Data warehouses and databases both store and analyze data and support business operations. Many people fall into the trap of thinking these similarities make them interchangeable, but if you run a warehouse like a database (or a database like a warehouse), you’re asking for performance issues, unreliability, and frustration.

In this article, we break down data warehouses versus databases to show you how to get the most out of these systems and improve performance across your data ecosystem. 

What is a database?

A database is a highly structured repository of data. It is an operational system that serves as the backend of an application, allowing you to store and manage data, record transactions as they occur, and apply security and integrity standards. 

Depending on the type of database you use, it may support atomicity, consistency, isolation, durability (ACID) transactions; query languages to interact with content; and indexes to improve performance. Database management systems (DBMS) help you interact with information in your databases. 

If you need to manage highly structured data for processes like online transactions, databases are your go-to system.

Types of databases with examples

There are several types of databases, all optimized for different data models and usage patterns. Here are a few of the most common varieties, alongside examples.

Relational databases

These systems store data in structured tables using predefined data schemas and relationships. Most of the time, relational databases use SQL to query information and enforce consistency. Microsoft SQL Server and MySQL are two popular DBMS platforms for this kind of database.

Document databases

A subset of not only SQL (NoSQL) databases, document databases store data as JSON-like documents. Since they’re designed with flexibility in mind, these systems can be a great option if you’re dealing with semi-structured or unstructured data. Individual documents can have different structures, allowing your data to vary without following the strict schema used in relational databases. MongoDB and CouchDB are two common examples.

Key-value databases:

As the name suggests, key-value databases use a system of keys and values to store data. They’re most commonly used to improve performance and hasten lookups. Each unique key represents a different dataset, which could be anything from simple structured data to compound objects. DynamoDB and Redis both support key-value structures and are extremely scalable.

What is a data warehouse?

Data warehouses are centralized analytics systems that store enormous volumes of data from across entire businesses. By connecting to multiple sources like databases, SaaS applications, log systems, and file storage, they consolidate all your data in one place. 

When an analyst needs to query company information to support business decisions, a data warehouse is a central resource. Creating specialized data marts for specific teams and business units inside warehouses can also help support analytics.

To compare data warehousing versus database structures: the former are optimized for analytical workloads and complex queries, while the latter are designed for operations and real-time processing

Data warehouse examples

Here are a few popular data warehouses alongside their core features: 

• Amazon Redshift: A fully managed data warehouse tool that runs on the AWS cloud. Redshift is easy to integrate with data lakes and third-party sources for large-scale analytics.
• Google BigQuery: An automated data-to-AI platform that supports the entire data lifecycle. It offers serverless data warehousing for simple management. 
• IBM Db2 Warehouse: A low-cost, high-impact analytics platform for rapid querying and streamlined analytical workflows.

What is a data warehouse vs. a database? Key differences 

While they both store and engage with data, databases and warehouses have dramatically different use cases. To help you identify the differences, we’ve put together a table that focuses on the broad capabilities of relational databases versus data warehouses. 

The main points of difference between databases and data warehouses are:

Data lakes vs. data warehouses vs. databases

While data warehouses and databases cover a large number of business use cases, they’re not the only information architecture companies rely on: Data lakes (and lakehouses) also play an important role. Here’s a brief overview of three different systems and when to choose each one.

Data lake

A data lake is a low-cost method of storing large volumes of raw data in its original format. Instead of following the fixed schema of a database or fitting in the modeled tables of a warehouse, data lakes are a much more flexible method of handling data. 

They allow you to store semi-structured JSON logs, schema-enforced structured data, and unstructured content like images in one place. But their flexibility can also make them tough to govern. Without proper governance and modeling systems like open table formats and lake catalogs to help you find your content, they can become disordered and difficult to manage. 

Data lakes are ideal for storing massive amounts of data cheaply and anchoring a unified data architecture for analytics workloads. Make sure, however, that you are able to structure and govern the data through open table formats and catalogs.

Data warehouse

Data warehouses store structured, analytics-ready data that supports your business intelligence (BI) workloads. They enforce consistency and structure, helping your teams query information quickly and find historical reports to support their work. But if you want to add a new source to your warehouse manually or change its schema over time, you’ll likely struggle with its inflexible structure. 

Warehouses are tough to scale, which is why businesses often use data lakes to ingest instead. Alternatively, you could turn to Fivetran’s automated extract, transform, and load (ELT) pipelines, which come with over 700 pre-built connectors, giving you instant access to the sources you need.

Choose a data warehouse when you need fast access to curated data and want to support BI dashboards at scale.

Database

Databases are a small-scale storage option for operational workloads. While they can support analytics, this often leads to storage errors if they’re not paired with a warehouse. Databases are better at handling frequent reads and writes. Their low-latency, reliable storage architecture allows companies to log transactions like inventory updates, customer records, and user logins consistently.

Choose a database if you need a reliable record system for operational data with strong data integrity.

How Fivetran connects databases to data warehouses

If your business wants data-driven insights, it needs up-to-date, consistent, and accurate information. To do that, your databases connect to your centralized data warehouse or data lake, so you always have the data you need. 

Fivetran simplifies the process by fully automating the extract and load layers of data integration. Rather than having to write complex migration logic, Fivetran helps you capture updates in your databases and link them directly to your destinations.

The platform’s high-volume replication scales data movement and reduces the load on your databases. By optimizing how large tables are replicated, Fivetran improves pipeline performance and helps your applications scale with stability.

To learn how you can streamline your ELT workflow and support your data infrastructure, get started for free today.

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