Skip to main content

What is an OLTP database and how does it work?

If you have ever used an online banking app, booked a flight ticket, or placed an order on an e-commerce website, you have interacted with an OLTP database. An OLTP database is a type of database that handles online transaction processing (OLTP), which is a data processing category that deals with numerous transactions performed by many users.


In this post, we will explain what an OLTP database is, how it differs from an OLAP database, and what are the features and benefits of using an OLTP database.


What is online transaction processing (OLTP)?

Online transaction processing (OLTP) is a data processing category that deals with numerous transactions performed by many users. The OLTP system is an online database system that processes day-to-day queries that usually involve inserting, updating, and deleting data.


A transaction is a logical unit of work that consists of one or more operations on the database. For example, when you transfer money from your checking account to your savings account, the transaction involves two operations: debiting your checking account and crediting your savings account.


Transactions must follow the ACID properties, which are:


•  Atomicity: A transaction must either complete entirely or not at all. If any part of the transaction fails, the whole transaction is rolled back to its previous state.


•  Consistency: A transaction must leave the database in a consistent state, meaning that all the data integrity rules are enforced and no data is corrupted or lost.


•  Isolation: A transaction must not interfere with other concurrent transactions. Each transaction must execute as if it is the only one running on the system.


•  Durability: A transaction must be permanently recorded in the database once it is committed. Even if the system crashes or power fails, the effects of the transaction must not be lost.


OLTP systems are designed to handle high volumes of transactions with low latency and high concurrency. They are optimized for fast and efficient query processing and ensuring data integrity in multi-access environments. They typically use a relational database management system (RDBMS) as the underlying data store.


What is an OLTP database?

An OLTP database is a database that supports online transaction processing (OLTP). It is a centralized repository that stores the operational data of an organization, such as customer records, order details, inventory levels, etc.


An OLTP database has the following characteristics:


•  High normalization: The data in an OLTP database is organized into multiple tables with well-defined relationships and constraints. Normalization reduces data redundancy and inconsistency, and improves data integrity and performance.


•  Small transactions: The transactions in an OLTP database are short-lived and involve small amounts of data. They are usually simple CRUD (create, read, update, delete) operations that access one or a few records at a time.


•  Frequent updates: The data in an OLTP database is constantly updated by multiple users and applications. The updates are reflected in real-time and affect the current state of the system.


•  Large number of users: An OLTP database serves many concurrent users who perform transactions on the system. The system must be able to handle high throughput and scalability demands.


•  Query optimization: An OLTP database uses indexes, partitions, views, stored procedures, triggers, and other techniques to optimize query performance and response time. The queries are usually predefined and parameterized to avoid complex joins and aggregations.


What is the difference between an OLTP database and an OLAP database?

An OLTP database is not the only type of database that exists. Another common type of database is an OLAP database, which stands for online analytical processing. An OLAP database is a type of database that supports business intelligence (BI) and analytical applications.


The main difference between an OLTP database and an OLAP database is their purpose and functionality. An OLTP database handles day-to-day transactions of an organization, while an OLAP database supports business analyses, such as planning, budgeting, forecasting, data mining, etc.


What are the benefits of using an OLTP database?

Some of the benefits of using an OLTP database are:


•  Data accuracy: An OLTP database ensures that the data is accurate, consistent, and reliable. It enforces data integrity rules and constraints, and prevents data corruption and loss.


•  Data availability: An OLTP database provides real-time access to the data for multiple users and applications. It supports high availability and fault tolerance features, such as replication, backup, recovery, etc.


•  Data security: An OLTP database protects the data from unauthorized access and modification. It supports data encryption, authentication, authorization, auditing, and compliance features.


•  Data efficiency: An OLTP database improves the efficiency and productivity of the business processes and operations. It enables faster and smoother transactions, better customer service, and more informed decisions.


How to get started with an OLTP database?

If you are interested in building and deploying an OLTP database in the cloud, you can use Azure as your platform. Azure offers a range of services and tools that can help you with every aspect of your OLTP database project, from data ingestion and storage to data processing and analytics.


