What is a Data Warehouse?

YYou would have manually gathered data from many sources and worked hard to make them consistent before loading them into your databases. Then, you would pick up the data from the database and process the huge volume using analytical tools to gain meaningful insights. All of these tasks can be done in a single place, which is a central repository for your data. This platform will become your organization’s single source of truth, complete with built-in analytical tools. There are many tools like Hevo available that load data into such platforms and automatically transform it.

In this blog, you will learn everything about a data warehouse, starting from its definition to the tools available in the market. This is a complete guide to help you get started with such a system.

What is a Data Warehouse?

A data warehouse is an aggregated collection of data from various sources. This makes it a single, central, and consistent data store that helps in the processes of data mining, data analysis, machine learning, artificial intelligence, and more. It serves as a repository of both current and historical information that has been collected. It is an information system that forms the core of an organization’s business intelligence infrastructure.

It acts as the central data store within your company. There is a need for such a system in all enterprises that want to make data-driven decisions, as it serves as the “single source of truth” for all the data in the company. It stores data from multiple sources such as APIs, databases, cloud storage, and more, using the ETL (Extract, Load, Transform) process.

What is Data Warehousing?

Data warehousing is the combination of various processes and methods used for collecting and storing vast amounts of data for the purpose of query and analysis, in order to generate information and insights for business intelligence. 

Getting the data from the business transaction systems to the analytical systems (known as data migration) involves the ETL Process. The ETL process is used to Extract data from the source systems, Transform the data into a usable, query-able form and then Load said data to the destination database: the data warehouse. This may also involve extracting and combining different data sets from a variety of disparate sources into a singular cohesive form. This process is referred to as data integration.

Elements of Data Warehouse

• Relational Database – To store and manage data from multiple sources.
• ELT solution – Extraction, Loading, and Transformation solution to transfer the data for further analysis.
• Data mining capabilities – The data mining facilities available in traditional databases are also available.
• Statistical analysis and reporting – Built-in options for statistical analysis and preparing reports based on the insights achieved.
• Client analysis tools – These systems include visualization tools for presenting data to business users.
• Advanced Analytical tools – Various analytical applications to gain useful insights from the data without having to transport it anywhere

Cloud Data Warehouse and Modern Data Warehouse

Feature	Cloud Data Warehouse	Modern Data Warehouse
Data Storage	Ingests and stores data in the cloud from various sources.	A blend of best architecture patterns with all essential components to handle all types of data.
Scalability	Easily scalable, overcoming the difficulties of scaling in on-premises systems.	Designed to overcome challenges faced by different teams in an organization.
Complex Forecasting	Can handle complex forecasting.	Supports advanced analytics and machine learning to handle forecasting and complex tasks.
Management	Most cloud-based data warehouses are fully managed and easy to use.	Provisioning, scaling, and administration are fully automated, reducing complexity.
Cost Efficiency	Cost-efficient, aside from scalability.	Not explicitly mentioned, but automation and streamlined workflows help in efficiency.
Ease of Use	Easy to use and manage.	Self-serviced ETL service available, with SQL, machine learning, and spatial processing support.
Key Characteristic	Primarily focused on scalability, ease of use, and cost-efficiency.	Streamlined data workflows and reduced delays and complexity when different teams interact with it.

Data Warehouse Architecture

The general architecture is a three-tier architecture with a bottom, middle, and top tier.

Bottom tier

This is the first layer in the Architecture that has a Data Warehouse server that typically gathers data from different sources and stores them in a relational database. The process of Extract, Transform, and Load (ETL) or Extract, Load, and Transform (ELT) are employed for collecting, cleansing, and transforming data from multiple sources.

Middle tier

This middle tier generally has the OnLine Analytical Processing (OLAP) server. This is available to increase the query speed. There are different types of OLAP models that are preferred depending upon the type of database used. The three types of OLAP models are:

• ROLAP -Relational Online Analytical Processing
• MOLAP -Multidimensional Online Analytical Processing
• HOLAP -Hybrid Online Analytical Processing

Top tier

The topmost tier of the architecture consists of reporting tools for the users to conduct data analysis on the available data. The top tier is mostly the front-end user interface that directly connects with the users.

Schemas in Data Warehouse

The two most important Schema available are Star Schema and Snowflake Schema.

Star Schema

The Star Schema provides the simplest way of organizing data in a warehouse. The centre of the Star Schema can have one or more Fact Tables indexing a series of Dimension Tables. Dimension Tables are used to describe Dimensions; they contain Dimension Keys, Values, and Attributes.

The goal of the Star Schema is to separate the numerical “FACT” data that relates to a business from the descriptive or “DIMENSIONAL” data. 

Fact data can include information such as price, speed, weight, and quantities, that is, data presented in a numerical format. Dimensional Data can include uncountable things such as colours, geographical locations, salesperson names, employee names, etc., alongside the numerical information. 

The Fact Data is organized inside the Fact Tables, while the Dimensional Data inside the Dimension Tables. The Fact Tables form the integration points at the centre of a Star in a Star Schema. 

Snowflake Schema

This type of schema takes the shape of a Snowflake. The goal of the Snowflake Schema is to normalize the denormalized data of the Star Schema. The Snowflake structure materialized when the dimensions of a Star Schema are detailed and highly structured, having several levels of relationship, and the child tables have multiple parent tables. The Snowflake Schema solves some of the common problems associated with the Star Schema. 

You can see the Snowflake Schema as a “multi-dimensional” structure. At the core of a Snowflake Schema is Fact Tables that connect the information contained in the Dimension Tables, which in turn radiate outwards like the Star Schema. However, the Dimension Tables in the Snowflake Schema are divided into more than one table, creating a Snowflake pattern.

The Dimension Tables are turned into more than one table until they are completely normalized. 

Comparing Star and Snowflake Schema

The following chart summarizes the differences between the Star and Snowflake Schemas by comparing the Star and Snowflake Schema across numerous factors:

Star Schema	Snowflake Schema
The Star Schema contains the Fact Tables and the Dimension Tables.	The Snowflake Schema contains the Fact Tables, Dimension Tables, and the Sub-Dimension Tables.
It is a top-down model.	It is a bottom-up model.
Queries execute faster in the Star Schema. This is achieved using the Star Join Query Optimization technique. Tables can be connected with Multiple Dimensions.	Execution of queries takes a longer time in the Snowflake Schema than in the Star Schema. The centralized Fact Table in the Snowflake Schema is less likely connected with Multiple Dimensions. The longer execution time is also caused by the many number of foreign keys.
There is no normalization in the Star Schema.	In the Snowflake Schema, there is both normalization and denormalization.
It has a very simple design.	This Schema has a complex design because of the Sub-Dimension Tables.
The Star Schema has a low query complexity.	The query complexity of the Snowflake Schema is higher than that of the Star Schema.
It’s easy to understand the Star Schema.	It’s difficult to understand the Snowflake Schema.
It only has a few number of foreign keys.	It has a high number of foreign keys.
The Star Schema has a high data redundancy, which makes it a bit difficult to maintain and change.	The Snowflake Schema has a low data redundancy, which makes it easier to maintain and change.
The hierarchies for the Dimensions are kept in the Dimensional Table.	The hierarchies for the Dimensions are divided into separate tables.
In the Star Schema, only a single join creates the relationship between the Fact Table and the Dimension Tables.	In the Snowflake Schema, many joins are needed to fetch the data.
It has a simple database design.	It has a complex database design.
Faster cube processing.	Cube processing may be slow due to the complex join.
It is suitable for data marts that have simple relationships (1:1 or 1:many).	Suitable for use in the Data Warehouse core for simplifying complex relationships (many:many).
Should be used when the Dimension Table has less number of rows.	Should be used when the Dimension Table has a relatively big size because it reduces space.

Data Warehouse vs Others

Data Warehouse vs Database

Feature	Data Warehouse	Database
Purpose	Designed to store data from various applications and support analytics.	Primarily focused on fast querying and transaction processing.
Analytics	Has built-in analytics tools to perform complex data analysis.	Does not support analytics.
Data Storage	Stores data from multiple applications under a single unit.	Limits storage to a particular application.
Data Focus	Collects both real-time and historical data for analysis.	Focuses mainly on real-time data updates.
Key Difference	Enables analysis of real-time and historical data for deeper insights.	Mainly focused on real-time data updating and transaction processing.

Data Warehouse vs Data lake

Feature	Data Warehouse	Data Lake
Subset of	It is a complete storage system.	A data lake is a subset of a data warehouse.
Data Collection	Collects raw data from various sources and creates a single source of truth.	Also collects raw data, but without any predefined schema.
Schema	Structured using predefined schemas for analytics.	Does not have any predefined schema.
Technology	Typically built using traditional relational databases.	Often built on big data platforms like Apache Hadoop.
Purpose	Designed for analytics using structured data with clear organization.	Stores raw, unstructured data for future analysis.

Data Warehouse vs Data mart

Feature	Data Warehouse	Data Mart
Scope	Acts as a large data storage unit for all business data.	A subset of a data warehouse focused on a specific line of business.
Purpose	Helps an organization make informed decisions by integrating data from various sources.	Provides narrow insights for a particular business stream or department.
Data Processing	Processes data from various departments and functions.	Saves time by processing only data related to a specific business function.
Usage	Used for organization-wide analytics and reporting.	Used for specific business line analysis, often reducing complexity and time.

OLAP and OLTP in Data Warehouse

Online Analytical Processing (OLAP) is a software tool designed to perform analysis on large volumes of data at high speed. While Online Transactional Processing (OLTP) helps in processing real-time queries by multiple users simultaneously over the internet. OLAP is used for analytics, while OLTP is used for transactional purposes.

OLAP’s main function is the multidimensional analysis of both real-time and historical data. It is mainly employed in data mining and Business Intelligence (BI) applications. Complex and predictive analyses can be carried out easily using OLAP. Key business reporting functions like financial analysis, forecast planning, and budgeting are common tasks for OLAP.

For processing real-time data transactions quickly and accurately, OLTP is used. It is commonly found in ATM systems, e-commerce software, online bookings, reservation systems, credit card processing, and more.

Benefits of a Data Warehouse

Data Warehousing have a lot of benefits, to highlight the two most important benefits.

• Better data: This solves the problem of inconsistent data and data accessibilty. Data Warehouse becomes a single source of truth with consistent and relevant data from various data sources.
• Faster decisions: The ready to use data with inbuilt analytics tool makes it the best option to make factual fast decisions. The decision makes can strongly rely on the decisions as they are from complete , high quality data ,hence reducing the risk.

Data Warehousing Tools

There a lot of Data Warehousing Tools, here are few examples of Data Warehousing Tools.

Amazon Redshift

Amazon Redshift is a simple, fast, and efficient Data Warehousing Tool that makes it possible to analyze data using simple SQL queries with existing Business Intelligence Tools. It can run complex analytical queries by using processes such as high-performance computing, parallel execution, uniform query optimization, and columnar storage. 

Amazon Redshift uses the AWS platform and requires administrators to configure the environment by creating nodes and clusters. Hence, it is based on a clustered methodology. These clusters help in providing capacity, monitoring, and backing up the data warehousing and also to apply patches and maintain the software. These clusters can be managed based on the user’s requirements with ease. As a beginner, users can start with either a small or single node cluster and then expand it to larger multi-node clusters as their requirements increase.

Google BigQuery

Google BigQuery is a Data Warehousing Tool that is serverless, cost-effective, highly-scalable, and has machine learning built into it and uses the Business Intelligence Engine for its operations. It analyzes petabytes of data using the ANSI SQL language at very fast speeds, offers insights and solutions from data across clouds with a flexible architecture, is able to store and query massive data sets in a cost-effective and efficient way.

Google BigQuery also has a flexible, multi-cloud analytics solution that is powered by Anthos to analyze data across different cloud platforms. This is called the BigQuery Omni(private alpha). Google BigQuery also has a natural language interface for petabyte-scale analytics to operate on data sources. It helps to improve the productivity of different Business Intelligence teams and manage data from tools like Sheets and chatbots and custom applications.

Snowflake

Snowflake is an analytical Data Warehousing Tool that provides a faster, easier to use, and more flexible framework more than a normal data warehousing. Snowflake offers a complete SaaS (Software as a Service) architecture because it runs completely on the cloud. 

In order to use Snowflake, no hardware or software needs to be externally or internally installed or configured. Maintenance is also handled directly by Snowflake. It can only be run using public cloud infrastructure as it uses the virtual compute instances for its computational needs and storage service for persistent storage. All updates and patches are taken care of by Snowflake only and no other software is responsible for installing these features.

Learn more about:

• Top open source ETL tools list
• Optimizing Data Warehouse Cost
• Operational Data Store

Conclusion

From this blog, you would now know entirely about Data Warehouse starting from what it is, it’s architecture, benefits, schema, tools. This blog will be your forever guide to get started with Data Warehousing.