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12 Dec 2022

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How to Build a Data Warehouse: Steps, Approaches & Tools

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Home > Blog > Data Warehouse Development: Steps, Approaches & Use Cases 12 Dec 2022 We can answer all your questions about the DWH implementation plan, project cost, DWH architecture & technology stack. We can help. Book your technology consultation today to consider your case and build a tailored pack.

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Data warehouses are an important part of a business.

As a developer, you'll be involved in the data warehouse in one way or another: creating it, designing it, accessing it, or working on software that provides data to it.

Learn all about data warehouses in this guide.

What Is a Data Warehouse?

A data warehouse is a type of database that's designed for reporting and analysis of a company's data. It collects data from one or many sources, restructures it in a specific way, and allows business users to analyse and visualise the data.

Why Do We Need a Data Warehouse?

You've probably heard of databases that are used for storing data for applications or websites. These databases specialise in recording transactional data: things that happen during the operation of a business. Some examples are:

Hotel reservations

Airline bookings

Student course enrolments

Posts on a forum

Sign-ups and sales on an eCommerce website

If we have these databases, why do we need a data warehouse?

Why don't we just run analytics on the normal database?
The main reason is performance.
These transactional databases (the ones that store the data for business operations) are often changing regularly. New data is added every second (depending on the size of the application and who is accessing it).

The design of the database is optimised for regular changes: INSERTs and UPDATEs. The design is often normalized to avoid redundant data and to improve data integrity.

These databases are not often optimised for SELECT statements (viewing data). They allow data to be viewed, but the design is not focused on selecting a lot of data in a short amount of time.

A data warehouse is designed in a different way. The design is made to optimise the performance of SELECT queries across more data. The INSERTs and UPDATEs happen rarely (often overnight as a big batch) and the SELECT statements happen all the time. This means it performs better when viewing data.

Also, the performance of a database in a live system is important. Businesses want their websites and applications to be fast, and so do customers. If your database is allowing customers and users to place orders or enrol or create forum posts (or whatever it is designed to do), allowing business users to generate reports on this data at the same time will put extra load on the database and slow everyone down.

Having a separate database allows the normal transaction database to perform well and the analysis of data to be done without impacting it.

So, the two main reasons to use a data warehouse are performance-related:

Allow business users to analyse data quickly using a specialised database design

Prevent the main transactional database from slowing down due to data analysis

Data Warehouse vs Database (OLAP vs OLTP)

There are many differences between a traditional database that you might use for a website or application and a data warehouse.

Two terms you might see when referring to these databases are OLAP and OLTP.

OLAP stands for OnLine Analytical Processing, and represents databases that work like a data warehouse: focused on analysis instead of inserts and updates.

OLTP stands for OnLine Transaction Processing, and represents databases used for transactional data (inserts and updates), such as websites and applications.

The differences between OLAP and OLTP, or regular databases and data warehouses, can be summarised by this table:

What does each of these points mean?

Purpose

As I've mentioned above, the purpose of a database is to store transactional data from a system, such as an application or a website. The purpose of a data warehouse is to allow people to analyse the data from one or more systems.

Design Focus

The focus of the design of a database is to be optimised for regular and fast INSERT and UPDATE statements, so data is changed easily and the users don't experience a delay when using the system. The focus of a data warehouse design is for fast SELECT statements, to allow data to be viewed quickly.

Table Rows and Columns

In a regular database, there are often many tables compared to a data warehouse. These tables have fewer columns and rows (they still may have a lot, just less compared to a data warehouse).

Data warehouses, on the other hand, have a smaller number of tables, but these tables often have more columns and rows in them.

Table Design

The design and relationships of the tables in a regular database are usually normalised. This means they follow a standard set of rules for what they represent and how they are related, to ensure data integrity and reduce repetition.

In a data warehouse, the tables are often designed using a "fact and dimension" structure. This means there are one or more tables that store transaction records, and many tables that store data about the transactions.

I'll go into more about this later in this guide.

Data Source

Regular databases usually have only one system that populates the data. This could be a website or an application or something else. Data warehouses can be populated from one system, but are often populated from many systems in a company. This is because companies often have many systems that each focus on one area of their business, and an enterprise-wide data warehouse that aggregates them all together.

Populated By

Regular databases are populated by the use of the related application. Records are added, updated, and possibly deleted as the system is used. Data warehouses are often populated using custom-built scripts that extract data from these systems and add the data to the data warehouse.

Concurrent Users

A regular database needs to support a large number of concurrent users. This could be hundreds if it's an internal company application. It could be thousands or even millions if it's an external-facing website.

