As a company formed by data engineers and analysts, we at Panoply love data in all its forms! But when you’re working with different types of data storage and processing tools, it’s important to understand the constraints and performance abilities of each unique type. Not all data is created equal, and neither are all data storage types!
A database is a collection of information stored in an organized manner, and it usually contains data points from one application, program or platform. These data points can include things customer information, product information, inventory numbers, and sales transactions.
Data retention and purging is straightforward in databases. Information within a database is periodically indexed to make relevant information more accessible. When databases are loaded, they automatically include any changes since the most recent load - all new data points, updates, and deletions.
Management of a database is typically handled by a Database Administrator. However some databases provide atomicity, consistency, isolation, and durability (ACID), which helps ensure consistency and transactional completeness within the database.
The key difference between a database and a data warehouse is the data source. Databases typically use a single application, program or platform as the basis for its data. The database approach assumes all information you’d like to use for your analysis is contained within that single source.
Data warehouses, on the other hand, source their data from numerous sources and systems - included those not interrelated or unified by platform. Data warehouses collect and sort this disparate data, and then formulate returns based upon end user queries. Because a data warehouse contains data from multiple sources, the query returns it provides to the analyst can form a more 3-dimensional view of the data story.
Use cases for databases or data warehouse vary, but each has distinct features that could make one option or the other better suited for your organization’s needs.
- Database application relationships are one-to-one, which means data transfer is typically fast and concurrent, and data sets are detailed.
- Data warehouse application relationships are one-to-many, which means data presented is a summarized historical aggregate. While query returns are less detailed than databases, their variety of source materials means they can offer a more integrated view.
- Use cases that would be better served by databases include deep dives into detailed data sets on a narrow topic query. Use cases for data warehouses would highlight the interplay between unique data sources in a broadly-summarized historical context.
- Database data source(s) contain large amounts of minute data, and their processing rules, called Online Transactional Processing (OLTP) require it to update each time there’s a change, deletion, insertion or other modification. But the database’s deeply joined structure means all these changes can cause delays or result in less meaningful query returns.
- Data warehouses utilize Online Analytical Processing (OLAP), which, as the name implies, handles broader queries that cover a large spectrum of aggregated historical data sets. These diverse data sets mean data in a warehouse is normalized (kept in native data source format) until queried, leading to faster overall query times, multidimensional views, and summarized data returns.
- Frequent data changes and queries are best reserved for databases, because of the transaction processing operations. Where analysis is more important than transactions, utilizing a data warehouse would provide a macro view of the data story with diverse viewpoints.
- Uptime is a requirement for databases, because of their reliance on the OLTP’s performance to supply real-time data. Most databases come with a 99.99% uptime SLA.
- Downtime is a de facto requirement for data warehouses, who utilize the downtime breaks to accommodate the scale of the data they collect.
- Always-on or real-time data users would be better served utilizing a database for their analyses, with the understanding that the view would be limited. For contextual, historical, or similar analyses that incorporate multiple views (for instance, departmental overviews or performance evals that require input from multiple data sources), a data warehouse would be a better fit.
Creating a hybrid, layered approach to your data analysis needs could allow you to capitalize on the unique strengths of both the database and data warehouse storage types. For example, using a database for quick check-ins on transactional information while exporting historical data into a warehouse for big picture, longitudinal analysis could give you actionable insights while best utilizing the features of each storage type.