Read the complete data warehouse vs database comparison.
The world of big data is getting bigger every day.
As the volume of data increases exponentially, businesses of all sizes try to capture raw data, process it, and extract insights for competitive decision-making.
The end-to-end operation of extracting value from data is called the ETL process. It stands for:
A crucial component of the ETL process is the data storage aspect.
The two main contentious architectures for storage solutions are databases and data warehouses.
But how do they differ? And which one should you chose to get a competitive data edge?
In this article, we answer these questions and more as we dig into the comparison of databases versus data warehouses.
A database is an organized set of information.
The schema under which the data is organized is tabular (read: looks like Excel Spreadsheet tables) with relationships between different tables.
The predominant solution for databases is SQL or relational databases, such as MySQL, PostgreSQL, Oracle, Microsoft SQL Server, and IBM Db2.
But there are also NoSQL databases that better handle unstructured or semi-structured data such as JSON and XML files, that do not easily comply with the relational tabular schema.
The main focus of databases is to record and provide data for single applications.
A data warehouse stores data similar to a database.
But unlike a database, a data warehouse acts as a single repository for multiple data sources.
The primary focus of a data warehouse is to provide insights via business intelligence.
Notable examples of data warehouses include Amazon Redshift, Snowflake, and Google BigQuery.
(Dig deeper and read more about data warehouses here)
The two data storage solutions seem similar at first glance. But there are 5 crucial differences between them.
OLTP stands for online transaction processing. It is a type of processing that allows specific operations to execute concurrently in real-time and therefore commit changes to a data storage faster.
OLAP, on the other hand, stands for online analytical processing. Instead of writing changes to data storage, OLAP operations read data from data storage and perform complex sorting, filtering, and aggregating operations needed to reshape data.
OLTP is the main processing type in databases. Databases are concerned and optimized for CRUD (Create, Read, Update, Delete) process transactions that take small amounts of data and process those faster. A typical OLTP process would be to insert a new data record into the database when someone purchased on your online store (e.g. CREATE a new row in the orders table).
In contrast, data warehouses are optimized for OLAP queries. OLAP queries are complex queries that transform data into a shape used for data analysis. A typical data warehousing task would be to aggregate and count all the orders in the last month by different sales regions.
Databases usually collect information from a single data source. For example, you deploy MySQL in production to collect events from your web app.
On the other hand, data warehouses usually collect information from multiple different sources. For instance, you would load data from your web app into your data warehouse, alongside Facebook Ads data exports and your email marketing data.
The data warehouse architecture is divided into three areas to optimize for multiple sources:
Databases operate on shorter timelines - they both persist data for shorter periods and keep data in memory for less time. A typical database might only keep records for the last 3 months and delete from the disk anything older to release valuable resources for new incoming transactions.
In contrast, data warehouses persist data for longer. Historical data is valuable for multiple analyses such as determining trends in data over time.
Data warehouses are designed to handle large amounts of data. Databases operate with smaller data volumes and can be compromised by a sudden surge in data ingestion.
Databases design the data model with normalization. Any data redundancy is removed by splitting data into small, narrow tables. Normalization allows better transaction processing.
Data warehouses, on the other hand, operate with large, wide, denormalized tables. Their data modeling involves the star schema or snowflake schema that is optimized for analytical queries instead of transactions.
The majority of SQL databases are ACID-compliant. This means each transaction will carry out under a set of expected behaviors that preserve data integrity even if the database fails.
In contrast, data warehouses carry more about data analytics than data accuracy. A lot of data warehouses do not offer ACID-compliant operations but are instead optimized for delivering fast analytics, even at the expense of some accuracy.
The biggest players in the field - Amazon Redshift, Snowflake, and Google BigQuery - are ACID-compliant.
In a punchline, both solutions are used for data storage, but databases are optimized for transactions while data warehouses are optimized for data analytics.
We can visualize the step by step comparison:
Yes, of course, you can!
PostgreSQL and other relational database management systems have devoted functions to facilitate analytics, such as statistical functions or devoted SQL commands like ROLLUP for building OLAP cubes.
But just because you can it does not mean you should.
PostgreSQL is optimized for OLTP processes, not OLAP processes.
So running a heavy OLAP workload on your database might take resources away from your transactional abilities and interfere with the normal CRUD operations.
Concretely - if you ran an analytical job to calculate a complex query, that query could hog all resources and make PostgreSQL unavailable for mission-critical operations.
The ultimate choice will depend on your business needs - if you need operational security, opt for a database.
If your needs are analytical and you deal with low data volumes, a good SQL database management system might suffice.
But if you operate with large volumes, or foresee those volumes increasing in the future, make sure to choose a data warehouse.
Keboola allows you to choose the best of both worlds.
As an end-to-end data integration platform, Keboola automates the ETL process with more than 250 components.
With its components, Keboola automatically extracts, transforms, and loads data to automate your ETL processes and save you valuable engineering hours.
You can save your data to the database or data warehouse of your choice - Keboola supports all the best-in-class data storage solutions. Explore more than 250 integrations Keboola has to offer and check for yourself how Keboola can unlock the power of modern data storage.
Try it out. Keboola offers a no-questions-asked, always-free tier, so you can play around and build your pipelines leading to the database or data warehouse with a couple of clicks.