A data warehouse, on the other hand, only needs to support a select number of users. This is usually a small number, depending on the company, and limited to those who make use of the data in the data warehouse.

Creating a Data Warehouse

We've talked about what a data warehouse is and how it's different to a regular database. I've also written about creating a database, which starts with the design.

But how do you create and populate one?

Design the Data Warehouse

The first step is to design the data warehouse. There are several types of designs you can follow, which I'll cover shortly. The process works in a similar way to creating a regular database, and at a high level, the steps are:

1. Determine the purpose of the data warehouse

2. Determine what systems the data comes from

3. Design a table for your facts - the numerical measurements that are being reported on

4. Design tables for your dimensions - the attributes of these facts or measurements

There's a lot that goes into these steps, and I'll cover some of it later in this article. At the end of the design steps, you should have a diagram or a structure of how your tables will look.

Populate the Data Warehouse

Now you've got your design, how do you add the data?

Getting the data into the data warehouse involves three steps: Extract, Transform, and Load (or ETL for short).

1. Extract: get the data from the system that captures it (the source system).

2. Transform: make adjustments to the data to fit your data warehouse (formatting dates, looking up other tables, etc).

3. Load: add the data to your data warehouse tables

This can also be done as ELT, or Extract Load Transform, which means the transformation of data is done after it is loaded.

These steps are often done using a set of SQL scripts, or a tool that's purpose-built for performing this process. Whether you use scripts or a tool, they need to be created manually, and this process can take some time due to the analysis and development involved.

Reporting and Business Intelligence

Once the data is loaded into the data warehouse tables, it needs to be made available to other people in the company.

While you and I might be familiar with SQL and able to run queries on the database, the majority of people who want to see this data will not know SQL.

So, how do we show the data to them?

We can create reports on this data.

The ability to create reports on this data is called Business Intelligence. There are many tools that allow you to create reports on data warehouses. These tools will allow business users to:

View reports you have created

Maybe create their own reports

Filter report data based on criteria (e.g. date range or region or category)

Display data in charts (bar charts, pie charts, and much more)

Display data summarised in tables

The development team often creates these reports in specialised tools that the business users can access. Some tools also allow business users to create their own reports, saving everyone time.

Data Warehouse Designs

We've mentioned the designs of data warehouse briefly in this article, and that they are different to regular databases.

While regular databases usually follow a process of normalisation to get to an ideal design, data warehouses have several different design approaches you can follow.

What are they?

Star Schema

A star schema is a type of data warehouse design. It's called a star schema because it looks like a star. It has a single fact table in the centre, and several dimension tables linked to it.

A fact table is a table that stores a row for each value you want to measure. For example, it could be a sale of a product to a customer. Fact tables are usually quite large: they can have millions or billions of rows.

A dimension table is a table that stores reference information about a fact. It can store an entity, such as a customer or product, or it can store a concept such as a date. They are not normalised as they contain a lot of redundant data, but this is OK as it is a deliberate design that helps to speed up SELECT queries.

Snowflake Schema

A snowflake schema is a type of data warehouse design that has a single fact table in the centre, several dimension tables linking to the fact, table, and further dimension tables linking to the dimension tables.

It's called a snowflake schema as the design often looks like a snowflake: a large centre with smaller nodes or branches at certain points.

Star Schema vs Snowflake Schema

A star schema and snowflake schema are two of the most popular designs for data warehouses.

What is the difference between a star schema and a snowflake schema?

Let's take a look.

Whether you decide to use a star schema or a snowflake schema is up to you and your team. If you're just starting out, I would recommend using a star schema, as the concept of a single dimension is easier to understand.

Kimball and Inmon Methods

You might have heard about or read two different approaches to creating a data warehouse: the "Kimball" method and the "Inmon" method. These methods are not ways to design a database. They are ways to create a data warehouse for a company, or the steps that are followed and the order they are done in.

Kimball Method

The Kimball Method, named after Ralph Kimball, suggests a "bottom-up" approach.

What does that mean?

It means you create a small data warehouse for one area of the business at a time, and eventually integrate several areas into one large data warehouse. This allows one area of the business to get the benefits of a data warehouse sooner, instead of waiting until the entire data warehouse is done before seeing it.

