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Data Analytics vs. Data Analysis
Quantitative vs. Qualitative Data
What is Behavioral Analytics?
Data Analytics vs. Business Analytics
Data Analytics vs. Data Science
The Difference Between Data Analytics and Statistics
The Difference Between Data Analytics and Data Visualization
Data Analytics Lifecycle
Data Analytics vs Business Intelligence
What is Descriptive Analytics?
What is Data Analytics?
What is Diagnostic Analytics?
Data Analytics Processes
A top-level guide to data lakes
Redshift vs Snowflake vs BigQuery: Choosing a Warehouse
Data Warehouse Architecture
What Is a Data Warehouse?
How to Create and Use Business Intelligence with a Data Warehouse
Best Practices for Accessing Your Data Warehouse
Data Warehouse Best Practices — preparing your data for peak performance
How do Data Warehouses Enhance Data Mining?
Data Warehouses versus Databases: What’s the Difference?
What are the Benefits of a Data Warehouse?
Key Concepts of a Data Warehouse
Data Warehouses versus Data Lakes
Data Warehouses versus Data Marts
Difference Between Big Data and Data Warehouses
How to Move Data in Data Warehouses
What Is Customer Data?
Customer Data Analytics
Customer Data Management
Collecting Customer Data
The Importance of First-Party Customer Data After iOS Updates
Types of Customer Data
What Is a Customer Data Platform?
What is an Identity Graph?
Customer Data Protection
A complete guide to first-party customer data
CDPs vs. DMPs
What is Identity Resolution?
What is Consent Management?
Data Access Control
Data Sharing and Third Parties
What is PII Masking and How Can You Use It?
Data Security Strategies
Data Security Technologies
Data Protection Security Controls
How to Manage Data Retention
How To Handle Your Company’s Sensitive Data
Data Security Best Practices For Companies
What is Persistent Data?
Google Analytics 4 and eCommerce Tracking
What Is Google Analytics 4 and Why Should You Migrate?
GA4 Migration Guide
GA4 vs. Universal Analytics
What are the New Features of Google Analytics 4 (GA4)?
Benefits and Limitations of Google Analytics 4 (GA4)
Understanding Google Analytics 4 Organization Hierarchy
Understanding Data Streams in Google Analytics 4
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Data Warehouses versus Databases: What’s the Difference?
Using the right data system can be a stumbling block between hitting it big with a super-app and slowly drowning in technical debt. Understanding the sometimes-subtle differences between types of data infrastructure can be challenging. This article will explain the differences and the best usages of a data warehouse as opposed to a database.
But first, if you'd prefer a quick summary, here's the difference between a database and a data warehouse.
- A database is built to service high-volume, small-cost transactions in an online ledger
- A data warehouse is built to combine many different data fields for the purposes of querying, displaying, modeling, or otherwise analyzing complex data layers
Essentially a database is like the in-stock inventory of a store. It is optimized for high speed transactions for all customers. The data warehouse on the other hand, is just that – a warehouse. It is optimized for behind the scenes logistics and serving the comparatively few users of an organization in performing their work.
Now, if you have 10 minutes to spare, let's go into more detail on these differences.
For those who don't have a clear sense of what a data warehouse is, why not start by reading our learning center article on the subject?
Diving deep on databases
“Database" can be an ambiguous catch-all, especially as it is used in casual language to describe any large data system. This article will use the term database to mean a relational database, or relational database management system (RDBMS). The RDBMS is a fundamental data system that gained widespread usage in the 1970s. Today, it’s folded into more complex variants: data warehouses, data lakes, data marts, etc.
Many people have at least some familiarity with SQL (Structured Query Language), the most common format for interacting with a RDBMS. SQL and its dialects are rigid languages that conform to the relational database model. This means that, regardless of which proprietary database system is used, a set of general rules characterizes the performance of the database. Some modern databases do eschew the limitations of SQL and the relational model on their database, but for the purposes of comparison to a data warehouse, they are similar to a RDBMS.
