cost-effective, easy to manage and performed great. Plus, the team already knew SQL, so they didn't have to learn new tools. This setup has been serving them well for years with no problems.

Why Relational Databases Are Good for Structured Data

  • Data Integrity: Primary and foreign keys keep relationships in check so your data stays clean.

  • Complex Queries: SQL lets you write powerful queries with joins, aggregations, and more.

  • ACID Compliance: Transactions are reliable, which is essential for data accuracy.

Ideal Use Cases

Relational databases are best for applications with structured data and clear relationships, like e-commerce, financial systems, or customer management. PostgreSQL is a fantastic choice because it's solid, open-source, and supports advanced SQL.

Do you want to learn data engineering quickly? Subscribe today and learn how to build an entire data engineering project for free. It only takes 3 minutes a day!

Option #2: Document Databases

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