The process generally looks like this:

1. Choose one business area (such as Sales)

2. Design the data warehouse for this business area (e.g. star schema or snowflake schema)

3. Extract, Transform, and Load the data into the data warehouse

4. Provide the data warehouse to the business users (e.g. a reporting tool)

5. Repeat the above steps using other business areas

6. Once all business areas are done, design a data warehouse to combine them all.

7. Populate the main data warehouse from the smaller data warehouses

8. Provide access to the main data warehouse to business users.

Inmon Method

The Inmon Method, named after Bill Inmon, suggests a "top-down" approach. This means that the requirements are gathered and data warehouse is developed based on the organisation as a whole, instead of individual areas like the Kimball method. It results in a single data warehouse without the smaller data warehouses in between, but it may take longer to see the benefits.

The process generally looks like this:

1. Design the data warehouse for the entire business (e.g. star schema or snowflake schema)

2. Extract, Transform, and Load the data into the data warehouse

3. Provide the data warehouse to the business users (e.g. a reporting tool)

Data Warehouse vs Data Mart

The term "data mart" is one you might hear when working with data warehouses.

What is a data mart?

A data mart is a subset of a data warehouse that applies to a specific business area. It works in a similar way to a data warehouse, and has a design that is specific to the business area it needs to support. A company may have several data marts, one for each area of the business.

So what's the difference between a data warehouse and a data mart?

A data warehouse applies to one or more business areas, often the entire organisation. A data mart applies to only one business area.

Data Warehouse vs Data Lake

A data lake is another term you'll hear when working with data warehouses.

What is a data lake?

A data lake is a large collection of raw data. It has not been transformed or processed for analysis, as there is no requirement for analysis yet. It's just a place to store data and make it available for future analysis.

How is this different from a data warehouse?

While both a data warehouse and data lake store data, the data warehouse has a specific design and is based on requirements. The data lake stores data without a specific design and without an identified need for analysis.

The benefit of using a data lake is that it's easy to access and make changes. The structure is less defined and the data is not processed, so there are less checking and updates to do.

Data Warehouse Concepts

There are many other concepts you might encounter when working with a data warehouse. Here are some that we haven't covered already:

Fact Table

A fact table is a table that stores a row for each value you want to measure. For example, it could be a sale of a product to a customer.

Dimension Table

A dimension table is a table that stores reference information about a fact. It can store an entity, such as a customer or product, or it can store a concept such as a date.

Slowly Changing Dimension

What is a slowly changing dimension?

It's a type of table that captures both current and historical data. This data changes over time and needs to be kept up to date in a data warehouse. One example is a customer record: their contact details or account status is not updated very often but it may change over time. A data warehouse needs to keep a history of these changes.

Conceptual Data Model

The conceptual data model shows the business objects that exist in the system and how they relate to each other.

It defines the entities that exist, which are not necessarily tables. Thinking of tables is too detailed for this type of data model.

Logical Data Model

A logical model is a more detailed version of a conceptual data model. Attributes are added to each entity, and further entities can be added that represent areas to capture data in the system.

Physical Data Model

The physical data model is the most detailed data model in this process. It defines a set of tables and columns and how they relate to each other. It includes primary and foreign keys, as well as the data types for each column.

These diagrams can be created manually in a data modelling tool. They are also often generated by an IDE from an existing database.

Conclusion

Data warehouses are an important part of a business as they allow a business to analyse and report on the data that it captures. The process for creating a data warehouse is where the developers come in, and they follow a specific design and build process to create and populate a data warehouse. Reporting tools are then used to analyse and view the data.

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Data warehouses are an important part of a business. As a developer, you'll be involved in the data warehouse in one way or another: creating it, designing it, accessing it, or working on software that provides data to it. Learn all about data warehouses in this guide.

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Having worked in data-driven organizations make you observe the significant amounts of data that are produced every day within the systems. This data needs to be combined and stored on a single platform, which can be used for business intelligence, analytics, reporting, and decision-making by many divisions within a firm. In contrast to past times, data now comes in many different formats and from several platforms or sources. In order to transform the data into useful information, it must be carefully integrated into a common platform called a data warehouse.

If you are designing a data warehouse for your company, this guide will help you achieve that with a few simple steps.

What is a Data Warehouse?

A warehouse of something is generally a space where you store large amounts of it in an organized fashion. And that is what a data warehouse is - a space to store a large amount of data.

A data warehouse is a centralized server/location where you store massive amounts of data sourced from different channels for analytics purposes. The practice has become essential among businesses given the increasing reliance on data to make business decisions.

Characteristics of a Data Warehouse

You can identify a data warehouse using the following characteristics -

1. Concept-oriented: Data warehouse presents information on a specific subject like inventory, etc., rather than talking about the operations. So, it gives you access to data around a particular theme, using which you can make business decisions and run operations.

