In this guide, we will discuss the Data Warehouse Concepts in Modern World, and how it is helpful in today’s data-driven world.
Understanding the Data Warehouse Concepts needs a broad diversity of knowledge about data, data processing tools, and the modern system.
The data warehouse theory (DW) is basically known as enterprise data warehousing (EDW) and its concepts or systems used for data analytics, forecasting, and data mining of big data for business operations.
This data processing and storing widely relies on two main approaches ETL (Extract, transform, load), and ELT (extract, load, transform) to build a big data system.
Enterprise data warehousing is a centralized system or a container of mixed data from one or more distinct data sources.
The data passes through operational storage which requires data cleaning and additional operations to ensure quality before entering Data Warehouse.
In the following part, we are going to see data warehouse architecture and a few Data warehouse concepts in detail.
What is ETL in Data Warehouse Concepts?
Although it’s ETL or ELT, the data transformation and integration process needs the following three very important actions:
Extract: Extraction refers to picking the source data from the primary database or data source and this comes first in warehouse concepts.
Transform: Transformation relates to the process of modifying the structure of the data for integrating it with the target data system and the other available data in that system.
Load: Loading regards the process of collecting the information into a data storage system and this also very crucial part in warehouse concepts.
ETL Based Data Warehouse
ETL is the combination of three data warehouse concepts that apply data staging, data integration, and access layers on stored data to perform key functions.
The staging layer stores raw data which is an extract from each of the disparate sources and data systems.
The integration layer integrates the assorted data by converting the data into the staging layer and then store the transformed data in the operational database.
The integrated data then migrated to another database called the data warehouse datastore.
Then the data is organized into hierarchical collections called dimensions and aggregated data.
The combination of data and dimensions known as a star schema and on the other part the access layer helps users to retrieve data.
The central source of data is cleansed, transformed, classified, and constructed for handlers and other business professionals for better data operations.
Related Article: Apache Spark Architecture – Detail Explained
ELT Based Data Warehouse
ELT data warehousing avoids the separate ETL tools for data transformation and it keeps a staging area inside the data warehouse itself.
In the ELT the data gets extracted from different sources and then directly loaded into the data warehouse before any transformation occurs.
All fundamental transformations are then managed inside the data warehouse to build up the Data Warehouse Concepts.
In the end, the manipulated data goes loaded inside destination tables in the identical data warehouse.
The basic features define the data in the data warehouse that include subject orientation, data integration, time-variant, nonvolatile data, and data granularity, etc.
Related Article: What Is Big Data? In Modern World
Basic Features of Data Warehouse
1. Data Aggregation
Data aggregation is the process of gathered and expressed raw data in a summary form for correct data analysis.
Data aggregation is used to combine all types of data into a single system like data marts at different levels of abstraction.
It is used for finding the sales of products or services at a regional level, city level, and at last the store or workstation level.
The analyst starts the data exploring by drilling and slicing the data from the big part to the small data points.
2. Subject-oriented Data
Unlike operational systems, the data in the data warehouse turn throughout the subjects of the enterprise.
Subject orientation is the understanding of data and exploration but it is not the database normalization.
The subject introduction can be useful for decision making and gathering the required objects is called subject oriented.
3. Integrated Data
The data obtained in the data warehouse is integrated that comes from various operational systems with consistencies.
Consistencies involve physical attributes of data, naming conventions, measurement of variables, encoding structures, etc.
Data integration involves combining data continuing from many sources and providing users with a consolidated view of it.
4. Time-variant Data
DW stores historical data from operational systems, the data reflect current values as they support day-to-day operations.
data warehouse handles huge data it represents data over a long time range up to 10 years.
It is proposed for data mining and prediction of future perspectives based on customer purchasing patterns like current and past purchases.
The granularity of time variance of changing data over time depends upon data values like periodic inspection (hourly, daily, weekly, monthly).
5. Nonvolatile Data
The data in the data warehouse is the historical or old data which is read-only which means nobody can update, created, or deleted it.
Nonvolatile data is the information which is not able to modify or update or delete after creation but it can access or read for future purpose.
Data Warehousing Approaches
1. Online analytical processing (OLAP)
Online analytical processing or OLAP is a Data warehousing approach to analyze multidimensional data from multiple sources and perspectives.
OLAP applications are widely used for report writing and data mining operations.
It is characterized by a comparatively low volume of transactions and processing.
OLAP systems mostly have a data latency of minimum hours as compared to data marts and the latency might increase in some cases.
There are three basic multidimensionality operations in OLAP like Slicing & Dicing, Drill-down, Roll-up or Consolidation.
2. Online transaction processing (OLTP)
OLTP or Online transaction processing is described a large number of short on-line transactions using INSERT, UPDATE, DELETE of data.
OLTP systems feature fast query processing and keeping data integrity in multi-access data environments.
The effectiveness of OLTP is estimated by the number of transactions per second with detailed and current data.
OLTP information systems typically help and manage transaction-oriented applications like payment processing, online purchasing, etc.
OLTP used to refer to a fast processing system that responds immediately to user requests with the goal of availability, speed, concurrency, and recoverability.
3. Data Mart
The data mart is a subset of the data warehouse and is usually determined by a specific team or business plan.
Data marts are developed and managed by a single department inside an organization and the sources could be inside operational systems or external data or a central data warehouse.
Each department or business unit is recognized as the owner of its data mart with all data, hardware, and software.
Every Data mart allows each department to separate the use, manipulation, and development of their own data.
Datamart is a simplistic form of a data warehouse that focuses on a single functional area.
4. Predictive Analytics
Predictive analytics use for finding and quantifying hidden patterns in the data using combined analytical models for predicting future outcomes.
Predictive analysis is different from OLAP in that OLAP focuses on historical data analysis and it widely focuses on prediction and forecasting of future problems.
The data warehousing concepts are massively essential to handle a huge amount of data for a wide variety of business operations.
The data warehouse is the dumping place where the huge historical information gets stored at the disposal for future reference.
Historical and past data generated from businesses crucially use in OLAP and Predictive analysis.
Related Article: What Is The Architecture Of Hadoop?
Meet our Analytics Team, a dynamic group dedicated to crafting valuable content in the realms of Data Science, analytics, and AI. Comprising skilled data scientists and analysts, this team is a blend of full-time professionals and part-time contributors. Together, they synergize their expertise to deliver insightful and relevant material, aiming to enhance your understanding of the ever-evolving fields of data and analytics. Join us on a journey of discovery as we delve into the world of data-driven insights with our diverse and talented Analytics Team.