OLTP vs OLAP: What Are The Differences?
Data is significant in our world today. High-growth organizations are reaping the benefits of data-driven decisions. Just look around and you'll not fail to see that almost any top company you come across is highly dependent on data. Almost every dope product out here is heavily reliant on data, constantly changing for the better and each change driven by data. At the heart of this enormous data utilization is data processing, one of the core ingredients that helps organizations quickly make sense of data and use it to accomplish the kind of swift progress that we are witnessing. Online data processing, in particular, is critical for any business that depends on real-time operations. The inputting, manipulating, and outputting of data must be efficient and in most cases demands that the output data is produced immediately after the input data has been entered. Online data processing plays a critical role across areas such as online shopping and online banking. In some cases, online data processing is also used to process offline data, such as when a company processes payroll information. Robust data processing requires powerful systems, and luckily we have super systems in the market. OLTP and OLAP are the two major system types that are unfortunately often mistaken to be the same thing altogether. Let's put OLTP vs OLAP into perspective and help you make the distinction, especially regarding where to use either.
What is OLTP?
OLTP stands for Online Transaction Processing. In a nutshell, it is a system that processes transactions (inserts, updates, deletes) on data. This system enables several people to execute multiple database transactions in real time over the internet. An OLTP system gathers and stores transactional data in a database. OLTP databases contain multiple records and fields that maintain each transaction. These types of processing systems have numerous uses in our daily financial and non-financial transactions, including hotel reservations, in-store purchases, ATMs, text messaging, and password changes.
An example of OLTP at work would be a banking system where you are transferring money from one account to another. The source account would have money subtracted (a debit transaction), and the destination account would have money added (a credit transaction). The OLTP system would then take care of making sure both of those changes are made to the relevant records in the database.
While OLTP systems are typically based on Relational Database Management Systems (RDBMS), which use the Structured Query Language (SQL) to interact with the data, there are also NoSQL solutions such as a graph database that can be used for OLTP as well. These systems use a different paradigm than RDBMS, but they can still be used to process online transactions.
In general, OLTP systems need to be able to support a high volume of concurrent transactions with low latency. This means that changes need to be made to the data quickly and efficiently in order to keep up with the demands of users. A good example is the use of graph databases for real-time recommendations where things can change very fast. Consequently, OLTP systems need to be designed carefully in order to meet these performance requirements.
What is OLAP?
Online analytical processing, or OLAP, is an approach to answering multi-dimensional queries of data. OLAP systems perform multidimensional data analysis on massive amounts of data. This system uses data from a centralized data store, a data mart, or an OLAP data warehouse. An OLAP cube is the most vital component of OLAP systems because it allows users to run queries, analyze and report on multidimensional data.
The OLAP cube also extends the format of the databases by adding data dimensions and layers. This allows OLAP to perform complex queries that involve multiple columns of data. Decision makers and data analysts can use OLAP to create custom reports that process the data into useful information for decision-making. OLAP systems have multiple applications in business intelligence, data mining, and complex data analysis. They are also used in business reporting functions, such as sales forecasting, financial analysis, and budgeting. For example, a sales manager could examine previous years' sales totals by month, quarter and region, looking for trends that might indicate changes in customer behavior.
Data in OLAP databases are organized into dimensions and measures. Dimensions are the attributes of data, such as time, location or product type. Measures are the facts about those attributes, such as sales totals or inventories. Users can interact with dimensions to filter and drill down into measures of interest. For example, a user might select "North America" as a dimension to filter a measure such as "Sales Totals". OLAP tools typically help users to identify patterns and trends that would be difficult to discern from looking at raw data tables.
OLAP & OLTP in graph databases
Essentially, a graph database can be partitioned into OLAP and OLTP parts. The OLAP part is concerned with offline analysis whereas OLTP is mainly for online processing.
There are two key frameworks in the OLAP partition: graph computing and graph streaming. Graph computing is used for analyzing data based on graph structures, and this is quite similar in concept to the OLAP in traditional relational databases. The difference is the iterative algorithm, which is not available in the traditional database. For example, the Google PageRank algorithm applies constant iterative computing to determine the relevance of a web page. And so graph databases will be best suited for such a need. The graph streaming framework is basically a mix of basic computing and streaming computing. The latency here is in seconds and the computing is triggered by events.
The OLTP section of the graph database can have many use cases online which are entirely different from the OLAP use cases.
Refer to the illustration below:
Examples of systems that support OLTP
Common examples of OLTP systems include.
- Titan: Titan is a highly scalable graph database that allows multiple users to access and update a single graph concurrently.
- NebulaGraph: NebulaGraph is an extremely fast open source graph database that uses RocksDB as its storage engine - the only open source graph database that can process graphs with trillions of edges and vertices.
- OrientDB：This OLTP system manages relationships using direct pointers that provide consistent performance.
- Neo4j: Neo4j is an enterprise-strength OLTP that provides advanced security, native graph storage, and scalable, fast architecture.
