EDUCBA

EDUCBA

MENUMENU
  • Free Tutorials
  • Free Courses
  • Certification Courses
  • 360+ Courses All in One Bundle
  • Login

ROLAP vs MOLAP vs HOLAP

By Priya PedamkarPriya Pedamkar

Home » Data Science » Data Science Tutorials » Head to Head Differences Tutorial » ROLAP vs MOLAP vs HOLAP

Rolap-vs-Molap-vs-Holap

Difference Between ROLAP vs MOLAP vs HOLAP

ROLAP vs MOLAP vs HOLAP are the associated terminologies for data warehousing that represents logical data models. ROLAP means relational online analytical processing for relational data. MOLAP is known as multidimensional online analytical processing those implements through multiple data dimensions. HOLAP is known as hybrid online analytical processing that works for both ROLAP and MOLAP concepts. The data storage and data arrangements, designed view access in the data warehouse varies depending upon the type of the OLAP implementation. ROLAP SQL is being the querying technique, whereas MOLAP works with the sparse matrix, and HOLAP uses both SQL and sparse matrix technologies.

Head to Head Comparison Between ROLAP and MOLAP and HOLAP (Infographics)

Below is the top 8 comparison between ROLAP vs MOLAP vs HOLAP:

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

Rolap-vs-Molap-vs-Holap-info

Key Differences Between ROLAP vs MOLAP vs HOLAP

Let us discuss some of the major key differences between ROLAP and MOLAP and HOLAP:

  • ROLAP is relational OLAP where the data is arranged in traditional methods like rows and columns in the data warehouse. It is visible and accessible to users in multi-dimensional form. To display it as a multi-dimensional view the data is designed as the related layer of metadata which supports the collection and storage of data. It does dynamically in handling the complex query. It is slower than MOLAP where ROLAP deals with the enormous volume of data at a higher speed.

ROLAP Model

  • MOLAP is a multi-dimensional OLAP where the data is analyzed on the registered system. The data is arranged in a multi-dimensional array. The array carries predefined data when the data is loaded in database management. MOLAP system is implemented on the application layer and when the user sends any request it fetches the data with the minimum response time.

MOLAP Model

  • The expressing power of the relational model does not include the topics of dimension and measure to create a specific data type. The basic elements include integrity, attributes, relations which are mainly applied in Star schema.
  • ROLAP uses SQL as its functioning language to fetch the data and work on it, whereas the MOLAP uses the Sparse matrix technique to get the data from multi-dimensional array in the form of dimensional data cubes.
  • ROLAP has slow response time because it shows the multi-dimensional form of any data but MOLAP is very fast since it does not show any multi-dimensional view.
  • Both ROLAP and MOLAP handle complex query and it has its unique performance. If the user wants any fast response system he can adopt to MOLAP
  • ROLAP and MOLAP work on optimization techniques and created due to its sparsity.
  • Here the intermediate structure HOLAP formed with a mixture of advantages of MOLAP and ROLAP. A large amount of data handling capacity is taken from ROLAP and the query speed method is taken from MOLAP which is fed to HOLAP which stands as a standardized model. HOLAP relies on its enormous data should be saved in a relational database management system to get rid of flaws created by sparsity and multi-dimensional engine which stores only the required information of the user and provide them frequent access. But if the user request more related data to solve any complex query it provides transparent access to that portion of a relational database. This HOLAP technique is adopted by popular MicroStrategy to increase their platform performance in partnership with other vendors who have already implemented this solution in their business.
  • But in this design, there are few troubles which should be overcome to have a high performance.
  • The quality of the process should be enhanced to satisfy client requirements. The quality should be consistent in data warehousing from the initial phase to the end phase. The few main areas where quality should be considered are defining areas, measuring areas and maximizing parts.
  • The important qualities are accuracy, updated data, completed data, consistency, traceability, availability, and clarity.
  • In Accuracy, the data should have the correct and real values because at the time of ETL the chances of missing values are high and also giving nonstandard value to any attribute should be avoided
  • The data should be updated periodically and should not contain any old data
  • The data cubes should not be missed. Because each data set represent unique primary keys and all the values should be stored from top to bottom and should be available as a complete data
  • The representation of data should be in a proper arrangement in an orderly manner where it gives the user a high consistency performance.
  • The data should be easily available and accessible to the user at any time
  • The data pool should have the correct navigation about the sources so that the user can easily direct to that part of data without any wastage of time
  • The data should have high clarity and should be easy to understand.