2. Time-variant: Data in a warehouse gets organized w.r.t time intervals. It could be daily, weekly, monthly, or yearly. Another aspect is that the data you store at a particular time cannot be altered later.

3. Non-Volatile: Past data is stored in the records for future references. Thus, there is no immediate deletion once new data comes in. This property of a data warehouse is called non-volatility.

4. Integrated: Data from various sources gets integrated into one shared entity that facilitates consistency and reliability. Implementation of common conventions, formats, and codes makes it easier to structure, handle, and analyze data.

Data Warehouse supported functions

Data Consolidation - The data needs to be collected & consolidated from different data sources. Data consolidation is the combining, corralling, and storing of assorted data in a single place.

Data Cleaning - It is a process of removing corrupted, duplicate, and incorrectly formatted data within the data set.

Data Transformation - Once you have cleaned your data, you transform it into a format suitable for data storage and operations.

Data Loading - The next logical step is to load this data into your warehouse. Hence, every data warehouse must support data loading.

Data Updating - You must keep data updated with time. Thus, this is another functionality that a data warehouse cannot do without.

The architecture of a Data Warehouse

There are three types of data architectures that organizations can scope for their requirements.

1. Single-layer architecture

2. Two-layer architecture

3. Three-layer architecture

Let us look at how each is different from the other.
1. Single Layer Architecture
In a single-layer architecture, there is only one layer, i.e., the source layer. This layer represents the primary data sources and interfaces directly with the data warehouse layer. This architecture is essentially opted for by very early age startups/ businesses who have not organized their data operations for scaling. This architecture is not a recommended way of designing your warehouse.

In this architecture, the source layer delivers all the data directly to the warehouse. Result? It removes data redundancies. However, this approach is more error-prone, causing efficiency issues in the long run.

The source layer could be your legacy databases, any customer communication, and relationship management channels, or any other source connected to your business's information systems. The client layer of the system interacts with the Data warehouse directly for all query processing, data mining, reporting, and analytics.
2. Two-layer architecture
Although it is frequently referred to as two-layer architecture to highlight a division between physically accessible sources and data warehouses, in reality, it consists of four consecutive stages of data flow, i.e., source layer, data staging, data warehouse, and analysis. With a staging layer, data does not go directly from the data sources into the warehouse. Instead, It is cleansed and unified here so that only high-quality data gets stored in the data warehouse.

After the staging layer, this data moves to the data warehouse layer, where it reaches ready for BI operations. With the frontend reporting tools, you can thoroughly analyze this data to draw conclusions.
3. Three-layer architecture
When you adopt the three-layer architecture, you add an additional layer before the data finally gets to the warehouse. That layer is the Data Reconciliation layer. Data to this layer comes from the staging layer.

The Data reconciliation layer ensures that no error creeps in due to mapping or transformation mistakes. It avoids discrepancies like missing records, incorrect values, broken relationships across tables, etc. It creates a standardized reference data model for the whole enterprise. However, you need extra storage space for the additional layer introduced.

Additionally, the data warehouse supports division and organization into data marts. Data marts are specialized databases for each department within an organization. So, the sales division can have its own data mart, while the Finance department gets its own. This approach improves the operational efficiency of your database. Data marts can be introduced to single and two-layer architectures as well.

How to Design a Data Warehouse?

When it comes to data warehousing, there is no one-size-fits-all approach. The design should be according to your business requirements.

Some questions that you might want to ask yourself are -

1. What kind of data do we store?

2. What are our data sources?

3. What operations do we run on this data?

4. What type of data organization suits our business objectives & operations?

5. Is there any compliance risk?

6. How to keep our data secure from loss and cyber threats?

Once you answer these questions, a picture should emerge in front of you that will act as a catalyst to help you in designing a data warehouse.

In this process, you decide upon hardware specifications, staff training requirements, data sources, and a project plan.
1. Collect what you need
To set up the environment, you must gather your requirements right. The first thing is to gauge the required throughput accurately. Once you know what you need, you can start collecting the resources.

What would be your peak load? What would be your general load? Once you have sized that requirement, that is when you can start preparing your environment. It will help you quantify your hardware requirements like the number and power of CPUs, required memory, bandwidth, and disks.

A rough estimate for the number of CPUs could be arrived at using the formula - #CPUs = Max required throughput/200

Similar best practices and general formulas can be used as a general guideline to decide on other requirements.

Additionally, your physical environment needs development, testing, and production units to facilitate proper warehousing. And these units need to be separate from each other. This approach ensures data integrity, increased resource availability, better testing, and efficient production.
2. Database setup for data warehouse
Before building a warehouse, you need to have a blueprint of what goes where. Data modeling helps you visualize the organization of your data.

