Types of Data Warehouse

Information processing, analytical processing, and data mining are the three types of data warehouse applications that are discussed below:

1.  Information Processing – A data warehouse allows to process the data stored in it. The data can be processed by means of querying, basic statistical analysis, reporting using crosstabs, tables, charts, or graphs. 
2.  Analytical Processing – A data warehouse supports analytical processing of the information stored in it. The data can be analyzed by means of basic OLAP operations, including slice-and-dice, drill down, drill up, and pivoting. 
3.  Data Mining - Data mining supports knowledge discovery by finding hidden patterns and associations, constructing analytical models, performing classification and prediction. These mining results can be presented using visualization tools.  

OLTP vs OLAP: What's the Difference?

What is OLTP?

Online transaction processing shortly known as OLTP supports transaction-oriented applications in a 3-tier architecture. OLTP administers day to day transaction of an organization.

The primary objective is data processing and not data analysis

examples of OLTP system are:



Online banking
Online airline ticket booking
Sending a text message
Order entry
Benefits of OLTP method

It administers daily transactions of an organization.
OLTP widens the customer base of an organization by simplifying individual processes.


What is OLAP?



Online Analytical Processing, a category of software tools which provide analysis of data for business decisions. OLAP systems allow users to analyze database information from multiple database systems at one time.

The primary objective is data analysis and not data processing.

Example of OLAP

Any Datawarehouse system is an OLAP system. Uses of OLAP are as follows

A company might compare their mobile phone sales in September with sales in October, then compare those results with the with another location which may be stored in a sperate database.
Amazon analyzes purchases by its customers to come up with a personalized homepage with products which likely interest to their customer.
Benefits of using OLAP services
OLAP creates a single platform for all type of business analytical needs which includes planning, budgeting, forecasting, and analysis.
The main benefit of OLAP is the consistency of information and calculations.
Easily apply security restrictions on users and objects to comply with regulations and protect sensitive data.

Data Warehouse Concepts, Architecture and Components

1.

What is Data warehouse?

Data warehouse is an information system that contains historical and commutative data from single or multiple sources. It simplifies reporting and analysis process of the organization.

It is also a single version of truth for any company for decision making and forecasting.

In this tutorial, you will learn-

What is Data warehouse?
Characteristics of Data warehouse
·         Subject-Oriented

·         Integrated

·         Time-Variant

·         Non-volatile

Data Warehouse Architectures
Datawarehouse Components
·         Data Warehouse Database

·         Sourcing, Acquisition, Clean-up and Transformation Tools (ETL)

·         Metadata

·         Query Tools

·         Data warehouse Bus Architecture

Data Marts
Data warehouse Architecture Best Practices

List of ETL tools

List of ETL tools - 

1. Oracle Warehouse Builder (OWB)
2. SAP Data Services
3. IBM Infosphere Information Server
4. SAS Data Management
5. PowerCenter Informatica
6. Elixir Repertoire for Data ETL
7. Data Migrator (IBI)
8. SQL Server Integration Services (SSIS)
9. Talend Studio for Data Integration
10. Sagent Data Flow
11. Actian DataConnect
12. Open Text Integration Center
13. Oracle Data Integrator (ODI)
14. Cognos Data Manager
15. CloverETL
16. Centerprise Data Integrator
17. IBM Infosphere Warehouse Edition
18. Pentaho Data Integration
19. Adeptia Integration Server
20. Syncsort DMX
21. QlikView Expressor
22. Relational Junction ETL Manager (Sesame Software)

The Python topics Go From Beginner To Advanced!

What Will I Learn?

• Understand the basic as well as core concepts of Python programming language

• Acquire enhanced skills with Python graphics

• Design, create, develop, and code Python games

• Read and write external data files using Python
• Implement functions and call built-in Python functions
• Debug an entire program and handle errors in Python
• Implement basic Python structures such as if statements, loops, and functions

Learn Data Science

learn Data Science in R from Scratch

Table of Contents

1. Basics of R Programming for Data Science
   • Why learn R ?
   • How to install R / R Studio ?
   • How to install R packages ?
   • Basic computations in R
2. Essentials of R Programming
   • Data Types and Objects in R
   • Control Structures (Functions) in R
   • Useful R Packages
3. Exploratory Data Analysis in R
   • Basic Graphs
   • Treating Missing values
   • Working with Continuous and Categorical Variables
4. Data Manipulation in R
   • Feature Engineering
   • Label Encoding / One Hot Encoding
5. Predictive Modeling using Machine Learning in R
   • Linear Regression
   • Decision Tree
   • Random Forest

Let’s get started !

Note: The data set used in this article is from Big Mart Sales Prediction.

1. Basics of R Programming

Why learn R ?

I don’t know if I have a solid reason to convince you, but let me share what got me started. I have no prior coding experience. Actually, I never had computer science in my subjects. I came to know that to learn data science, one must learn either R or Python as a starter. I chose the former. Here are some benefits I found after using R:

1. The style of coding is quite easy.
2. It’s open source. No need to pay any subscription charges.
3. Availability of instant access to over 7800 packages customized for various computation tasks.
4. The community support is overwhelming. There are numerous forums to help you out.
5. Get high performance computing experience ( require packages)
6. One of highly sought skill by analytics and data science companies.

There are many more benefits. But, these are the ones which have kept me going. If you think they are exciting, stick around and move to next section. And, if you aren’t convinced, you may like Complete Python Tutorial from Scratch.

More :https://www.analyticsvidhya.com/blog/2016/02/complete-tutorial-learn-data-science-scratch/

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