Comparison Table of ROLAP vs MOLAP vs HOLAP

The table below summarizes the comparisons between ROLAP vs MOLAP vs HOLAP:

Basics for comparison ROLAP MOLAP HOLAP
Acronym Relational online analytical processing Multi-dimensional online analytical processing Hybrid online analytical processing
Storage methods Data is stored on the main data warehouse Data is stored on the registered database MDDB Data is stored on the relational databases
Fetching methods Data is fetched from the main repository Data is fetched from the Proprietary database Data is fetched from the relational databases
Data Arrangement Data is arranged and saved in the form of tables with rows and columns Data is arranged and stored in the form of data cubes Data is arranged in multi-dimensional form
Volume Enormous data is processed Limited data which is kept in proprietary is processed Large data can be processed
Technique  It works with SQL It works with Sparse Matrix technology It uses both Sparse matrix technology and SQL
Designed view It has dynamic access It has a static access It has dynamic access
Response time It has Maximum response time It has Minimum response time It takes Minimum response time

Conclusion

The main topic should be discussed here is Information Security which should be carried from the development stage to the implementation stage and it is performed on its maintenance time also. Security is a key element for data warehousing because that is a place where the solution to crucial problems is taken and a large amount of data transaction and processing is done. The management and its auditing systems are crucial for data warehousing as important as the security system. The enterprise takes advantage of this online analytical processing system and implies it according to the demand.

Recommended Articles

This is a guide to ROLAP vs MOLAP vs HOLAP. Here we also discuss the key differences with infographics, and comparison table. You may also have a look at the following articles to learn more-

  1. CFA vs CFP – Top Differences
  2. Physical Address vs Logical Address
  3. List vs Set – Useful Comparisons
  4. Traditional Marketing vs Digital Marketing

All in One Data Science Bundle (360+ Courses, 50+ projects)

