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Data Warehouse Design: A Complete Guide

13 Mar 2023

Businesses are increasingly turning to the data warehouse (DWH) design & development to enhance their company’s analytical and reporting performance. It helps combine and organize diverse data sources in a centralized location (on-premises or cloud). But as organizations plan data warehouse initiatives, they’ll discover that each DWH will be unique, requiring a healthy level of preparation, design, and due diligence.

This blog will answer your queries concerning data warehouse design and its importance, architecture, the steps to design a DWH, technologies used, design approaches, best practices, cost considerations, and more.

Table of Contents

What is Data Warehouse Design, and Why is it Important?

DWH design is about creating a relational database schema for storing and analyzing large volumes of data from various sources. The purpose of a data warehouse is to provide a centralized repository of data that can be accessed and analyzed by business analysts, data scientists, and other stakeholders.

Data warehouse design is essential because it provides a structured and organized way of storing and analyzing large amounts of data from various sources. Here are some reasons that suggest the importance:

  • Improved data quality: A well-designed data warehouse ensures accurate, consistent, and up-to-date data. It eliminates redundant, inconsistent, and conflicting data that may exist in multiple sources, thereby improving the overall data quality.
  • Better decision-making: A data warehouse provides decision-makers easy access to accurate and timely information. This information can be used to gain insights into customer behavior, market trends, and other factors that impact business performance, leading to better decision-making.
  • Efficient querying: A well-designed data warehouse is optimized for querying and analysis, making it easy to retrieve data quickly and efficiently. It can save time and resources by reducing the need for complex queries or processing large volumes of data.
  • Integration of data: A data warehouse integrates data from multiple sources, providing a comprehensive view of the organization’s data. It makes identifying patterns, relationships, and trends easier across different data sources.
  • Scalability: A well-designed data warehouse is designed to handle large volumes of data and can be easily scaled as data needs grow. This ensures that the organization’s data needs are met, even as it grows and changes over time.

Today, cloud data warehousing helps overcome physical data centers’ storage and resource constraints. And a cloud-based data warehouse can dynamically scale & shrink to meet your ever-changing user needs & business priorities! It is an excellent solution for enterprises that require the agility to store, process & manage massive amounts of data for analytics & BI applications. Do explore the basics of cloud data warehouse, its key features, business benefits, reasons to move to a cloud data warehouse (DWH), major cloud data warehouse providers & how to choose the right one.

Data Warehouse Solution Architecture

Data Warehouse architecture components

Listed are the key elements of a typical data warehouse architecture:

  • Data Source Layer: It has all the data sources of your company that feed data into the DWH. For instance, various apps, databases, and devices collect your business data.
  • Staging Area: The data from the data source layer doesn’t directly enter the storage area. It needs to be cleaned and processed. So, data enters the staging area, a temporary transformation repository. If your data warehouse design allows data transformation within the targeted database, which could be your DWH or a data mart, then this staging area is absent.
  • Data Storage Layer: It represents the permanent and master database within the data warehouse that stores your business data in a structured format. It also includes data marts that hold your business line, department or team-specific data for analytics and reporting.
  • Analytics and BI Layer: This layer consists of data mining tools, OLAP tools, visualization tools, that help in querying the data in data marts and the DWH database.

12 Data Warehouse Design Steps

Data Warehouse design process

Designing a data warehouse solution involves several steps that need to be followed to ensure that the end product is effective and meets the requirements of the business. Below are the typical steps to explain how to design a data warehouse.

