OLTP vs OLAP : Unveiling Crucial Data Processing Contrasts

OLAP (Online Analytical Processing) and OLTP (Online Transaction Processing) are two types of data processing systems used in data management.

OLAP is designed for complex queries that involve aggregating data from multiple sources and performing in-depth data analytics and business intelligence.

OLTP is used to manage transaction-oriented applications in real time. It drives applications in e-commerce, banking, and inventory management.

Many organizations combine OLAP and OLTP systems to create a high-performance hybrid database that provides real-time access to transaction data and enables complex analysis of historical data.

In this article, we’ll outline the comparison of OLTP vs OLAP systems, their key characteristics, & their use cases. We’ll define four crucial differences between the solutions & explain how a hybrid processing system is ideal for modern data teams.

What is OLTP?

OLTP stands for Online Transaction Processing. It is used to process high volumes of simple database transactions made by multiple users concurrently.

A database transaction is a change made within the database. It could be inserting, updating, or deleting data.

OLTP’s main purpose is to support an organization’s day-to-day operations by recording and processing business transactions as they occur. 

Examples of these transactions include bank withdrawals and deposits, online purchases, flight reservations, and hospital patient admissions.

OLTP systems are optimized for fast and reliable transaction data processing, with a focus on data integrity. The data is typically stored in a normalized form to minimize data redundancy.

Key Features

Let's look at the key characteristics of OLTP system:

• Real-time processing: OLTP systems process transactions in real time. This allows for immediate updates to the database as transactions occur and ensures that data is always current.
• Concurrent processing: An online transactional system must be able to handle many transactions, often made by multiple users simultaneously.
• Data consistency: OLTP solutions ensure the accuracy and consistency of data through techniques such as data validation, concurrency control, and transaction management.
• ACID properties: OLTP systems adhere to the ACID (Atomicity, Consistency, Isolation, and Durability) properties to maintain data integrity in real time.
• Simple transactions: OLTP transactions are typically simple and short-lived, involving small amounts of data. This allows for quick processing and reduces the risk of conflicts with other transactions.

What is OLAP?

Online Analytical Processing (OLAP) is a data processing system used to analyze large amounts of data from various perspectives. OLAP software is used in business intelligence and data analytics to support complex reporting, analysis, and decision-making processes.

An OLAP database uses a multidimensional data structure known as an OLAP cube to store and manage data. This provides a flexible and dynamic data view and enables analysis across different dimensions.

The main purpose of OLAP is to allow users to explore data more intuitively and interactively than traditional reporting tools. An OLAP database can support operations like pivot, slice and dice, and drill down to help data scientists visualize & analyze aggregated data.

OLAP tools are also optimized for read-intensive operations. They are built for fast data retrieval, so business analysts can efficiently run complex analytical queries on high data volumes.

Online processing systems support strategic planning, forecasting, and trend analysis.

Key Features

Let's look at the key characteristics of OLAP system:

• Multidimensional data model: OLAP systems use an OLAP cube to represent data in multiple dimensions. This data model enables analysis from different angles, leading to comprehensive data-backed conclusions.
• Complex queries: OLAP software is designed to deliver fast query performance, even when dealing with large volumes of data. This allows users to run complex queries quickly and efficiently, regardless of the query complexity involved.
• Data aggregation: OLAP cubes use aggregation to pre-calculate and store summary data. This allows for faster data retrieval and analysis of high data volumes.
• Analytical operations: OLAP solutions enable data teams to perform advanced analytical functions, like drill down, slice and dice, and pivot, to drive in-depth analysis and valuable insights.
• High data volume: OLAP databases use techniques like caching, indexing, and partitioning to optimize query performance on large volumes of data.

Key Differences: OLTP vs OLAP

Data engineers need OLTP and OLAP for two different purposes. OLTP to rapidly process database transactions and OLAP for in-depth data analytics.

OLAP databases are optimized for complex queries and data analysis, with a focus on read-intensive operations. While OLTP databases are designed for fast, transactional processing, handling high volumes of read and write operations in real time.

We’ve illustrated the critical differences between OLTP and OLAP using this table:

Let’s take a deeper look at four of the key differences.