360+ Online Courses

1500+ Hours

Verifiable Certificates

Lifetime Access

Learn More

0 Shares
Share
Tweet
Share
Primary Sidebar
Head to Head Differences Tutorial
  • Differences Tutorial
    • Cloud Computing vs Big Data Analytics
    • PostgreSQL vs MariaDB
    • Domo vs Tableau
    • Data Scientist vs Data Engineer vs Statistician
    • Big Data Vs Machine Learning
    • Business Intelligence vs Data Warehouse
    • Apache Kafka vs Flume
    • Data Science vs Machine Learning
    • Business Analytics Vs Predictive Analytics
    • Data mining vs Web mining
    • Data Science Vs Data Mining
    • Data Science Vs Business Analytics
    • Analyst vs Associate
    • Apache Hive vs Apache Spark SQL
    • Apache Nifi vs Apache Spark
    • Apache Spark vs Apache Flink
    • Apache Storm vs Kafka
    • Artificial Intelligence vs Business Intelligence
    • Artificial Intelligence vs Human Intelligence
    • Al vs ML vs Deep Learning
    • Assembly Language vs Machine Language
    • AWS vs AZURE
    • AWS vs Azure vs Google Cloud
    • MapReduce vs Spark
    • Big Data vs Data Mining
    • Big Data vs Data Science
    • Big Data vs Data Warehouse
    • Blu-Ray vs DVD
    • Business Intelligence vs Big Data
    • Business Intelligence vs Business Analytics
    • Business Intelligence vs Data analytics
    • Business Intelligence VS Data Mining
    • Business Intelligence vs Machine Learning
    • Business Process Re-Engineering vs CI
    • Cassandra vs Elasticsearch
    • Cassandra vs Redis
    • Cloud Computing Public vs Private
    • Cloud Computing vs Fog Computing
    • Cloud Computing vs Grid Computing
    • Cloud Computing vs Hadoop
    • Computer Network vs Data Communication
    • Computer Science vs Data Science
    • Computer Scientist vs Data Scientist
    • Customer Analytics vs Web Analytics
    • Data Analyst vs Data Scientist
    • Data Analytics vs Business Analytics
    • Data Analytics vs Data Analysis
    • Data Analytics Vs Predictive Analytics
    • Data Lake vs Data Warehouse
    • Data Mining Vs Data Visualization
    • Data mining vs Machine learning
    • Data Mining Vs Statistics
    • Data Mining vs Text Mining
    • Data Science vs Artificial Intelligence
    • Data science vs Business intelligence
    • Data Science Vs Data Engineering
    • Data Science vs Data Visualization
    • Data Science vs Software Engineering
    • Data Scientist vs Big Data
    • Data Scientist vs Business Analyst
    • Data Scientist vs Data Engineer
    • Data Scientist vs Data Mining
    • Data Scientist vs Machine Learning
    • Data Scientist vs Software Engineer
    • Data visualisation vs Data analytics
    • Data vs Information
    • Data Warehouse vs Data Mart
    • Data Warehouse vs Database
    • Data Warehouse vs Hadoop
    • Data Warehousing VS Data Mining
    • DBMS vs RDBMS
    • Deep Learning vs Machine learning
    • Digital Analytics vs Digital Marketing
    • Digital Ocean vs AWS
    • DOS vs Windows
    • ETL vs ELT
    • Small Data Vs Big Data
    • Apache Hadoop vs Apache Storm
    • Hadoop vs HBase
    • Between Data Science vs Web Development
    • Hadoop vs MapReduce
    • Hadoop Vs SQL
    • Google Analytics vs Mixpanel
    • Google Analytics Vs Piwik
    • Google Cloud vs AWS
    • Hadoop vs Apache Spark
    • Hadoop vs Cassandra
    • Hadoop vs Elasticsearch
    • Hadoop vs Hive
    • Hadoop vs MongoDB
    • HADOOP vs RDBMS
    • Hadoop vs Spark
    • Hadoop vs Splunk
    • Hadoop vs SQL Performance
    • Hadoop vs Teradata
    • HBase vs HDFS
    • Hive VS HUE
    • Hive vs Impala
    • JDBC vs ODBC
    • Kafka vs Kinesis
    • Kafka vs Spark
    • Cloud Computing vs Data Analytics
    • Data Mining Vs Data Analysis
    • Data Science vs Statistics
    • Big Data Vs Predictive Analytics
    • MapReduce vs Yarn
    • Hadoop vs Redshift
    • Looker vs Tableau
    • Machine Learning vs Artificial Intelligence
    • Machine Learning vs Neural Network
    • Machine Learning vs Predictive Analytics
    • Machine Learning vs Predictive Modelling
    • Machine Learning vs Statistics
    • MariaDB vs MySQL
    • Mathematica vs Matlab
    • Matlab vs Octave
    • MATLAB vs R
    • MongoDB vs Cassandra
    • MongoDB vs DynamoDB
    • MongoDB vs HBase
    • MongoDB vs Oracle
    • MongoDB vs Postgres
    • MongoDB vs PostgreSQL
    • MongoDB vs SQL
    • MongoDB vs SQL server
    • MS SQL vs MYSQL
    • MySQL vs MongoDB
    • MySQL vs MySQLi
    • MySQL vs NoSQL
    • MySQL vs SQL Server
    • MySQL vs SQLite
    • Neural Networks vs Deep Learning
    • PIG vs MapReduce
    • Pig vs Spark
    • PL SQL vs SQL
    • Power BI Dashboard vs Report
    • Power BI vs Excel
    • Power BI vs QlikView
    • Power BI vs SSRS
    • Power BI vs Tableau
    • Power BI vs Tableau vs Qlik
    • PowerShell vs Bash
    • PowerShell vs CMD
    • PowerShell vs Command Prompt