  • Requirements Gathering: As a data warehouse impacts all verticals, departments, and teams of a company; it is essential to identify the expectations of DWH end-users. The design should meet the present and future business needs, including security and compliance.
  • Preliminary Analysis:This step includes data source analysis, like determining the number of data sources, data quality, data volume and more. Data warehouse consultants identify potential users and their locations to align the project with department goals. They also collaborate with all stakeholders to understand their vision and expectations.
  • Conceptualization: It includes determining the core and advanced functionality of the data warehouse system. This stage begins with determining the components required in the DWH based on the chosen deployment option (on-premise or cloud). For cloud deployment, deciding between public, private, hybrid, and multi-cloud is essential to select the optimal architecture option. The focus should be identifying how the chosen architecture will meet business goals and solve problems. Usually, a solution architect and business analyst collaborate with you for this step.
  • Project Planning: The data contained within a data warehouse determines its reliability. So, the DWH project’s scope should be related to business objectives. The project deliverables, timelines, resources, and budget are decided along the same lines, focusing on the findings of the preceding stages. This stage also includes planning for disaster recovery in case of system failure.
  • Technologies Selection: This stage involves selecting technologies for your data warehouse components like databases & data lakes. You should focus on your data security strategy and existing analytics infrastructure, while selecting technology and tools for your DWH project.
  • System Analysis: It is essential to comprehensively analyze data sources, including their relationship, access rules, and the quality, volume, complexity, sensitivity, type, and structure of their data.
  • Data Governance: This stage involves setting up a data governance framework for your data warehouse system. So, you must determine the criteria for data quality. Also, create the policies and rules for data cleaning, data access, data usage, and data security for your DWH solution and its users. They could include policies concerning data backup, and data encryption.
  • Data Modelling: This is probably the most complex part of designing a data warehouse, as it is the process of visualizing data distribution within your DWH. It includes identifying data sets/entities, creating relationships between them, determining key attributes of every data set/entity and mapping them. It involves designing data models for the data warehouse and data marts. A data mart is a storage area within a data warehouse that houses the data for a particular business function. Creating data marts enhances query performance by accelerating the data analytics speed for a specific business area. The design of data models typically starts at the data mart level and branches out to the data warehouse. The popular data models include:
    1. Star Schema: It has a fact table surrounded by many associated dimension tables in the center.
    2. Snowflake Schema: It is an extension of the star schema where additional tables surround every dimension table.
    3. Galaxy Schema: It contains two facts tables with dimension tables surrounding each of them.

    Experienced system analysts work on this step of DWH design which also includes converting logical data models into database tables, indexes, keys, and columns.

  • ETL/ELT Processes Design: ETL (Extract, Transform, Load) is the process of pulling out data from your data sources, cleaning and organizing the data, and feeding it into your data warehouse. Contrarily, ELT (Extract, Load, Transform) includes extracting and loading data in the DWH, followed by data processing for structure and quality. Depending on your DWH components and architecture, data engineers will choose between the ETL and ELT processes and design them for data flow control and data integration.
  • OLAP Cubes: Online Analytical Processing Cubes (OLAP) help with data analysis and reporting in the data warehouse or data mart. Your data warehouse design may or may not require them.
  • Front-end Visualization design: Users interact with the front-end of any software, so your data warehouse must be user-friendly with intuitive and interactive features. Popular visualization tools like Power BI and Tableau help provide unique front-end experiences. The solution architect can customize the front end to meet your ad-hoc reporting requirements.
  • Rolling out the data warehouse: Once you have the final design of your data warehouse, it is time to develop and launch it.

Wish to Design a Data Warehouse?

We are ready to assist you in designing a scalable and effective solution for your short- and long-term data storage and processing needs.

Data Warehouse Design Approaches

You can adopt any one of the below-mentioned data warehouse design approaches or methodologies depending on your business needs and expectations:

  • Top-down Approach or Bill Inmon’s Approach
  • Bottom-up Approach or Ralph Kimball’s Approach
  • Hybrid Approach
  • Federated Approach

Read about data warehouse development approaches to learn about each in detail.

Technologies Used for DWH Design

Our technically proficient team has hands-on knowledge & experience in utilizing a wide range of DWH tools & technologies.  It includes:

Databases and Data Warehouse Services

  • Amazon Aurora
  • Apache Cassandra
  • Apache Hadoop
  • Apache Hive
  • Azure Synapse Analytics
  • Microsoft SQL Server
  • PostgreSQL
  • Snowflake
  • Amazon Redshift

Data Storage

  • Amazon DocumentDB
  • Amazon DynamoDB
  • Amazon Keyspaces
  • Amazon RDS
  • Amazon S3
  • Microsoft Azure Blog Storage
  • Azure Cosmo DB
  • Azure Data Lake
  • MongoDB

Cloud Services

  • Amazon Web Services (AWS)
  • Microsoft Azure

Data Integration

  • Apache Airflow
  • Apache Kafka
  • Azure Data Factory
  • Microsoft SQL Server Integrated Services
  • Talend

Big Data

  • Amazon DynamoDB
  • Amazon Redshift
  • Apache Cassandra
  • Apache Hadoop
  • Apache HBase
  • Apache Hive
  • Apache Spark
  • Apache Zookeeper
  • Azure Cosmo DB
  • MongoDB

Do give this blog on core components of cloud data warehouse software & tools and learn how they help organizations of any size store, manage, and analyze large volumes of data from multiple sources.