1. Data Structure

OLTP

OLTP databases use a normalized data structure optimized for transactional processing. This database design involves a relational database that stores data in columns and rows to form a table.

Each database is a collection of tables organized around specific business processes, such as order processing, inventory management, or customer management.

Data teams use a relational database management system (RDBMS) to manage the data within an OLTP database.

OLAP

OLAP systems use a denormalized data structure optimized for analysis. 

In this structure, data is organized using an OLAP cube, a multidimensional array of data representing a specific business aspect, like sales or inventory. Dimensions and measures within the cube represent data attributes and values.

Data engineers use two schemas when data modeling for OLAP - the Star schema and the Snowflake schema. These schemas organize data into a central fact table containing the measures. This table is connected to dimension tables containing descriptive data.

The significant difference is that the dimension tables in the Snowflake schema are normalized, creating a more complex structure.

2. Query types

OLTP

The types of queries executed in an OLTP system are relatively simple and focused on retrieving individual records or a small number of related records.

 Supported query types are adding a new record, updating an existing record, deleting a record, and searching for records.

OLAP

The queries executed in an OLAP system are complex and focused on retrieving aggregated data from large volumes of data.

Standard OLAP query operations include roll-up, drill-down, slice and dice, pivot, and forecasting. 

3. Performance focus

OLTP

The performance focus of OLTP is on processing individual transactions with low latency and high concurrency.

Key performance metrics for an OLTP system include transaction throughput, response time, concurrency, and availability.

OLAP

The performance focus of OLAP is on efficient data retrieval and gaining fast, accurate insights into high data volumes.

Key performance metrics for OLAP solutions include query response time, data aggregation speed, query concurrency, and data accuracy.

4. Data processing

OLTP

OLTP data processing focuses on data integrity. OLTP transactions are processed sequentially. Each transaction is executed and committed to the database before the next transaction is processed. This ensures that all the data in the database is current.

OLTP systems use a client-server architecture, where a front-end application allows users to send requests to the back-end database server. The server processes the transaction and returns a response to the user.

OLAP

OLAP data processing focuses on data consolidation and aggregation. Data integration is used to extract data from sources and load it into a centralized data store, like a cloud data warehouse. This process often involves using an ETL pipeline to transform the data into a suitable format and store it in an OLAP cube to drive analytical operations.

Efficient OLAP systems also pre-aggregate data, which involves summarizing and storing data in advance at different levels of granularity to support faster query processing.

Popular Database systems

Let’s look at well-known database systems for OLTP and OLAP:

OLTP databases

• MySQL: MySQL is an open-source RDBMS that is used to handle structured data. It supports OLTP workloads and is scalable, reliable, and easy to use.
• PostgreSQL: PostgreSQL is a free, open-source RDBMS. It can handle OLTP workloads, complex data analytics, and geospatial applications. It is known for its stability and advanced features like JSON and XML data support.
• Microsoft SQL Server: SQL Server is a feature-filled database platform for OLTP, business intelligence, and analytics.
• Oracle Database: Oracle Database is an RDBMS that supports transaction processing, data warehousing, and mixed database workloads. It is widely used in enterprise applications.
• MongoDB: MongoDB is a NoSQL database used for OLTP workloads. It uses a document-oriented data model to enable faster data access and easier scalability compared to traditional relational databases.

OLAP databases & tools

• Microsoft SQL Server Analysis Services (SSAS): SSAS is a popular OLAP tool that supports complex data analysis, data mining, and reporting.
• Oracle Essbase: Essbase is a business analytics solution that enables advanced data analytics, forecasting, and modeling.
• Apache Kylin: Kylin is an open-source OLAP data warehouse for Big Data. It helps data teams perform analytical operations using Hadoop and Spark.
• Snowflake: Snowflake is a cloud-based data warehousing platform that offers a fully managed and scalable solution for OLAP workloads.
• ClickHouse: ClickHouse is an open-source columnar database management system designed to process SQL queries in real-time. 

