Top Differences Every Data Professional Should Know in 2021

Data is essential for all successful business decisions. The more a company makes use of its data effectively, the more likely it will stay ahead of the curve. These days, a lot of businesses choose to build their business intelligence solutions using OLTP and OLAP technology. They help BI developers get maximum benefits from data and develop the most effective strategies. These two terms may sound very similar but they refer to different kinds of systems with different purposes. This article will discuss the importance of OLTP vs OLAP in business intelligence and their main differences.

Contents

What is OLTP?

OLTP stands for Online Transaction Processing and its primary objective is the processing of data. An OLTP system administers the day to day transaction of data under a 3-tier architecture (usually 3NF). Each of these transactions involves individual records made up of multiple fields. The main emphasis of OLTP is fast query processing and data integrity in multi-access environments. Some OLTP examples are credit card activity, order entry, and ATM transactions.

OLTP Benefits

Maintains all the daily transactions of an organization
Simplifies individual processes

OLTP Challenges

Transactions are severely affected if the OLTP system fails
Multiple users can access and change the same data at the same time which can cause confusion

OLTP Tools

An OLTP system is, in itself, a data processing system. For normal transactions, OLTP uses client/server processing to perform multiple transactions. For bigger applications, OLTP uses software, such as CICS for sophisticated transaction management.

What is OLAP?

OLAP stands for Online Analytical Processing and its primary objective is the analysis of data. It is generally described as a category of software tools used to provide data analysis for business decisions. With the help of OLAP, data analysts can get an insight into the information on multiple databases and analyze them at a time. The main emphasis of OLAP is the response time to complex queries.

Some OLAP examples are:

Personalized homepage for different customers (Netflix, Amazon)
Comparison of sales in different months stored in separate databases.

An OLAP database uses a multidimensional data model, which includes features of relational, navigational, and hierarchical databases. It also consists of an OLAP cube which consists of multiple types of data.

Fig: OLAP cube (source)

OLAP Benefits

Provides consistency of information and calculations
Security restrictions can be easily applied to different users

OLAP Challenges

Only users with data analyst skills can handle OLAP systems
It requires the cooperation between people of various departments which may not always be feasible

OLAP Tools

OLAP tools can be classified based on the three main types of OLAP – multidimensional OLAP (MOLAP), relational OLAP (ROLAP), and hybrid OLAP (HOLAP).

MOLAP stores data in a multidimensional database and requires a pre-computation of data.
ROLAP stores data in a relational database and does not require a precomputation of data.
HOLAP allows data analysts to decide whether to store their data in a MOLAP or ROLAP structure.

Although not widely used, these are some important OLAP types to keep in mind – Real-Time OLAP (RTOLAP), Web-based OLAP (WOLAP), Graph OLAP (GOLAP), Desktop OLAP (DOLAP), and Context-aware semantic OLAP (CaseOLAP).

MOLAP Tools – IBM Cognos, SAS OLAP Server, Microsoft Analysis Services
ROLAP tools – SAP Netweaver BW, Jedox OLAP Server, Microstrategy Intelligence Server
HOLAP tools – Mondrian OLAP server, Essbase, SAS OLAP server

OLTP vs OLAP: Differences

The main difference between OLTP vs OLAP is that OLTP is operational, whereas OLAP is informational.

Fig: OLTP vs OLAP (source)

Here is a list of OLTP vs OLAP’s top 15 key features that illustrate both their differences and how they need to work together.

Parameters	OLTP	OLAP
Main characteristics	Handles a large number of small transactions on a day to day basis	Handles large volumes of data in multiple databases
Data source	Transactions	OLTP databases and other sources
Purpose	To support essential business intelligence operations in real-time	To discover hidden insights and support business decisions
Response time	Milliseconds	Seconds to hours (depends on the amount of data to be processed)
Query type	Simple	Complex
Database design	Normalized database for efficiency	Denormalized database for analysis
Audience	Market-oriented	Customer-oriented
Domain	Industry-specific (manufacturing, finance, etc.)	Subject-specific (sales, marketing, etc.)
Performance metric	Transaction throughput	Query throughput
Challenge	Data warehouses can be expensive to build	Strong technical knowledge and experience is required
Design	Designed to have fast processing and low redundancy	Designed uniquely to integrate different data sources to build a consolidated database
Operations	INSERT, DELETE and UPDATE commands	SELECT command
Updates	Short and fast updates	Updates are scheduled and done periodically
No. of users	Thousands of users allowed at a time	Only a few users allowed at a time
Space requirements	Very small (if data is archived periodically)	Very large

What Holds OLTP and OLAP Together?

In order to get actionable insights from the OLTP data, it must be first ingested into a data warehouse for analysis. OLAP systems integrate the data from multiple OLTP databases through an ETL (extract, transform, load) tool. You can collect and copy data from multiple sources using ETL tools and send it to an OLAP data warehouse where data is processed and analyzed to develop useful insights.

ETL tools also remove the need for constant code maintenance, thus enabling data professionals to focus on more value-added activities.

Here are some of the best ETL tools out there:

Get broad exposure to key technologies and skills used in data analytics and data science, including statistics with the PG in Data Analytics Bootcamp.

Here’s What You Can Do Next

Business intelligence has a substantial impact on an organization’s strategic business decisions. If you are planning to venture into this career space, Simplilearn’s Data Analytics Certification Courses will provide you with a broad exposure to key technologies and skills currently used in Data Analytics and Data Science, including Statistics, Python, R, Tableau, SQL, and Power BI. This course has been created in collaboration with Purdue University and IBM, and provides a hands-on approach with case studies and industry-aligned projects to help you understand the most advanced concepts. Get started with this course today and become a professional in data analytics.

#Top #Differences #Data #Professional

Professional Certificate Program in Data Analytics

Data Analytics Free Course

Leave a Reply Cancel reply