The focus of—and benchmark for—a good relational database is efficiency in reading and writing small transactions. Additionally, relational databases follow strict normalization rules that make certain guarantees about data cleanliness and help prevent bugs or anomalies. These criteria exist because relational databases are meant to be reliable online ledgers, recording line-by-line lists of information and supporting rapid access to that information. Financial exchanges, password hashes, and demographic data are all classic examples of where a record-keeping tool must be efficient, precise and responsive.
On the other hand, the rigidity of a database system suffers when it is used outside of its design space. The "relational" aspect of databases refers to the interrelation of tables—each table is its own two-dimensional data field. When a search query examines data related across multiple fields, expensive operations are needed to cross-reference the appropriate combination of data. This makes relational databases notoriously poor performers when under heavy query load. The inflexibility of databases also means that expanding to cover new categories of data can often require redesigning the database.
Overall, relational databases are well-developed data systems that have been optimized for decades to serve a specific niche. As this article looks at the differences between databases and data warehouses, it’s important to keep in mind that tools are functional or dysfunctional in relation to the problem to which they are applied, rather than in an absolute sense of performance.
Data warehouses versus databases: difference by design
When considering the specific design differences between a database and a data warehouse, it becomes clear that they each represent a specific and exclusive data usage pattern. When tradeoffs are made in deploying a warehouse rather than a database, they are generally mitigated by the context that would benefit from a data warehouse. In other words, databases and data warehouses are two different species, well developed to their own niches.
Most of the taxonomic differences between databases and data warehouses are best understood through the lens of their use cases. We’ll look at categorical differences illustrated by the major difference in design features between the two.
Data structure and normalization
Data normalization refers to the rigidity of relationships between data in a storage system. To prevent duplicate or anomalous data, rules of varying degrees of severity can be imposed on the data structure. The terms "normalization" or "denormalization" refer to increasing or decreasing the amount of normalization applied.
The main advantage of normalized data is that any given piece of information is only stored in one place. This avoids the risk of discrepancies, where data is updated in one place but not another. As a further benefit, because normalized data is not redundant, the system consumes less storage space.
However, normalizing data involves a tradeoff: the reliable data management comes at an intensive computational cost. A normalized database links rows of different tables, such that multiple table joins are required to marshal all the information connected to a particular data point. Therefore, correlating different information between rows takes exponentially more computational power as the database size or query complexity increases.
The resource costs imposed by normalization are a hindrance to query responsiveness. A database, designed to maintain sanitary records, cares about fringe data anomalies. A data warehouse, however, is interested in speed above relatively minor data inconsistencies. The downsides to denormalization—ballooning storage requirements and buggy alterations to the data itself—are less important to users of data warehouses, who are often acting on fixed historical data that does not see many write operations.
The specific design of a data warehouse is intended to make it outperform a database as a tool for analysis, but that doesn't mean that databases are useless for those same analysis tasks. Many SQL wizards are capable of engineering delicate nests of JOIN-GROUP BY-WHEREs that can produce similar charts and metrics to the user interface of a proprietary warehouse. After all, warehouses are only acting on data that is likely contained in existing databases—why add another layer of software when a clever engineer can do the job just as well?
The answer, of course, is that the clever engineer should be at lunch. Rather than consuming the labor of highly technical workers, users of a data warehouse can independently generate the insights they need from a friendly user interface or accessible software. A data warehouse is a layer on top of existing databases that increases an organization's access to data analytics while reducing the overhead on that analysis.
In addition to that, data warehouses tend to be separated from production databases, so that when these less technical users query datasets in the warehouse, there is no impact on the performance of the main production database.
When considering the difference between a database and data warehouse, the intended user base of each tool is also relevant to the throughput of the data system. Relational database management systems can typically handle dozens of transactions per second, a useful capability for tracking votes in a poll or checking the passwords of users logging into a server. These are use cases which are defined by a high volume of users making small updates or read operations.