Data Warehouse Design Best Practices

Our experience working on data warehouse design projects has helped us compile a list of best practices that help eliminate errors and delays. They include:

  • Understand business needs: With the changing world scenarios and business dynamics, the needs of your enterprise will also change over time. So, when designing your data warehouse, it is imperative to consider immediate and future business requirements.
  • Stakeholder buy-in: Data warehouse development is a big project for a company, and it is essential that all key stakeholders, including management, and investors, agree with its design and proposed outcomes.
  • Use a dimensional data model: It is the most commonly used model for data warehouses. It organizes data into facts and dimensions, making it easier to analyze and understand.
  • Keep the design simple: The data warehouse design should be simple and easy to understand. This will make it easier for users to access the data and perform analysis.
  • Data quality and governance: High data quality and robust data governance practices should be the focus areas in your data warehouse design.
  • Plan for scalability: The data warehouse should be designed to handle large volumes of data and be scalable as the business grows.
  • Use automation: Automation can help reduce the time and effort required for designing and maintaining the data warehouse. It can include automating the data integration process and using tools to monitor and optimize performance.
  • Design for ease of use: The data warehouse should be designed to be user-friendly and easy to use. It will encourage adoption and increase the value of the data warehouse to the business.
  • Ensure security: Security is a critical consideration for any data warehouse. The data should be protected against unauthorized access and breaches.
  • Optimization of query performance: The design should have efficient data structures that pull relevant data for specific queries. It should enable fast and intuitive data analysis.
  • Maintenance plan: A data warehouse requires ongoing maintenance to ensure that it continues to meet the needs of the business. This includes monitoring performance, optimizing queries, and updating the data model as the business evolves.

Data Warehouse Design Cost Considerations

Before starting your project, understand what are the factors impacting the cost of your data warehouse design. They include:

  • Software costs: The software costs can vary depending on the data warehousing solution used. Some data warehouse solutions have a high upfront cost, while others charge monthly or yearly subscription fees. Choosing a solution that meets your data warehousing needs and fits your budget is essential.
  • Data integration costs: Data warehouses require data to be extracted from various sources, transformed, and loaded into the warehouse. This process can be time-consuming and expensive, depending on the complexity of the data and the number of sources. It’s essential to consider the cost of data integration when designing a data warehouse.
  • Data quality costs: Data quality is critical to the success of a data warehouse. Poor data quality can lead to incorrect insights and decisions. To ensure high data quality, data cleansing & other data quality activities are necessary, which can add to the overall cost of the data warehouse.
  • Training costs: Data warehouse users must be trained on how to use it effectively. The cost of training should be factored into the overall cost of the data warehouse.
  • Scalability costs: As the data warehouse grows, additional hardware and software may be required to maintain performance. The cost of scaling up the data warehouse should be considered when designing the data warehouse.

How can Rishabh Software help with Data Warehouse Design?

We are experienced and skilled at providing end-to-end data warehouse services, from consulting to post-launch support, including design. We provide cloud deployment options across Amazon Web Services and Microsoft Azure. Our team of dedicated data engineers, solution architects and more have sound working knowledge of using leading data warehousing tools like Azure Synapse Analytics, Amazon Redshift, Snowflake and more. Utilizing them, we have designed and built comprehensive data warehouse solutions, Operational Data Stores, and Data Marts for clients across industries.

Here is a recent use case from our experience.

Success Story: Cloud-based Data Warehouse System

A North American food & beverage industry giant wanted to derive data insights that meet their business needs. They managed data using diverse applications like PMS, POS, analytics, inventory and more. Also, they had 20+ data storages housing disparate data. The enterprise had grown via several mergers. So, data collaboration for analytics & reporting from various apps and sources was manual, time-consuming, and expensive.

We recommended creating a cloud-based enterprise data warehouse. The data warehouse design covered all the phases – from conceptualization, planning, and technology selection to system analysis, data governance, data modeling, ETL/ELT processes, and front-end design towards implementation.

Benefits Delivered;

  • 50% rise in workflow efficiency
  • 99% increase in business understanding available on-the-go
  • 40% drop in data quality problems

Read more about how the developed cloud-based data warehouse system enabled the US-based hospitality giant enabled the US-based hospitality giant to manage disparate data & derive valuable insights as per their need.

Final Thoughts

From our experience, data warehousing and design functionalities play a significant role in how your DWH setup could function to accommodate the business’s scalability requirements. While what types of DWHs you choose would depend on your use case, the design characteristics are specific to the features that are more in use or can make the system more robust and efficient for analytics.

We hope you have the answers to pertinent questions like the importance of DWH design, its process, data design methodologies, design best practices, and factors impacting cost. It should help you to streamline your data warehouse development and implementation plan.

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