However, data modeling typically takes place at the level of data marts. Data marts are specialized databases that concern a specific department of your organization. All the design decisions happen at this level.

The first step usually is to define the user requirements and then move towards the implementation part of the data warehouse. The implementation involves choosing the schema and setting up other parameters defining the rules and resources available. In simple words, you create data definitions in this step.
3. Defining ETL
Now that you have completed the last step, you need to define the ETL (Extract, Transfer, Load) logic that you will employ for your warehouse.

Mappings are visualizations of the processes of how data is extracted and transformed between sources and targets. You can use operators to define data sources, targets, and transformations in these mappings.

As your ETL processes will have a long-term effect on the performance of your warehouse, your definitions must be nothing short of optimal.
Here is what ETL means -

Extract - Extract data from its source.

Transform - Transform into a suitable format.

Load - Load it into the data warehouse.

Data Analysis

As per the business requirements, multiple data points are essential to track. That is why you will be collecting data from numerous sources in a single place. However, that might make it hard to organize and analyze such data if not done optimally. That is where OLAP data cubes come in.

An OLAP data cube is a multi-dimensional data structure having its primary application as a Business Intelligence enabler. Data from your warehouse gets sent to these data cubes for efficient analysis.
4. Frontend and Reporting
The frontend is just as important as the backend. A good visualization tool (like Tableau) can help your end-users interact with data with ease. The next part of visualization is report generation. During analysis, the end-users can generate reports, facilitating them to draw conclusions about business data.

Your business reports -

Must be customizable.

Should be easily accessible through multiple channels.

Should have restricted access based on authority.

Data Warehouse Design Best Practices

Data warehousing is an ongoing process. And to get the best out of it, it is prudent for you to follow the industry best practices. Here are a few suggestions for you to get started.

1. Don't skip the initial work - The initial work while designing your warehouse might seem intimidating and useless, but it is quite the opposite. One should not go jumping right into the implementation part. Be methodical to ensure that your warehouse design solves the business problem you wish to address.

2. Data Quality - You must have proper standards & systems in place to ensure data quality in your organization. Your business might suffer eventual losses if you collect data that does not present you with the complete picture.

3. Query Optimization - Sub-optimally designed systems lead to SQL queries running into performance issues. You might face problems like slowing servers, obsolete data, etc. Some recommendations are - 1) Mirror resources for development, testing, and production. 2) Be specific while running your select queries.

4. Avoiding System Overloads - You should employ Database Diagnostic monitoring to prevent system overload by monitoring system performance and resource availability.

5. Performance Monitoring - You should monitor performance using various statistics. You can improve your warehouse performance using partitioning optimization (Partitioning pruning & Partition-wise joins), Query rewrites, indexes, and data compression.

6. Use resources optimally - You can use resources optimally via parallel processing of the queries.

7. Efficient Integrity Checks - They are resource-intensive thus checks should happen at the source of data instead of the data warehouse.

8. Backup your warehouse data - It becomes essential to successfully back up your warehouse data so that you can recover it later in case of a loss. As a data warehouse contains a massive amount of data, it becomes crucial that you have a backup strategy and design your system accordingly. You must prioritize what to back up and the frequency of such backups. Archive logs, recovery managers, etc., can be helpful.

9. Determine the acceptable standard - As an organization, you should be clear about your database standards in line with your business objectives.

10. Research and experiment with the agile approach in your warehouse design and development.

11. Use a data integration layer for better collation and data reporting to enable your business decisions.

Conclusion

Data Warehouse design is an evolving field. As data requirements of the organizations keep evolving, so will their design schemas and the corresponding best practices. Thus, in addition to what you have read here, it is suggested that you keep an eye on the evolving technologies that revolutionize the way we store and access data.

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Data Warehouse Architecture and Design: A Reflective Guide

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Having worked in data-driven organizations make you observe the significant amounts of data that are produced every day within the systems. This data needs to be combined and stored on a single platform, which can be used for business intelligence, analytics, reporting, and decision-making by many divisions within a firm.

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In today's rapidly changing corporate environment, organizations are turning to cloud-based technologies for convenient data collection, reporting, and analysis. This is where Data Warehousing comes in as a core component of business intelligence that enables businesses to enhance their performance. It is important to understand what is data warehouse and why it is evolving in the global marketplace.

In this article, we'll provide an overview of Data Warehouse - explore key concepts like data warehouse architecture, characteristics of data warehouse, what is data management, the benefits of data warehouse, and data warehouse applications.