Conversely, a data warehouse is used by a limited group of people to analyze large quantities of data. A warehouse is not usually exposed to the public, but used by scientists or analysts to model information and test hypotheses. In these situations there is a relatively low volume of users making comparatively infrequent demands of the data system—but each demand is computationally expensive.
Furthermore, traditional databases tend to suffer performance penalties when their tables have an excessively large number of columns. In data warehouses, tables with many dozens of columns and more can be handled without any issues.
As with other design distinctions between databases and data warehouses, the design tradeoffs align to clear categories—one for high-frequency, low-cost transactions, and another for low-volume, expensive queries.
Traditionally, changes to the state of a database are conducted by OLTP (online transaction processing) systems. These systems are designed to rapidly update data on the order of a single "transaction," for example changing account balances or creating a customer order. Their speed and specificity are useful in a database context, where many simultaneous users can trigger small, precise changes.
OLTP protocols are not well suited to a data warehouse. Warehouses must accommodate fewer users, make more complex transformations, and act over entire data series. Data warehouses therefore use an alternative system for processing data, termed OLAP (online analytical processing). OLAP systems draw from a series of OLTP-type databases to create a "cube" of multi-dimensional data. While database/OLTP systems act on data over columns and rows like a two-dimensional spreadsheet, an OLAP system allows the data warehouse to put many data dimensions together so that queries can compare arbitrary, complex conditions.
Data warehouses prefer data batch loading rather than individual INSERT statements. They often support ingestion of flat files like CSV, JSON, PARQUET, and more, directly from cloud storage. For example Google’s BigQuery supports loading data from Google Cloud Storage and Amazon Redshift can load directly from AWS S3 buckets.
When is a warehouse suitable?
We can see that databases and data warehouses have each evolved to perform in specific ecosystems. Most companies or projects are complex enough that they contain habitats suitable for both. Modern firms or projects use many types of online ledger suitable for relational databases, but equally, they require introspection over the data they are collecting—that type of analysis is the role of a warehouse. Recall, as well, that a data warehouse is built over existing data—data almost certainly represented in a relational database.
Although the two needs are clear (as is the tool to apply to each), the distinction in roles between a relational database and a data warehouse can be ambiguous. A team might skip building up a separate warehouse, opting to hack together analytics views over a database that is at low load, only to find that as their users scale up, their queries are too much extra burden and they lose observation of their data. Conversely, a marketing department with access to a data warehouse might start extracting data from their analytics views to spreadsheets or even their own database, creating a siloed data source away from the original databases used in their warehouse.
Although it’s impossible to look at any situation and be certain which services should be supported by a warehouse and which with a database, there are some signs that a warehouse would be a useful addition to existing relational database systems:
- Monolithic databases serving many teamsHaving multiple teams rely on the same database tables with their applications can cause long-term issues when the underlying data changes. If an organization fails to communicate limitations on data usage in such an environment, analysis projects that rely on specific database tables might become technically challenging or impossible. A data warehouse, however, enables teams to use their own databases while collecting all the relevant data centrally for analysis purposes.
- Highly professional demands on dataWhen presenting to investors or high-value clients, high performance and responsiveness can be valuable even if a database would be capable of handling the load using visualization/analysis tools.
- Experimental research or high-iteration analysisNot all data analysis is equal. Simple metrics and logging are well served by a relational database, but if analysis itself requires many quick iterations, a data warehouse might be better suited to reduce technical headaches.
Even in situations where a data warehouse might serve you better, existing databases can carry the load. That being said, just because a given task can be accomplished with a database, a data warehouse might very well serve your organization better when it comes to supporting data analysis or plug & play connections to various business intelligence and dashboarding tools.
Relational databases, warehouses, and more
In the end, when considering whether to implement a database or a data warehouse, context and technical limitations will be the primary measure of which system is appropriate for you. Hopefully this review of the theory behind each technology can help you make that decision. If you found this article helpful, you may also enjoy listening to this episode of The Data Stack Show or exploring more articles in our learning center: