OLTP vs OLAP: 9 Critical Differences

on Data Driven, Data Driven Strategies, OLTP • June 14th, 2021 • Write for Hevo

Based on unique business cases, organizations require different types of data processing systems. Leveraging a standard processing system for reading, writing, or deleting cannot effectively serve every business-critical need. Depending on the demand, data-driven companies mostly use online transaction processing (OLTP) or online analytical processing (OLAP). However, both data processing systems are a part of the entire data movement within organizations.

In this article, you will be learning about the differences between OLTP and OLAP namely the 9 critical factors to help you decide from OLTP vs OLAP for your use case. This article covers everything you need to know for this, from the basics of OLTP and OLAP right down to the challenges of the two.

Table of Contents

Prerequisites

  • Understanding of relational databases.
  • Working knowledge of Data Warehouses.

Introduction to OLTP

OLTP Illustration
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OLTP is a Data Processing system that requires real-time executions of a plethora of transactions. This is leveraged mainly by e-commerce websites for reading, writing, or deleting data associated with purchases or ETL tools to extract information from Data Lakes. OLTP systems use relational databases to organize data into tables to support the recording of transactional information with speed.

Introduction to OLAP

OLAP is a Data Processing system leveraged to support multi-dimensional analysis at scale. This is used by Data Warehouses while transferring information to Data Analytics tools for gaining insights with complex analysis. OLAP systems use OLAP cube, a multi-dimensional array of data for different reporting needs.

The different types of OLAP are as follows:

  • Relational OLAP (ROLAP)
  • Multidimensional OLAP (MOLAP)
  • Hybrid Online Analytical Processing (HOLAP)
  • Desktop OLAP (DOLAP)
  • Web OLAP (WOLAP)
  • Mobile OLAP
  • Spatial OLAP

Understanding the Applications of OLTP and OLAP

OLTP and OLAP Differences
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Before digging into the critical differences that separate OLTP and OLAP it is a good idea to look into the applications of OLTP and OLAP to get a better picture of what you will be dealing with.

Applications of OLTP

OLTP supports broader use cases as it empowers organizations to process data within milliseconds. It is used for online ticket bookings, banking, e-commerce websites, fintech, and any other business where the daily frequency of transactions is in a range of a few hundred thousands or millions. Today, OLTP systems are used in almost every digital product to manage very large but short online transactions. 

For instance, if an e-commerce website receives several orders in seconds, the OLTP system has to modify or add information into the database. Some of the data could be order number, name of the purchaser, address, item name, and more. Such information is quickly updated in databases by associating with an account. Similarly, there are many modifications an OLTP system carries out within milliseconds while ensuring data integrity. Since tables in OLTP databases are normalized, it is vital to maintain consistency to eliminate discrepancies. Any delay in the process could hamper associated operations, resulting in a bitter customer experience.

Applications of OLAP

Over the last few years, OLAP systems have proliferated due to the adoption of Data Science practices within organizations. Since Data Analytics requires sophisticated processing of information, a totally different kind of database was required to handle requests from complex queries. As a result, Data Warehouses powered by OLAP systems were introduced to support analytics operations like Roll-Up, Drill-Down, Slice and Dice, and Pivot Tables.

Here are a few Analytics Operations of OLAP:

  • Roll-Up: Roll-up is used for reducing dimensions by aggregating related data into one variable. For instance, sales from different cities across the world can be grouped into a country.
Roll Up Method
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  • Drill-Down: Drill-Down is the opposite of Roll-Up, which segregates information to obtain granular insights of collected data. For example, the sales performance of a financial institution can be evaluated based on each quarter.
Drill Down Table
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  • Slice and Dice: Slice and Dice also assist in the granular understanding of data but with a different perspective. For instance, you can analyze sales of a company by different salespeople, which can then be further assessed based on the types of products they sold.
Slice Method
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Dice Method
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  • Pivot Table: Pivot Table is used for rotating the axis of data to summarize information and obtain an overall view of the relation between two variables. For example, sales of items can be segregated by salespeople or vice versa through pivoting.
Pivot Table
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OLAP systems are incorporated into Data Warehouses to allow grouping, aggregating, and joining of data effortlessly. With traditional relational databases, sophisticated analytics becomes sluggish as complicated data modelings are resource-intensive. But, with OLAP systems, data can be molded in several shapes that can accelerate analytics with Big Data.

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Understanding the Key Differences between OLTP and OLAP

The 9 critical differences that set OLTP and OLAP apart are as follows:

OLTP vs OLAP: Purpose

OLTP systems are leveraged to control and run essential business processes in real-time, but OLAP systems are for planning, decision making, problem discovery, and uncovering insights. Therefore, OLTP is considered for operational tasks while OLAP is used for informational use cases.

OLTP vs OLAP: Processing Needs

The most significant difference between OLTP and OLAP systems is that they are used for different processing needs. While the former is deployed for optimizing several simple transactions/updates, the latter is implemented for strenuous Data Analysis. As a result, their performance is evaluated across different metrics. While the performance of OLTP is evaluated based on the Transactional Throughput, OLAP’s performance is assessed based on the Query Throughput. 