What Is a Data Warehouse

Data warehouses serve as a central repository for storing and analyzing information to make better informed decisions. An organization's data warehouse receives data from a variety of sources, typically on a regular basis, including transactional systems, relational databases, and other sources.

A data warehouse is a centralized storage system that allows for the storing, analyzing, and interpreting of data in order to facilitate better decision-making. Transactional systems, relational databases, and other sources provide data into data warehouses on a regular basis.

A data warehouse is a type of data management system that facilitates and supports business intelligence (BI) activities, specifically analysis. Data warehouses are primarily designed to facilitate searches and analyses and usually contain large amounts of historical data.

A data warehouse can be defined as a collection of organizational data and information extracted from operational sources and external data sources. The data is periodically pulled from various internal applications like sales, marketing, and finance; customer-interface applications; as well as external partner systems. This data is then made available for decision-makers to access and analyze.

So what is data warehouse? For a start, it is a comprehensive repository of current and historical information that is designed to enhance an organization's performance.

Key Characteristics of Data Warehouse

The main characteristics of a data warehouse are as follows:

A data warehouse is subject-oriented since it provides topic-wise information rather than the overall processes of a business. Such subjects may be sales, promotion, inventory, etc. For example, if you want to analyze your company's sales data, you need to build a data warehouse that concentrates on sales. Such a warehouse would provide valuable information like 'who was your best customer last year?' or 'who is likely to be your best customer in the coming year?'

A data warehouse is developed by integrating data from varied sources into a consistent format. The data must be stored in the warehouse in a consistent and universally acceptable manner in terms of naming, format, and coding. This facilitates effective data analysis.

Data once entered into a data warehouse must remain unchanged. All data is read-only. Previous data is not erased when current data is entered. This helps you to analyze what has happened and when.

The data stored in a data warehouse is documented with an element of time, either explicitly or implicitly. An example of time variance in Data Warehouse is exhibited in the Primary Key, which must have an element of time like the day, week, or month.

Database vs. Data Warehouse

Although a data warehouse and a traditional database share some similarities, they need not be the same idea. The main difference is that in a database, data is collected for multiple transactional purposes. However, in a data warehouse, data is collected on an extensive scale to perform analytics. Databases provide real-time data, while warehouses store data to be accessed for big analytical queries.

Data warehouse is an example of an OLAP system or an online database query answering system. OLTP is an online database modifying system, for example, ATM. Learn more about the OLTP vs. OLAP differences.

Data Warehouse Architecture

Usually, data warehouse architecture comprises a three-tier structure.

Bottom Tier

The bottom tier or data warehouse server usually represents a relational database system. Back-end tools are used to cleanse, transform and feed data into this layer.

Middle Tier

The middle tier represents an OLAP server that can be implemented in two ways.

The ROLAP or Relational OLAP model is an extended relational database management system that maps multidimensional data process to standard relational process.

The MOLAP or multidimensional OLAP directly acts on multidimensional data and operations.

Top Tier

This is the front-end client interface that gets data out from the data warehouse. It holds various tools like query tools, analysis tools, reporting tools, and data mining tools.

How Data Warehouse Works

Data Warehousing integrates data and information collected from various sources into one comprehensive database. For example, a data warehouse might combine customer information from an organization's point-of-sale systems, its mailing lists, website, and comment cards. It might also incorporate confidential information about employees, salary information, etc. Businesses use such components of data warehouse to analyze customers.

Data mining is one of the features of a data warehouse that involves looking for meaningful data patterns in vast volumes of data and devising innovative strategies for increased sales and profits.

Types of Data Warehouse

There are three main types of data warehouse.

Enterprise Data Warehouse (EDW)

This type of warehouse serves as a key or central database that facilitates decision-support services throughout the enterprise. The advantage to this type of warehouse is that it provides access to cross-organizational information, offers a unified approach to data representation, and allows running complex queries.

Operational Data Store (ODS)

This type of data warehouse refreshes in real-time. It is often preferred for routine activities like storing employee records. It is required when data warehouse systems do not support reporting needs of the business.

Data Mart

A data mart is a subset of a data warehouse built to maintain a particular department, region, or business unit. Every department of a business has a central repository or data mart to store data. The data from the data mart is stored in the ODS periodically. The ODS then sends the data to the EDW, where it is stored and used.

Data Warehouse Example

Let us look at some examples of how companies use data warehouse as an integral part of their day-to-day operations.

Investment and Insurance companies use data warehouses to primarily analyze customer and market trends and allied data patterns. In sub-sectors like Forex and stock markets, data warehouse plays a significant role because a single point difference can result in huge losses across the board.