    • PowerShell vs Python
    • Predictive Analysis vs Forecasting
    • Predictive Analytics vs Data Mining
    • Predictive Analytics vs Data Science
    • Predictive Analytics vs Descriptive Analytics
    • Predictive Analytics vs Statistics
    • Predictive Modeling vs Predictive Analytics
    • Private Cloud vs Public Cloud
    • Regression vs ANOVA
    • Regression vs Classification
    • ROLAP vs MOLAP
    • ROLAP vs MOLAP vs HOLAP
    • Spark SQL vs Presto
    • Splunk vs Elastic Search
    • Splunk vs Nagios
    • Splunk vs Spark
    • Splunk vs Tableau
    • Spring Cloud vs Spring Boot
    • Spring vs Hibernate
    • Spring vs Spring Boot
    • Spring vs Struts
    • SQL Server vs PostgreSQL
    • Sqoop vs Flume
    • Statistics vs Machine learning
    • Supervised Learning vs Deep Learning
    • Supervised Learning vs Reinforcement Learning
    • Supervised Learning vs Unsupervised Learning
    • Tableau vs Domo
    • Tableau vs Microstrategy
    • Tableau vs Power BI vs QlikView
    • Tableau vs QlikView
    • Tableau vs Spotfire
    • Talend Vs Informatica PowerCenter
    • Talend vs Mulesoft
    • Talend vs Pentaho
    • Talend vs SSIS
    • TensorFlow vs Caffe
    • Tensorflow vs Pytorch
    • TensorFlow vs Spark
    • TeraData vs Oracle
    • Text Mining vs Natural Language Processing
    • Text Mining vs Text Analytics
    • Cloud Computing vs Virtualization
    • Unit Test vs Integration Test?
    • Universal analytics vs Google Analytics
    • Visual Analytics vs Tableau
    • R vs Python
    • R vs SPSS
    • Star Schema vs Snowflake Schema
    • DDL vs DML
    • R vs R Squared
    • ActiveMQ vs Kafka
    • TDM vs FDM
    • Linear Regression vs Logistic Regression
    • Slf4j vs Log4j
    • Redis vs Kafka
    • Travis vs Jenkins
    • Fact Table vs Dimension Table
    • OLTP vs OLAP
    • Openstack vs Virtualization
    • Cluster v/s Factor analysis
    • Informatica vs Datastage
    • CCBA vs CBAP
    • SPSS vs EXCEL
    • Excel vs Tableau
    • Cassandra vs MySQL
    • RabbitMQ vs Kafka
    • SAAS vs Cloud
    • RabbitMQ vs Redis
    • AMQP vs MQTT
    • Forward Chaining vs Backward Chaining
    • Google Data Studio vs Tableau
    • ActiveMQ vs RabbitMQ
    • Cloud vs Data Center
    • Cores vs Threads
    • Inner Join vs Outer Join
    • ZeroMQ vs Kafka
    • Mxnet vs TensorFlow
    • Datadog vs Splunk
    • Redis vs Memcached
    • RDBMS vs NoSQL
    • AWS Direct Connect vs VPN
    • Cassandra vs Couchbase
    • Elegoo vs Arduino
    • Redis vs MongoDB
    • Chef vs Puppet
    • GSM vs GPRS
    • Keras vs TensorFlow vs PyTorch
    • Cloudflare vs CloudFront
    • Bitmap vs Vector
    • Left Join vs Right Join
    • IaaS vs PaaS
    • Blue Prism vs UiPath
    • GNSS vs GPS
    • Cloudflare vs Akamai
    • GCP vs AWS vs Azure
    • Arduino Mega vs Uno
    • Qualitative vs Quantitative Data
    • Arduino Micro vs Nano
    • PIC vs Arduino
    • PRTG vs Solarwinds
    • PostgreSQL vs SQLite
    • Metabase vs Tableau
    • Arduino Leonardo vs Uno
    • Arduino Due vs Mega
    • ETL Vs Database Testing
    • DBMS vs File System
    • CouchDB vs MongoDB
    • Arduino Nano vs Mini
    • IaaS vs PaaS vs SaaS
    • On-premise vs off-premise
    • Couchbase vs CouchDB
    • Tableau Dimension vs Measure
    • Cognos vs Tableau
    • Data vs Metadata
    • RethinkDB vs MongoDB

Related Courses

Online Data Science Course

Online Tableau Training

Azure Training Course

Hadoop Certification Course

Data Visualization Courses

All in One Data Science Course

Footer
About Us
  • Blog
  • Who is EDUCBA?
  • Sign Up
  • Corporate Training
  • Certificate from Top Institutions
  • Contact Us
  • Verifiable Certificate
  • Reviews
  • Terms and Conditions
  • Privacy Policy
  •  
Apps
  • iPhone & iPad
  • Android
Resources
  • Free Courses
  • Database Management
  • Machine Learning
  • All Tutorials
Certification Courses
  • All Courses
  • Data Science Course - All in One Bundle
  • Machine Learning Course
  • Hadoop Certification Training
  • Cloud Computing Training Course
  • R Programming Course
  • AWS Training Course
  • SAS Training Course

© 2020 - EDUCBA. ALL RIGHTS RESERVED. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS.

EDUCBA Login

Forgot Password?

EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

*Please provide your correct email id. Login details for this Free course will be emailed to you
Book Your One Instructor : One Learner Free Class

Let’s Get Started

This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy

EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you
EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

*Please provide your correct email id. Login details for this Free course will be emailed to you

Special Offer - All in One Data Science Bundle (360+ Courses, 50+ projects) Learn More