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Q1 : Define Data Warehouse ?

A1 : An Enterprise structured repository of subject-oriented, time-variant, historical data used for information retrieval and decision support. The data warehouse (DWH) stores atomic and summary data

  • Subject Oriented
  • Non Volatile
  • Integrated
  • Time Variant

Q2 : Difference between Data Warehouse vs OLTP

A2 :

Property

Response Time

Operation

Nature of Data

Data

Organization Size

Data Source

Activities

Operational (OLTP)

Sub Seconds to Seconds

DML(INSERT, UPDATE, DELETE)

30-60 Days

Applications

Small to Large

Operational, Internal Processes

Processes

Data Warehouse

Seconds to hours

Primarily read (SELECT)

Snapshots over time

Subject, Time

Large to Very Large

Operational , Enternal Process

Analysis

Q3 : Data Warehouses vs Data Mart

Property

Scope

Subject

Data Source

Size(typical)

Implementation Time

Data Warehouse

Enterprise

Multiple

Many

100 GB to > 1TB

Months to Years

Data Mart

Department

Single-Subject, LOB (Line of Business)

Few

< 100 GB

Months

Q4 : Define Operational Data Store

Stores tactical data from production systems that are subject-oriented and integrated to address operational needs (OR)

An operational data store is used for operational reporting and as a source of data for the Enterprise Data Warehouse. (OR)

An operational data store (ODS) is an alternative to having operational decision support system (DSS) applications access data directly from the database that supports transaction processing (TP). It is kind of transaction processing system. (OR)

An operational data store (ODS) is a central database that provides a snapshot of the latest data from multiple transactional systems for operational reporting. 

Q5 : Relational Database vs No SQL

Relational Database

Table based data structure

Requires predefined schema

Vertically scalable

Mostly Expensive

No SQL

Non-relational database

Doesn’t require predefined schema

Horizontal Scaling

Cost Efficient

  • NoSQL tends to be a better option for modern applications that have more complex, constantly changing data sets, requiring a flexible data model that doesn’t need to be immediately defined. Most developers or organizations that prefer NoSQL databases, are attracted to the agile features that allow them to go to market faster, make updates faster. Unlike traditional, SQL based, relational databases, NoSQL databases can store and process data in real-time.
  • While SQL databases do still have some specific use cases, NoSQL databases have many features that SQL databases are not capable of handling without tremendous costs, and critical sacrifices of speed, agility, etc.

Ref: MongoDB (No SQL) vs Teradata (Relational)

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