Retail chains use data warehouses for marketing and distribution, so they can track items, examine pricing policies and analyze buying trends of customers. They use data warehouse models for business intelligence and forecasting needs.

Healthcare companies, on the other hand, use data warehouse concepts to generate treatment reports, share data with insurance companies and in research and medical units. Healthcare systems depend heavily upon enterprise data warehouses because they need the latest, updated treatment information to save lives.

Data Warehousing Tools

Wondering what Data warehouse tools is? Well, these are software components used to perform several operations on an extensive data set. These tools help to collect, read, write and transfer data from various sources. What do data warehouses support? They are designed to support operations like data sorting, filtering, merging, etc.

Data warehouse applications can be categorized as:

Query and reporting tools

Application Development tools

Data mining tools

OLAP tools

Some popular data warehouse tools are Xplenty, Amazon Redshift, Teradata, Oracle 12c, Informatica, IBM Infosphere, Cloudera, and Panoply.
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Benefits of Data Warehouse

Wondering why businesses need data warehousing? Well, there are several benefits of data warehouse for end users.

Improved data consistency

Better business decisions

Easier access to enterprise data for end-users

Better documentation of data

Reduced computer costs and higher productivity

Enabling end-users to ask ad-hoc queries or reports without deterring the performance of operational systems

Collection of related data from various sources into a place

Companies having dedicated Data Warehouse teams emerge ahead of others in key areas of product development, pricing, marketing, production time, historical analysis, forecasting, and customer satisfaction. Though data warehouses can be slightly expensive, they pay in the long run.

Looking forward to a career in Data Science? Check out the PG in Data Science now.

Build Your Career in Data Warehousing

If you are looking to work as a Business Intelligence (BI) professional or learn data warehousing, you have many exciting career options available. Data architects, database administrators, coders, and analysts are some of the most sought-after BI professionals. Prepare yourself for a job interview with our data warehouse interview questions.

With data sources growing larger, businesses of the future need to devise better data insights and data analysis. Prepare for the future with Data Science Courses offered by a leading eLearning institute like Simplilearn and position yourself as an asset for top organizations.

What Is a Data Warehouse

Data warehouses serve as a central repository for storing and analyzing information to make better informed decisions. An organization's data warehouse receives data from a variety of sources, typically on a regular basis, including transactional systems, relational databases, and other sources.

A data warehouse is a centralized storage system that allows for the storing, analyzing, and interpreting of data in order to facilitate better decision-making. Transactional systems, relational databases, and other sources provide data into data warehouses on a regular basis.

A data warehouse is a type of data management system that facilitates and supports business intelligence (BI) activities, specifically analysis. Data warehouses are primarily designed to facilitate searches and analyses and usually contain large amounts of historical data.

A data warehouse can be defined as a collection of organizational data and information extracted from operational sources and external data sources. The data is periodically pulled from various internal applications like sales, marketing, and finance; customer-interface applications; as well as external partner systems. This data is then made available for decision-makers to access and analyze.

So what is data warehouse? For a start, it is a comprehensive repository of current and historical information that is designed to enhance an organization's performance.

Key Characteristics of Data Warehouse

The main characteristics of a data warehouse are as follows:

A data warehouse is subject-oriented since it provides topic-wise information rather than the overall processes of a business. Such subjects may be sales, promotion, inventory, etc. For example, if you want to analyze your company's sales data, you need to build a data warehouse that concentrates on sales. Such a warehouse would provide valuable information like 'who was your best customer last year?' or 'who is likely to be your best customer in the coming year?'

A data warehouse is developed by integrating data from varied sources into a consistent format. The data must be stored in the warehouse in a consistent and universally acceptable manner in terms of naming, format, and coding. This facilitates effective data analysis.

Data once entered into a data warehouse must remain unchanged. All data is read-only. Previous data is not erased when current data is entered. This helps you to analyze what has happened and when.

The data stored in a data warehouse is documented with an element of time, either explicitly or implicitly. An example of time variance in Data Warehouse is exhibited in the Primary Key, which must have an element of time like the day, week, or month.

Database vs. Data Warehouse

Although a data warehouse and a traditional database share some similarities, they need not be the same idea. The main difference is that in a database, data is collected for multiple transactional purposes. However, in a data warehouse, data is collected on an extensive scale to perform analytics. Databases provide real-time data, while warehouses store data to be accessed for big analytical queries.

Data warehouse is an example of an OLAP system or an online database query answering system. OLTP is an online database modifying system, for example, ATM. Learn more about the OLTP vs. OLAP differences.