Examples of systems that support OLAP
Here are a few examples of OLAP.
- IBM Cognos: This is an integrated, web-based OLAP system with a toolkit for data analysis, score carding, reporting, and performance analysis.
- Integrate.io: This toolkit has code, no-code, and low-code capabilities for developing data pipelines. It allows users to create data for business intelligence.
- Apache Kylin: This multidimensional open-source analytics tool provides an SQL interface that supports large datasets.
The main differences difference between OLAP and OLTP: Processing type
In order to effectively decide which system to use between OLTP and OLAP, it is vital to understand the key differences between them. Of course the key difference is that OLTP is designed for transactions, while OLAP is great for analysis and insights. Here are the main differences worth knowing.
1. Data source
OLAP databases are multidimensional and thus support complex queries from historical and current data sets. OLTP databases can be set up as a data warehouse to provide aggregated data for OLTP. On the other hand, OLTP systems source their data from traditional relational databases that handle massive amounts of real-time transactions.
2. Processing time
OLAP handles read-intensive workloads that involve massive amounts of data and this means the response and processing time for OLAP will depend on the amount of data that is being processed. Therefore, it may take seconds, minutes, or even hours, making it slower than OLTP.
OLTP is a transaction processing tool that handles simple read-write operations using a structured query language (SQL). Such processes require fewer computing resources, and thus transactions and responses are derived in milliseconds.
OLTP handles current transactional data. Such data is updated and modified frequently and thus requires regular backup to maintain data integrity constraints. Furthermore, OLTP requires frequent backup to adhere to governance and legal requirements. However, OLAP doesn’t handle so much current data, which gives room for less frequent backups.
OLTP runs and controls business operations in real time. It makes it simple to perform insertions, deletions, and updates in a database. On the other hand, OLAP handles complex data analysis to discover hidden insights and support business decisions. OLAP queries involve large amounts of data.
5. Storage requirements
OLTP deals with historical transactional data that requires a small storage capacity, especially if the data is archived. In contrast, OLAP handles massive data because it aggregates multiple large datasets. These processes require a larger storage capacity. 6. Database design and data view
OLTP databases store daily business transactions and thus are designed for efficiency. On the other hand, OLAP provides a multidimensional view of business data and is thus designed for data analysis.
OLTP databases are market-oriented. They increase the end user’s productivity by providing data for data-critical users such as clerks and tellers. In contrast, OLAP databases are customer oriented and provide data for data-knowledge users, like top executives, business managers, and data analysts.
The table below summarizes the key differences between OLTP and OLAP.
|Data Source||Sources data from OLTP databases||Sources data from the traditional relational databases|
|Processing time||Processing time depends on the amount of data being processed||Responses are derived in milliseconds|
|Backup||Less frequent backups||Frequent backups|
|Purpose||Complex data analysis||Simple transactional operations|
|Storage requirements||Requires large storage capacity||Needs less storage capacity|
|Database design and data view||Designed for analysis||Designed for efficiency|
|Audience||Market-oriented data for data-critical users||Customer-oriented data for data-knowledge users.|
Drawbacks of OLTP
1. Dependency: OLTP systems can be heavily dependent on staff, which can call for a lot of investment to train new staff members.
2. Information loss risks: OLTP systems often contain sensitive information, such as credit card numbers or social security numbers. This information is at risk of being compromised if the system is not properly secured.
3. Data inconsistencies: OLTP systems allow concurrent data modifications, which can lead to data inconsistencies. For example, two staff members may attempt to modify the same record at the same time, resulting in one of the changes being overwritten. Additionally, concurrent data modifications can make it difficult to track changes made to the data over time.
4. Number of queries: OLTP systems are designed to support a limited number of queries, making them unsuitable for business intelligence or decision support applications.
Drawbacks of OLAP
1. Speed: OLAP systems can be slow. This is because they have to process large amounts of data, which can take time.
2. Complex data organization: OLAP systems require data to be organized into star schemas or snowflakes, which can be complicated. This means that the data must be arranged in a specific way, which can be time-consuming and difficult.
3. Number of dimensions: An OLAP cube cannot accommodate large numbers of dimensions. This means that if there are too many dimensions, the system will not be able to cope.
4. Cube changes: If any changes are made to the OLAP cube, the entire cube must be updated, which can be time-consuming and frustrating.
Verdict: Which is best for you, OLAP or OLTP?
When it comes to which to use between OLTP and OLAP, the question should never be about which one is the best but rather how to leverage both for complex data tasks. OLTP focuses on operational data while OLAP focuses on analytics. For example, you can use OLAP to gather insights from massive data, then use these insights to create efficient transactions with OLTP.
Using both systems gives you the advantage of being able to have better control of your data and derive holistic value. Using only one where you need both can be limiting, as you will more or less be forcing a single system to perform functions that it’s not designed for. But if you are purely interested in data analysis, then by all means go for OLAP. If you are exclusively keen on transactions and nothing more, it’s perfectly okay to go for OLTP.