Use cases

Here are four well-known use cases for OLTP and OLAP systems:

OLTP

• E-commerce systems: OLTP systems in e-commerce applications help manage online transactions like purchases, refunds, and order management. These systems must be highly available and able to process large numbers of transactions without delay.
• Banking and financial services: OLTP is used in the banking and financial services industry to manage transactions such as deposits, withdrawals, and fund transfers. These systems require high levels of security, compliance, and reliability.
• Reservation systems: OLTP solutions in the travel and tourism industry allow staff at airlines, hotels, popular tourist attractions, and more to track and manage reservations, bookings, and tickets.
• Customer relationship management (CRM): CRM tools often use OLTP to handle customer data, order information, and customer service requests. It also helps automate processes in sales and marketing.

OLAP

• Business intelligence: OLAP is commonly used in BI applications to support data analysis, reporting, and visualization. This software allows data analysts to explore and analyze data from different angles and gain insights into business performance.
• Sales and marketing analysis: OLAP solutions in sales and marketing analysis can help analyze sales data, customer behavior, campaign performance, trends, and marketing ROI. It can also be used to forecast future sales.
• Financial planning: OLAP supports financial analysis applications used for budgeting, forecasting, and financial modeling. Analysts can explore data at different levels of granularity to make informed decisions.
• Supply chain optimization: Supply chain analysis applications use OLAP to optimize inventory levels, reduce costs, and improve delivery times. Data scientists can analyze supply chain data by different dimensions, like product, supplier, warehouse, and transportation.

Hybrid Systems: Combining OLTP & OLAP

Many organizations use hybrid systems to combine the features of OLTP and OLAP. Here are some common approaches to integrating OLTP & OLAP:

• Using a data warehouse: In this approach, transactional data is extracted from OLTP systems and loaded into a data warehouse for analysis. The data warehouse can support OLAP queries and provide a single source of truth for analytical data.
• Using in-memory databases: In this method, OLTP and OLAP data is stored in an in-memory database for faster processing. The in-memory database can support transactional and analytical queries and provide real-time analytics capabilities.
• Using hybrid databases: A single database supports transactional and analytical queries by providing different access paths to the same data.
• Using data virtualization: Data is accessed from multiple sources (such as OLTP systems and data warehouses) using a virtualization layer that provides a unified data view. The virtualization layer enables OLAP capabilities on top of OLTP data without data duplication.

Benefits

Here are some benefits of using hybrid systems:

• Real-time data analysis: Hybrid systems allow OLTP systems to feed data directly into OLAP software, enabling businesses to make real-time data-backed decisions.
• Faster data processing: Hybrid solutions can boost processing performance by directing only transactional data to the OLTP system and analytical data to the OLAP solution. This reduces the load on the OLTP database.
• Improved scalability: Organizations can scale up their transactional and analytical capabilities separately based on their needs to avoid over-provisioning or under-provisioning.
• Better decision-making: By combining OLTP and OLAP, businesses can analyze transactional data in real-time to quickly identify trends and make informed decisions.
• Reduced costs: Hybrid systems remove the need for separate OLTP and OLAP solutions. This can reduce infrastructure costs and simplify IT operations.

Examples

Three standard examples of hybrid databases are:

• SAP HANA: SAP HANA is an in-memory database platform that provides both OLTP and OLAP capabilities in a single platform database. SAP HANA can support real-time processing and analytics. It can scale up or out to meet changing business requirements.
• Microsoft SQL Server with columnstore indexes: Columnstore indexes are a type of index in SQL Server designed to store and query large data warehousing fact tables. They store data in a columnar format, which allows for efficient compression and scanning of large data sets. It is suitable for both OLAP and OLTP workloads.
• CockroachDB: CockroachDB is a distributed, cloud SQL database that can provide real-time analytics on transactional data. It also supports multi-cloud deployment and horizontal scaling to boost availability and reliability.

Conclusion

OLAP and OLTP are two essential systems that drive different areas of business operations and analysis. While OLTP databases drive real-time transactional processing required for daily operations, OLAP drives data analytics and reporting that can improve these operations.

Understanding the distinct roles of OLTP and OLAP in data processing is vital for creating an effective data ecosystem that can help businesses and data professionals reach their goals.

Most organizations use both OLAP and OLTP solutions, but the extent to which they use each system largely depends on their specific use cases and data needs.

You can learn more about data processing and data engineering on our content hub.