Data Warehouse Architecture

Usually, data warehouse architecture comprises a three-tier structure.

Bottom Tier

The bottom tier or data warehouse server usually represents a relational database system. Back-end tools are used to cleanse, transform and feed data into this layer.

Middle Tier

The middle tier represents an OLAP server that can be implemented in two ways.

The ROLAP or Relational OLAP model is an extended relational database management system that maps multidimensional data process to standard relational process.

The MOLAP or multidimensional OLAP directly acts on multidimensional data and operations.

Top Tier

This is the front-end client interface that gets data out from the data warehouse. It holds various tools like query tools, analysis tools, reporting tools, and data mining tools.

How Data Warehouse Works

Data Warehousing integrates data and information collected from various sources into one comprehensive database. For example, a data warehouse might combine customer information from an organization's point-of-sale systems, its mailing lists, website, and comment cards. It might also incorporate confidential information about employees, salary information, etc. Businesses use such components of data warehouse to analyze customers.

Data mining is one of the features of a data warehouse that involves looking for meaningful data patterns in vast volumes of data and devising innovative strategies for increased sales and profits.

Types of Data Warehouse

There are three main types of data warehouse.

Enterprise Data Warehouse (EDW)

This type of warehouse serves as a key or central database that facilitates decision-support services throughout the enterprise. The advantage to this type of warehouse is that it provides access to cross-organizational information, offers a unified approach to data representation, and allows running complex queries.

Operational Data Store (ODS)

This type of data warehouse refreshes in real-time. It is often preferred for routine activities like storing employee records. It is required when data warehouse systems do not support reporting needs of the business.

Data Mart

A data mart is a subset of a data warehouse built to maintain a particular department, region, or business unit. Every department of a business has a central repository or data mart to store data. The data from the data mart is stored in the ODS periodically. The ODS then sends the data to the EDW, where it is stored and used.

Data Warehouse Example

Let us look at some examples of how companies use data warehouse as an integral part of their day-to-day operations.

Investment and Insurance companies use data warehouses to primarily analyze customer and market trends and allied data patterns. In sub-sectors like Forex and stock markets, data warehouse plays a significant role because a single point difference can result in huge losses across the board.

Retail chains use data warehouses for marketing and distribution, so they can track items, examine pricing policies and analyze buying trends of customers. They use data warehouse models for business intelligence and forecasting needs.

Healthcare companies, on the other hand, use data warehouse concepts to generate treatment reports, share data with insurance companies and in research and medical units. Healthcare systems depend heavily upon enterprise data warehouses because they need the latest, updated treatment information to save lives.

Data Warehousing Tools

Wondering what Data warehouse tools is? Well, these are software components used to perform several operations on an extensive data set. These tools help to collect, read, write and transfer data from various sources. What do data warehouses support? They are designed to support operations like data sorting, filtering, merging, etc.

Data warehouse applications can be categorized as:

Query and reporting tools

Application Development tools

Data mining tools

OLAP tools

Some popular data warehouse tools are Xplenty, Amazon Redshift, Teradata, Oracle 12c, Informatica, IBM Infosphere, Cloudera, and Panoply.
/p>

Benefits of Data Warehouse

Wondering why businesses need data warehousing? Well, there are several benefits of data warehouse for end users.

Improved data consistency

Better business decisions

Easier access to enterprise data for end-users

Better documentation of data

Reduced computer costs and higher productivity

Enabling end-users to ask ad-hoc queries or reports without deterring the performance of operational systems

Collection of related data from various sources into a place

Companies having dedicated Data Warehouse teams emerge ahead of others in key areas of product development, pricing, marketing, production time, historical analysis, forecasting, and customer satisfaction. Though data warehouses can be slightly expensive, they pay in the long run.

Looking forward to a career in Data Science? Check out the PG in Data Science now.

Build Your Career in Data Warehousing

If you are looking to work as a Business Intelligence (BI) professional or learn data warehousing, you have many exciting career options available. Data architects, database administrators, coders, and analysts are some of the most sought-after BI professionals. Prepare yourself for a job interview with our data warehouse interview questions.

With data sources growing larger, businesses of the future need to devise better data insights and data analysis. Prepare for the future with Data Science Courses offered by a leading eLearning institute like Simplilearn and position yourself as an asset for top organizations.

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Data warehouses help you run logical queries, build accurate forecasting models, and identify impactful trends throughout your organization. But, what goes into designing a data warehouse? Whether you choose to utilize a pre-built vendor solution or you’re starting from scratch — you’ll need some le…