OLTP vs OLAP: Schema

OLTP systems use schema associated with relational databases to expedite write, read, and delete processes. On the other hand, OLAP systems use a multi-dimensional schema for enabling several drill-downs for Data Analysis and Data Reporting.

OLTP vs OLAP: Processing Time

OLTP systems are deployed for critical business operations where different processes are dependent on how seamlessly modification or addition of data is carried out. Consequently, OLTP systems are a must when execution speed is of priority. But the implementation of OLAP systems is more focused on supporting a colossal amount of data for reading operations. Therefore, a delay in processing time is entirely normal. While OLTP systems are required to deliver results in milliseconds, OLAP can take up to a few hours to execute a single query.

OLTP vs OLAP: Readiness

With OLTP systems, 24/7/365 availability is essential because numerous transactions are recorded, deleted, or modified every millisecond. However, interactions with OLAP systems are inconsistent or less frequent, thereby, an absence of OLAP systems might not negatively impact business operations.

OLTP vs OLAP: Backup

As business operations are dependent on OLTP systems, it requires frequent backup to protect millions of transactional records in case of any breakdowns. But, OLAP systems can be occasionally backed up to avoid data loss.

OLTP vs OLAP: Transactions Status

Transactions in OLTP systems are binary in nature. Either a transaction — read, write or delete — is successful or a failure; there is no middle ground. Since OLTP systems cater to real-time requirements, a transaction on hold can slacken the entire operation. OLAP system’s transactions, on the other hand, in this case, read-only, can be delayed, especially while waiting for resource allocation.

OLTP vs OLAP: Data Size

Since speed is vital for OLTP systems, for every transaction, the size of data is small. This ensures operational speed for a superior user experience. But, OLAP deals with a colossal amount of data, even for a single information request. In addition, digital storage can be optimized in OLTP systems by compressing or archiving data, but with OLAP, storage requirements are massive.

OLTP vs OLAP: Data Modification

With OLTP systems, data modification is essential to support real-time operations, but OLAP systems do not update data when Data Analytics tools request complex queries, thereby limiting the risk of data loss.

The aforementioned criteria can provide you with a better idea about the topic OLTP vs OLAP. Designed for processing several transactions every millisecond, OLTP systems are used in almost every digital product. You would need OLTP for high-speed daily transactions in most business processes to effectively perform various digital operations.

But you only require OLAP if you have Big Data and want to implement Data Science practices for generating insights. If you are leveraging OLAP, you are very likely to use OLTP in tandem to handle the flow of data across multiple sources. As a result, you would end up mostly leveraging both systems for gaining a competitive advantage.

OLTP vs OLAP summary
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Understanding the Challenges of OLTP and OLAP

Even the smallest flaw in OLTP systems can completely disrupt the workflow or bring digital solutions to a standstill. Since the tables are normalized, digital processes are interlinked and rely on data integrity to gain desired results.

In other words, there is no room for inconsistencies. Any shortcomings while implementing OLTP systems can lead to permanent loss of data. However, OLAP systems do not usually have real-time requirements, and any issues can be resolved without major loss in revenue. Although less critical than OLTP, OLAP requires experts who can mold data to support organizations with Data Analytics workflows.

Conclusion

OLTP and OLAP are an essential part of modern-day business operations that allow companies to offer products and services at ease. Both systems complement each other as proper analytics can only be carried out because OLTP systems handle information effectively. OLTP and OLAP have their own advantages and optimize different Data Processing tasks in the digital world. In this blog, you learned about the basics of OLTP and OLAP. This included their primary features. This was followed by a deep dive into the applications of OLTP and OLAP and the critical differences between the two. The blog wraps up with the challenges faced by OLTP and OLAP, summarised below:

ParametersOLTPOLAP
ProcessOLTP is an online transactional system for database modification and is characterized by a large number of small online transactions.It is a process of online analysis and data retrieving and is characterized by working with large amount of data
Method and Functionality It uses traditional DBMS and is an online databse modifying systemOLAP uses data warehouse for online database query management
Query TypesInsert, Update, and Delete information from databasesSelect Operations
Table typeNormalizedNot normalized
Data SourcesOLTP and its transactions are the data sourcesThe different OLTP databases are the data sources for OLAP
Data Integrity ConcernsIt is mandatory for OLTP databases to maintain integrity constraintData integrity is not an issue as OLAP databases do not get frequently modified
Read/ Write OperationsAllows Read and Write operationsAllows Read operations and rarely allows write operations
Response TimeMillisecondsSeconds to minutes
Use-CasesHelps to control and run fundamental business tasksPlanning, problem-solving, and decision support
Query ComplexitySimple and standardizedComplex queries with aggregations
Data QualityDatabase is always detailed and organized.Data might not always be organized
Back-upComplete backup of the data combined with incremental backupsOLAP only need a backup from time to time. Backup is not important compared to OLTP
Database DesignDatabase Design is application oriented, i.e., it changes with industry like Retail, Airline, Banking, etc.DB design is subject oriented, i.e., it changes with subjects like sales, marketing, purchasing, etc.

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