Some of the Azure services and tools that you can use for your OLTP database are:


•  Azure SQL Database: A fully managed relational database service that supports online transaction processing (OLTP) workloads. You can use Azure SQL Database to store and query structured and semi-structured data using standard SQL.


•  Azure Cosmos DB: A fully managed NoSQL database service that supports online transaction processing (OLTP) workloads. You can use Azure Cosmos DB to store and query schemaless data using various APIs, such as SQL, MongoDB, Cassandra, Gremlin, etc.


•  Azure Database for MySQL: A fully managed relational database service that supports online transaction processing (OLTP) workloads. You can use Azure Database for MySQL to store and query structured data using MySQL.


•  Azure Database for PostgreSQL: A fully managed relational database service that supports online transaction processing (OLTP) workloads. You can use Azure Database for PostgreSQL to store and query structured data using PostgreSQL.


•  Azure Synapse Analytics: A fully managed analytics service that supports online analytical processing (OLAP) workloads. You can use Azure Synapse Analytics to analyze your data from your OLTP databases using SQL or Spark.

I hope this post has given you a clear overview of what an OLTP database is and how it works. If you have any questions or feedback, please leave a comment below

Labels:

Comments

Post a Comment

Popular posts from this blog

Unlocking South America's Data Potential: Trends, Challenges, and Strategic Opportunities for 2025

  Introduction South America is entering a pivotal phase in its digital and economic transformation. With countries like Brazil, Mexico, and Argentina investing heavily in data infrastructure, analytics, and digital governance, the region presents both challenges and opportunities for professionals working in Business Intelligence (BI), Data Analysis, and IT Project Management. This post explores the key data trends shaping South America in 2025, backed by insights from the World Bank, OECD, and Statista. It’s designed for analysts, project managers, and decision-makers who want to understand the region’s evolving landscape and how to position themselves for impact. 1. Economic Outlook: A Region in Transition According to the World Bank’s Global Economic Prospects 2025 , Latin America is expected to experience slower growth compared to global averages, with GDP expansion constrained by trade tensions and policy uncertainty. Brazil and Mexico remain the largest economies, with proj...
Post a Comment
Continue reading

“Alive and Dead?”

 Schrödinger’s Cat, Quantum Superposition, and the Measurement Problem 1. A Thought-Experiment with Nine Lives In 1935, Austrian physicist Erwin Schrödinger devised a theatrical setup to spotlight how bizarre quantum rules look when scaled up to everyday objects[ 1 ]. A sealed steel box contains: a single radioactive atom with a 50 % chance to decay in one hour, a Geiger counter wired to a hammer, a vial of lethal cyanide, an unsuspecting cat. If the atom decays, the counter trips, the hammer smashes the vial, and the cat dies; if not, the cat survives. Quantum mechanics says the atom is in a superposition of “decayed” and “not-decayed,” so—by entanglement—the whole apparatus, cat included, must be in a superposition of ‘alive’ and ‘dead’ until an observer opens the box[ 1 ][ 2 ]. Schrödinger wasn’t condemning tabbies; he was mocking the idea that microscopic indeterminacy automatically balloons into macroscopic absurdity. 2. Superposition 101 The principle: if a quantum syste...
Post a Comment
Continue reading

5 Essential Power BI Dashboards Every Data Analyst Should Know

In today’s data-driven world, Power BI has become one of the most powerful tools for data analysts and business intelligence professionals. Here are five essential Power BI dashboards every data analyst should know how to build and interpret. ## 1. Sales Dashboard Track sales performance in real-time, including: - Revenue by region - Monthly trends - Year-over-year comparison 💡 Use case: Sales teams, area managers --- ## 2. Marketing Dashboard Monitor marketing campaign effectiveness with: - Cost per click (CPC) - Conversion rate - Traffic sources 💡 Use case: Digital marketing teams --- ## 3. Human Resources (HR) Dashboard Get insights into: - Absenteeism rate - Average employee age - Department-level performance 💡 Use case: HR departments, business partners --- ## 4. Financial Dashboard Keep financial KPIs under control: - Gross operating margin (EBITDA) - Monthly cash inflow/outflow - Profitability ratios 💡 Use case: Finance and accounting teams --- ## 5. Customer Dashboard Segme...
Post a Comment
Continue reading