What is Molap and Rolap and whats the difference between these two ?
MOLAP = Multidimensional Online Analytical Processing
ROLAP = Relational Online Analytical Processing
Essentially with ROLAP, the data is stored in a relational database, whereby with MOLAP, i.e. the traditional OLAP model, this is stored in multidimentional "cubes". Cubes are a multidimensional structure similar to the star schema in an RDBMS, but where the management of the storage is highly optimized to deal with such a structure.
At the risk of adding to the alphabet soup, another model is HOLAP, for Hybrid OLAP, which attempts to provide the best of traditional MOLAP with the benefits of "Relational" in ROLAP.
MOLAP's main advantage is its excellent query performance and fast data retrieval.
Its main disadvantage is that is may be limited in the amount amount of data it can handle. Another disadvantage is the use of proprietary engines.
ROLAP's performance is slower, but it also it is less limited in term of the number of dimensions etc.
These are terms associated with Datawarehousing. ROLAP is relational OLAP.
Related
One of the advantages of NoSQL databases is to handle unstructured data. Since that issue is now resolved in SQL databases, is there any need left for NoSQL? Only advantage that I can think of is NoSQL is still better at scalability.
You might choose a NoSQL database for the following reasons:
To store large volumes of data that might have little to no
structure.
NoSQL databases do not limit the types of data that you can store
together. NoSQL databases also enable you to add new data types as
your needs change. With document-oriented databases, you can store
data in one place without having to define the data type in advance.
To make the most of cloud computing and storage.
In order for a cloud solution to be scalable, the data must be easy
to share across multiple servers.
To speed development.
When you are developing in rapid iterations or making frequent
updates to the data structure, a relational database slows you down.
However, because NoSQL data doesn’t need to be prepped ahead of time,
you can make frequent updates to the data structure with minimal
downtime.
To boost horizontal scalability.
The CAP (consistency, availability, and partition tolerance) theorem
states that in any distributed system, only two of the three CAP
properties can be used simultaneously. Adjusting these properties in
favor of strong partition tolerance enables NoSQL users to boost
horizontal scalability.
The following Link provides sufficient details about the requirement of NoSQL databases.
https://support.rackspace.com/how-to/reasons-to-use-a-nosql-db/
What are the key differences between OLAP and OLTP databases.
Specifically in terms of implementation (rather than use cases).
OLAP is of course primarily used for reporting while OLTP is used for handling transactions.
I understand that OLAP databases are optimized for read over write, and that OLAP databases contain more denormalised data.
What other characteristics set the two apart?
OLTP:
As the name suggest "Online Transaction Processing", this is used for more transaction needs like "INSERT/SELECT/UPDATE/DELETE".
Low Response Time.
There are the original source of data.
Usually data is stored in 3NF form.
ACID properties are necessarily followed.
OLAP:
As the name suggest "Online Analytical Platform", used for analytical queries and in general are used for complex analytical queries and drawing inferences.
Periodic batch processing jobs are run here.
Typically de-normalized with fewer tables; use of star and/or snowflake schemas.
NOT necessarily follows ACID properties.
There are many difference. You may find tons of answers by googling this question. But some of the characteristics which are derived from practical implementation from my own experiences are:
OLTP is business domain specific system designed to perform specific tasks for example an eCommerce website having a database for handling online order while another OLTP database is being used for back end operation for order processing another OLTP database is for logistics etc. Whereas OLAP systems are designed to look at the information at whole business level by sourcing data from many heterogeneous system.
If I simplified the above example then OLTP is small units of Business Processing system while OLAP system is a large unit of Business Information.
You can refer this link for more clarification.
I was assigned a task to design a database schema using operational model, but unfortunately unable to get any information on what exactly the operational model mean.
I worked extensively on Star and snowflake schemas but management doesn't require these schema designs and stressing me to create a operational model.
If anyone can give some insight on what operational model and how can I get some knowledge would be great help.
Operational Model is a model which would help in daily operations and not just getting statistics about the data.
The management is looking for a daily Transactional Processing Database Systems whereas the snowflake and the star schema are great designs for Analytical Processing Systems and Warehouses but not for daily transactional Processing Systems. They are looking for an OLTP system than an OLAP System.
OLTP (On-line Transaction Processing) is characterized by a large number of short on-line transactions (INSERT, UPDATE, DELETE). The main emphasis for OLTP systems is put on very fast query processing, maintaining data integrity in multi-access environments and an effectiveness measured by a number of transactions per second. In OLTP database there is detailed and current data, and schema used to store transactional databases is the entity model (usually 3NF).
OLAP (On-line Analytical Processing) is characterized by a relatively low volume of transactions. Queries are often very complex and involve aggregations. For OLAP systems a response time is an effectiveness measure. OLAP applications are widely used by Data Mining techniques. In OLAP database there is aggregated, historical data, stored in multi-dimensional schemas (usually star schema).
I know that the relational databases are not scalable to Big-Data. I also know that memcaching is a bit complicated in them. I also know that you may need to unnest and flatten data to prepare it for analysis.
However, I still think that for structured data, for some scenarios, it is better to apply them rather than time series databases like InfluxDB.
I do not think that sending queries causes latency and make relational databases inappropriate for time-series analysis. I could not find my answer [here]. So, my question is when we can use Relational databases for Time-Series analysis?
I have read all those article about datawarehouse and olap.....however I have some question on it
I have created a datawarehouse using mysql and I also created an API which contain ad-hoc query to query from the datawarehouse, so is this API consider as ROLAP?
Is it possible to create own OLAP? If yes, how?
Usually data warehouse has normalized structure and DWH is not the same as ROLAP.
ROLAP it is technique used to modeling data. ROLAP is usually used for reporting. ROLAP is very good to make analytical query and you can use many reporting (BI) tools to easily build reports on you data.
It isn't necessary to write you own application to build reports. ROLAP (relational OLAP) it is when you model you data as "star" or "snowflake" using facts and dimension tables in traditional RDBMS. It star schemas also called "multidimensional cubes".
By OLAP often is meant MOLAP (multidimensional OLAP) - it's when you really store your data in multidimensional data structure in special data stores (not in RDBMS).
You shouldent create you own MOLAP e data storag- you should use alredy developed OLAP servers like MANDARIN, Pentaho Olap,Essbase, ORACLE EE database with OLAP option.
The confusion you are pointing out comes from the fact that peoples tend to use this term anywhere and in a wrong context.
OLAP applications are precisely defined by the OLAP council. These are applications that fullfill a bunch of requirements. You can read these requirements Here.
In big words, these are analytical oriented applications that allow you to build reports in an a multidimensional fashion (it means you have dimensions and indicators that you can cross) and get fast anwsers at enterprise-scale, with drill down and drill accross capabilities. Something close to OLAP applications is this : http://try.meteorite.bi/
Building an adhoc reporting engine on top of a datawarehouse doesn't mean you have an OLAP application. Does it have a multidimensional shape ? Is it user oriented ? Is it fast enough ? It has to answer yes to all these questions and the ones below to be a candidate to be an OLAP application.