Data Warehouse vs. Database: Key Similarities and Differences
If your work involves collecting marketing data, you might have heard the terms “data warehouse,” “database,” and “data lake.” Do data engineers use these words to describe one and the same thing?
But then, what are the differences, and which one should you choose to store your company’s or client’s data?
Table of Contents
What is a data warehouse?
A data warehouse is a data storage system that can hold highly structured data from various sources. These storage systems can hold both current and historical data from one or more systems and are often used to combine different data sources in order to analyze the data, discover insights, and create business intelligence from data reports and dashboards.
Data warehouses can store a range of data, from raw ingested data to highly curated, cleansed, filtered, and aggregated data.
But how does one store data from disparate sources in a data warehouse? What ensures data compatibility?
The answer lies in the ETL process. The ETL (extract, transform, load) process extracts the data from connected sources, transforms the data through a series of rules and operations to unify it, and loads it to a data warehouse on a regular schedule.
Depending on the ETL tool you use, you can set daily or hourly data transfer, which ultimately means that data in your data warehouse might not always be 100% up-to-date.
Data warehouses usually have a pre-defined and fixed relational schema — or arrangement of different data items. This ensures smooth operation with structured data, while some warehouses also support semi-structured data.
Once you move your data to a data warehouse, you can connect it with a BI tool to explore the data, look for insights, and create reports for stakeholders.
Why do I need a data warehouse?
A data warehouse helps you advance your data operations in several ways.
First, a data warehouse is a great option when you have large amounts of historical data from different sources and need to perform in-depth analysis. Since data is highly structured, data analysis in data warehouses is relatively simple and straightforward, so you don’t even need a data scientist to derive insights.
Another benefit of data warehousing is that you can protect the data from shifting policies of individual platforms, changing rules, or discontinued services.
For example, as of June 2023, the Google Analytics platform will stop collecting data, giving users six months to migrate their data to another platform, after which the historical data will be gone forever.
This “GA sunset” prompted many marketing agencies to start moving their Google Analytics data to data warehouses to keep it safe before they decide whether they’ll continue with the next-generation Google Analytics 4 or a paid 360 version.
Popular data warehouses include:
- Amazon Redshift.
- Google BigQuery.
- IBM Db2 Warehouse.
- Microsoft Azure Synapse.
- Oracle Autonomous Data Warehouse.
Another thing to keep in mind is that you can’t use a data warehouse to support the transactions and computation of your application.
Your business might benefit from a data warehouse, but you still need separate databases to support your daily operations.
What is a database?
A database is a collection of data or information that is typically accessed electronically and used to support Online Transaction Processing (OTLP) and Online Analytical Processing (OLAP). Data is stored in a database via a Database Management System (DBMS), which allows multiple users and apps to interact with the data.
A variety of database types have been developed over the last several decades, all of which store information but have their own characteristics.
Relational databases store data in tables with fixed rows and columns
Over time, databases have evolved different features that make them optimized for different use cases. These include:
- Security features that ensure only authorized users can access data.
- Full-text search
- Optimization for mobile devices
- On-premises, private cloud, public cloud, hybrid cloud, or multi-cloud hosting
Data warehouse vs. database vs. data lake
As we explained the difference between databases and data warehouses, we should mention data lakes and how they fit into data management operations.
Data lakes are a cost-effective way of storing huge amounts of unstructured data. The main difference between data warehouses and data lakes is that you can use a data lake when you need to gain insights from your current and historical data in its raw form without having to transform it.
Data lakes also support machine learning and predictive analysis, and since they operate with raw data, they are often used by data engineers who like to prepare data before running complex queries.
Examples of data lakes include:
- AWS S3
- Azure Data Lake Storage Gen2
- Google Cloud Storage
Now, let’s round up the key differences between databases, data warehouses, and data lakes.
- Database — Stores current data needed to power an application, website, etc.
- Data warehouse — Stores current and historical data from one or more systems in a predefined and fixed schema, which allows business users to emails analyze the data.
- Data lake — Stores current and historical data from one or more systems in its raw form, which allows data scientists to analyze data before it’s transformed.
Data warehouse vs. database — Key differences and similarities
Let’s now take a closer look at the key differences and similarities between data warehouses and databases.
|Reporting & Analysis|
Every interactive application needs a database. There’s no going around it.
On the other hand, if you want to analyze data from multiple sources, you may complement your database with a data warehouse or data lake.
But which one of those should I pick?
To answer this, you need to consider the following questions:
Is your data structured, semi-structured, or unstructured?
Data warehouses support structured and semi-structured data, while data lakes support all three types of data.
Does your analysis benefit from having a pre-defined, fixed schema?
With data warehousing, you need to create a pre-defined, fixed schema upfront. With a data lake, you can store data in the original raw format, which doesn’t require you to create and maintain the structure.
However, if you use a fully-managed cloud-based data warehouse such as Google BigQuery, and an ETL tool like Whatagraph, you don’t have to worry about either the management or creating schemas. You can complete each transfer with a couple of mouse clicks.
Where is your data currently stored?
With a data warehouse, you need to create ETL processes to move your data into the warehouse. Depending on where your data is stored, a data lake might not require any data to be moved. Some data lakes can readily access data stored in popular cloud storage services like Amazon S3, cloud contact centers or other cloud applications which can be a huge perk for companies that already store their data there.
Databases and data warehouses might use the same type of data, but this is where the similarities pretty much end.
With the growing number of business applications that collect all sorts of data, data warehouses have become a valuable data integration asset for organizations that want to draw deep insights from their data as well as keep it safe from vendor-related events and restrictions.
Luckily, nowadays, you don’t need a data engineer to create a schema for every data transfer.
You can use Whatagraph to move data from different sources to Google BigQuery with zero coding and no tech knowledge.
Whatagrah is an ETL tool that allows you to load data from popular marketing platforms to a fully-managed data warehouse in just four steps:
- Connect the destination
- Choose the integration
- Set the schema
- Schedule the transfer
Whatagraph effectively eliminates manual and developer work from your data transfers. You only need to schedule the data flow and put the whole process on autopilot.
If you have vast amounts of valuable data that lives on different platforms, why not take full ownership of it?
Use Whatgraph’s data transfer to replicate the data in BigQuery and start reporting on actionable insights using Whatagraph’s native data visualization.
Start a free trial and prepare to see the whole picture of your data assets.
Published on Apr 25 2023
WRITTEN BYNikola Gemes
Nikola is a content marketer at Whatagraph with extensive writing experience in SaaS and tech niches. With a background in content management apps and composable architectures, it's his job to educate readers about the latest developments in the world of marketing data, data warehousing, headless architectures, and federated content platforms.
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