In the age of Big Data, no organization can do without a database. These structured datasets facilitate the analysis, processing and electronic retrieval of information. Depending on their objectives, companies can use relational, noSQL, hierarchical, object-oriented, distributed or transactional database models. Let’s take a look at the latter.
What is a transactional database?
Definition
A transactional database stores information from transactions, which are processes during which documents or data are recorded, modified, or controlled within a system.
These transactions are performed independently to ensure the availability, reliability, and consistency of other data. To ensure this independence, the database management system (DBMS) is represented in a row-based storage format (rather than column-based), allowing individual data rows to be written.
Some of the most popular transactional databases include Oracle, MySQL, Microsoft Access, and SQLite.
It’s important to note that not all transactions are recorded in the database (DB). They must be executed correctly to be validated because the transactional database model relies on the ACID principles.
A noteworthy point: Transactional databases should not be confused with relational databases.
While these terms are not mutually exclusive, the former refers to data manipulation events, while the latter refers to data organization.
Transactional database architecture
Transactional databases are defined by certain properties known as ACID:
- 1. Atomicity: Transactional databases are considered as a whole. If even a tiny part of a transaction fails, the entire transaction is impacted. If it is not executed correctly, the transaction is canceled.
- 2. Consistency: Transactions are either written to the transactional database or canceled. There is no intermediate state.
- 3. Isolation: Uncompleted transactions cannot be processed by other transactions.
- 4. Durability: Once it is written to the database, the transaction remains there.
These different principles ensure the integrity of the data writes in the database.
For whom and why use the transactional database model?
While the transactional database model is primarily used for production systems (i.e. transaction processing), it can also be used for data analysis. Among other things, this enables us to understand peaks in transaction volume, peaks in data ingestion and peaks in data arrival.
Thanks to their usefulness and reliability, the transactional database model is used by a multitude of organizations: banks, e-commerce sites, local authorities, social networks, and so on.
In the financial sector, for example, it provides information on insurance costs, claims frequency, and the number of bank deposits and withdrawals. In the logistics sector, transactional data provides information on the status of shipments, the identity of the shipper, and so on.
What are the disadvantages of transactional databases?
Despite all these advantages, transactional database models have a number of weaknesses:
- Data retrieval: Transactional databases have a temporal and spatial aspect. Over time, data becomes less comprehensible. Users may find it difficult to find the right information.
- Higher costs: Because of their performance and the complexity of their technology, transactional databases are often expensive solutions.
- Complexity: Transactional databases are particularly difficult to master, as they are capable of responding to a wide range of problems. For this, specialized data training is essential.
- Inability to write data: It’s not always possible to write new data to duplicate databases. These databases are often primarily used for research purposes.
- Limited analysis: as we saw earlier, transactional databases are primarily designed for production systems. They can also be used for analytics, but this is not their primary vocation. As a result, data experts wishing to use these databases for analysis may be limited in their approach. For this reason, it’s best to define a specific configuration in advance.
How to optimize your transactional database?
Creating an index
It is advisable to create an index to reduce data retrieval time. Indexing helps organize data by defining a structured data architecture. As information becomes easier to find, the performance of the transactional database improves.
Managing data memory
While transactional databases can handle large volumes of data, it is essential to manage memory effectively. In practice, this involves defragmenting the disk to consolidate real data, which leads to much faster query responses.
Separate analysis and production
Since the transactional database model serves both production and analysis purposes, it is advisable to duplicate the original model for conducting analyses. This helps prevent potential conflicts between analytical queries and strategic production queries if they are executed simultaneously.
Now that you know all about the transactional database model, you’ll probably want to know how to use it to derive relevant information. We invite you to take a data analyst training course.
