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.
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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:
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.
Listed are the key elements of a typical data warehouse architecture:
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.
Experienced system analysts work on this step of DWH design which also includes converting logical data models into database tables, indexes, keys, and columns.
We are ready to assist you in designing a scalable and effective solution for your short- and long-term data storage and processing needs.
You can adopt any one of the below-mentioned data warehouse design approaches or methodologies depending on your business needs and expectations:
Read about data warehouse development approaches to learn about each in detail.
Our technically proficient team has hands-on knowledge & experience in utilizing a wide range of DWH tools & technologies. It includes:
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.
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:
Before starting your project, understand what are the factors impacting the cost of your data warehouse design. They include:
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.
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, technology selection to system analysis, data governance, data modeling, ETL/ELT processes, and front-end design towards implementation.
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.
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.