Basics of Data Warehouse: Types and Working

Data warehouse

 

A data warehouse is simply a large collection of data or a centralized information repository. Transactional systems, relational databases and a multitude of other sources ensure regular data flow.

 

Business intelligence tools and other SQL clients make the data accessible to business analysts, data engineers and data scientists. Businesses need to have ample expertise over business analytics tools and applications in order to maintain an edge over their competitors.

 

Decision making becomes a lot more well-strategized by utilizing reports, dashboards and insights provided by big data analytics tools. These reports and insights are powered by data warehouses that have the primary function of storing large amounts of vital data in the most effective and efficient possible manner. Data warehouse enables quick and effortless delivery of query results to a surprisingly large number of users simultaneously.

 

Some of the advantages of using data warehouse are; 

 

1. Supports decision making to an unprecedented extent 

2. Combines a multitude of sources to provide consolidated information

3. Offers consistent, consolidated and precise data 

4. Maintains the required separation between transactional databases and analytics processing 

 

As the data collected is consistent and relevant to its core, organizations no longer need to worry about the accessibility or the quality of data.  The fast and accurate delivery data helps organizations in ensuring the timely delivery of precise and productive results.

 

A standard, regular database cannot run powerful analytics on petabytes of historical data as efficiently as a Data warehouse does. 

 

(Must Read: data lakes vs data warehouse)

 

Basics of Data warehouse architecture

 

Data warehouses commonly have a three-tier architecture divided into a bottom, middle and top tier with specific roles and pain-points.

 

• Bottom tier: This tier usually contains a data warehouse server as a relational database system to collect, organize and transform the data from a variety of sources through ETL (Extract, transform and load) process or ELT (Extract, load and transform) process.

 

• Top tier: It has a front-end user interface so that end users are enabled to conduct ad-hoc data analysis on their business data.

 

• Middle tier: Online analytical processing server at this tier enables quicker delivery of query results. 

 

There are three distinct types of OLAP models that can be utilized in this tier, usually known as ROLAP, MOLAP and HOLAP. The existing database system determines the type of OLAP model used for data extraction.

 

OLAP in a data warehouse

 

Online analytical processing performs the primary role of analysis at unbelievably high speeds on surprisingly large amounts of data stored on unified, centralized data repositories.

 

These tools are being widely used for data mining, complex analytical calculations, financial analysis, budgeting and forecast planning. These tools are adept at performing multidimensional analysis of data in a data warehouse, including both historical and transactional data. 

 

Since when data warehouses have been around?

 

Business intelligence tools and applications have been making use of data warehousing technologies since the past three decades in order to aggregate data from a variety of sources into a centralized data repository for data analysis. 

 

Latest data types and data methods have been contributing towards the constant evolution of data warehousing tools and applications. Earlier, a mainframe computer hosted a data warehouse, which primarily focused on extracting data from a number of sources, preparing and organizing data into a relational database.

 

Now, data warehouses are hosted in the  cloud or a specific appliance. More recently, data visualization techniques and presentation tools are adding to the analytics functionality of a data warehouse. (Source)

 

 

How does a Data warehouse work?

 

A data warehouse basically contains lots of databases each of which includes data organized into tables and columns. Each column further holds vital data descriptions, including integer, string or data field.  

The folder-like schema consists of these tables, storing data. The schemas are utilized by query tools and applications in order to provide the suitable data table to be used for getting insights. 

 

Schemas in a Data warehouse

 

Schema structures are basically ways in which data can be structured within a database in a Data warehouse. Star Schema and snowflake schema constitute the most prominent types of schemas, which have a vital role in the shaping of the design of the data structure. 

 

1. Star Schema 

 

Star Schema is the most usual and regular schema which offers high speeds while querying to its users. Moreover, it is a one-fact table joined to several denormalized dimension tables. 

 

2. Snowflake schema 

 

It is a one fact table joined to a number of normalized dimension tables, that are in turn containing child tables within. Snowflake is not widely popular as the most used schema type largely due to the fact that it compromises query performance to some extent, though offering low levels of data redundancy. 

 

 

Different types of data warehouses

 

There are several data warehouses present, while three are the most prominent ones. 

 

1. Cloud data warehouse 

 

Built specifically to be operated in the Cloud, it is widely gaining popularity over the last five years.  The biggest reason behind a cloud data warehouse gaining currency is the willingness of more and more organizations to use cloud services and manage a cut in their costs for maintaining an on-premises data warehouse. 

 

Besides being offered to users as a managed service, the customer is rescued from the need of making an upfront investment in physical data warehouse infrastructure.

 

Moreover, as the hardware and software required for the cloud data warehouse is managed by the cloud company, the customer can stop worrying about ways to manage or maintain the data warehouse solution. 

 

2. On-premises or license data warehouse software 

 

Usually this proves to be more expensive than a cloud data warehouse solution, as an organization first needs to buy a data warehouse license and then install a data warehouse on their own on-premises data center footprint. 

 

Though the organization has to incur a higher amount for this data warehousing solution, it comes with its own advantages. 

 

Institutions or entities that need to have more strict control over their data or those that have strict data privacy standards and need to abide by them, are undoubtedly bound to choose a license data warehouse solution for their requirements. License data warehouse solutions are the go-to option for businesses focusing on stricter data security- control and privacy.

 

3. Data warehouse appliance 

 

It stands between the cloud and on-premises solutions regarding operational costs, speed of results delivery, scalability, reliability and management control. 

 

It is a package of CPU's, storage applications, operating system along with data warehouse software. It comes as a pre-integrated bundle that businesses can join to their network and begin using immediately. 

 

Ending Note 

 

While reaching to the end of the blog, we can successfully state that data warehouse solutions have become an integral part of Business Intelligence tools.

 

With their specific target areas data warehouses are undoubtedly proving to be a huge helping hand for corporate decision-makers in focusing on the most accurate data insights.

 

These systems maintain the standardization of data, reducing the scope of potential error. Arriving at the best data-driven strategies has become increasingly easier owing to the high speeds and efficiency offered by data warehousing solutions.

 

Extracting, organizing and transforming historical data using a data warehouse enables business leaders to decide on the most appropriate and beneficial strategy that can improve their bottom line, without having to compromise on costs, efficiency and data security.