Understand about data warehouse cost estimation

Disclaimer: The above estimations are rough figures based on our experience over years of developing data warehouse solutions and other general research. The prices can change based on regions, requirements, industry norms, and vendors and service providers. Advised to only use this as a reference when you are planning to implement data warehouse for your business.

You have decided to build a data warehouse to centralize your data storage and make data-driven decisions. One of the very first things you will think about is the cost. Cost to build a warehousing solution as well as the cost to maintain, store, or upgrade its capacity. 

On average, setting up a data warehouse costs anywhere from $30k to $2 million depending on the project complexity, timelines, whether you do it internally or outsource, and many other factors. 

But this is only an approximation. This is because both variable and fixed costs are involved in the data warehouse building. We can further break it down and explain what you are paying for - whether you do it on cloud or on-premise. 

On-premise data warehouse expenses

Building data warehouses in on-premise setup invites higher setup costs. This is because you need to buy the physical infrastructure, servers, and server licenses required to store and manage data. And, you should hire IT technicians to implement this. 

Initial setup

Infrastructure costs vary from one location to another. Data storage, server hardware, secondary storage, and facility setup can cost up to $100K to store 1 petabyte of data. To set up a data warehouse on-premise location, it can take from 10 to 15 days. 

This will need a team of data engineers, DevOps assistants, business analysts, a data architect, and a project management head to oversee the operations. Together, this can cost you an equivalent of $100k for a year approximately.

Maintenance costs

You should employ data analysts and engineers to maintain this data warehouse. Also, there will be maintenance and repair costs that have to be performed periodically, which depend on your database usage, maintenance frequency, ongoing custom development projects, latency issues, and more. Summing up the floor support, backup, 24/7 power supply and batteries, and frequent maintenance, you will spend $6 to $12k every month.

On-prem comes with a hefty initial investment but is beneficial for many.

In the future, if you need additional storage space for scalability, consider that you will have to purchase additional infrastructure and repeat the procedure.

Looking at the data, we can see that on-premise data warehousing is expensive considering the fixed and variable costs associated with it. Also, the onus is on you to take care of everything from purchasing the computing resources and servers to maintaining them periodically. For this reason, growing businesses are shifting to convenient and cost-effective cloud-based DWaaS (data warehousing as a service). 

Why on-prem for data warehousing?

There are a handful that want to manage data storage within their firewall and they prefer on-premise solutions for the following reasons.

• Highest security as the data resides within your premises. 

• Great for organizations that handle low volumes of data.

• Organizations handling sensitive data and have to fulfill too many compliance requirements. 

• Ability to have offline access to the data.

Cloud data warehouse expenses

With cloud options, you don’t have to set up or maintain physical infrastructure. The provider offers everything and you can host your data and scale as per your requirements. 

So, the major component here is the storage cost. This is a fixed, concurring cost you pay monthly or annually depending on usage. Some of the popular providers include Google Cloud, Amazon, Microsoft, etc.

Storage costs vary based on the providers, your plan, type of storage, your data storage and scaling, maintenance, and other parts.

It can take a while to understand cloud-pricing complexities. For instance, notice the above, you will not be able to find a standardized pricing format among cloud competitors. Also, these charges vary based on your storage type, number of users, and storage requirements. 

Besides, there will be charges other than storage - like service, networking, and transaction fees. This can be tricky to navigate and find the right plan and partner. With the help of cloud migration partners, you can avoid pitfalls like overspending, vendor lock-ins, and migration complexities.

You will still need an implementation team to take care of data migration, set up the ETL processes, and connect business intelligence tools. But it requires far fewer people, and for a short period until it is set up. 

There are plenty of data transformation tools available, each with varying pricing and features. Some of them are open-sourced and are free to use and some are charged monthly/based on usage. 

Data integration, transformation, and ETL tools cost around $200 per month and go up to $30k per month.

If you connect business intelligence tools for analytics and visualization purposes, the spending can range from $8k and go up depending on your analytics projects. This cost includes implementation, training, tool cost, and maintaining a team of BI experts. 

Conclusion

If you are planning to build a centralized data warehouse for your whole organization, it can take anywhere from 3 months to a year. The time and cost involved depend on your organization’s size, the number of applications you handle, your current architecture, and your future analytics needs. 

On top of that, cloud costs can be tricky to begin with, if you are going for a cloud-based data warehousing system. 

You will need a thorough implementation strategy, qualified data engineers and architects, and the best tech stack that can orchestrate everything. Security and governance are other big concerns you have to think about. 

Our data engineering team can meet your demands here, find what works best for you, lay the foundational plan, and get the data warehouse built from scratch. 

Take a look at this case study to see how we designed and built a data warehouse successfully for a logistics company. This has changed the way they access data and amplified their business intelligence - at reasonable expenses and timelines.