Let’s suppose your organization has reached a point where the best approach to analyze your data and make informed decisions is through a data warehouse. Many organizations design and build a data warehouse for business intelligence and data analytics to gain a competitive edge in the market. With all your business data aggregated in a centralized repository serving as the single source of truth, reporting becomes much more powerful, eventually leading to better, more profitable decisions for your organization.
When launching a data warehousing initiative within your organization, you need to address several questions. Which tools should you use? Should you build or buy a data warehouse solution?
Among these, one of the most important questions is: How much will it cost to build a data warehouse for your organization? Calculating the average cost to build a data warehouse can be challenging, especially when so many factors and moving parts are involved in the DW life cycle.
In this blog post, we have broken down and simplified the true cost of building a data warehouse and discuss how you can determine the ROI of your initiative.
Cost Components in Building a Data Warehouse
Before we provide you with hard numbers of data warehouse cost estimation, let’s discuss the key components that affect the cost of a data warehouse. Since every data warehouse project is different and based on the unique business requirements and needs for analytics, the cost of each component will vary.
Here is a breakdown of the cost components of a data warehouse:
Build Your Data Warehouse in the Cloud or On-Premises
The first decision you need to make is whether you will be storing all your data on-premises or on the cloud, i.e., where your data warehouse will actually be hosted. On-premises storage requires you to purchase and maintain all the hardware and software yourself, whereas, with a cloud-based solution, you have the cloud services provider to handle everything for you.
With an on-premise solution, the benefits are greater security, improved control over how and where your data is stored, and the ability to access and retrieve your information without relying on high-speed internet and connectivity. However, on the flip side, there are several overheads involving high upfront investment, increased maintenance cost, higher risk of data loss, and limited scalability.
The cloud, on the other hand, is the go-to storage choice for most enterprises. A 2021 Flexera report suggests that 87% of enterprises with a data warehouse are either experimenting with, currently using, or planning to use the cloud for their data warehouse projects. This goes hand-in-hand with the fact that cloud-based data warehouses are simpler to set up, easier to maintain, and don’t require any on-prem storage space or upfront investment. You only pay for what you use and have the luxury to scale up or down the storage and computational resources based on your business needs.
ETL: Manually Writing Code vs. ETL Tool
Extract, Transform, Load (ETL) is the process of moving data from the source location(s) to the target data warehouse. This includes the integration, migration, transformation, and synchronization of data to your warehouse from various data sources.
ETL is a core component of any DW implementation, and the cost depends on whether you choose to write your own code and scripts for ETL in-house or opt for an off-the-shelf ETL tool to populate your data warehouse.
Writing custom SQL for your ETL processes allows tailoring the data movement to the warehouse as per your business needs, but it comes with huge time and budget considerations. To hand-code the ETL flows, you will need a dedicated team of ETL developers that will coordinate with the data modelers and DW experts to construct ETL pipelines, which can easily take months or years to complete.
As opposed to this, choosing a data warehouse automation tool with built-in ETL functionality, such as Astera DW Builder, enables you to build ETL pipelines quickly at a logical level. Not only that, based on the resources that go into the manual process, you can develop ETL pipelines at a fraction of the cost. Such tools are versatile and robust. They support various data sources, provide many transformation options, and offer process orchestration capabilities to load and sync incoming data to warehouses.
Business Intelligence and Reporting
The primary goal of most data warehousing initiatives is to improve decision-making through business intelligence (BI) and data analytics. This is why data warehouses are often paired with powerful reporting and business intelligence tools to visualize data, unearth intelligible insights, and create reports for analyses. However, these BI and visualization tools come with a price tag too.
Businesses will have to choose whether to purchase an enterprise BI solution or use an open-source library such as D3.js or Kibana to meet their visualization requirements.
While open-source tools offer more freedom, there is a high cost of human resources attached to them since you need both developers and data analysts to build visualizations. On the other hand, most enterprise BI tools, like Tableau and Microsoft Power BI, are intuitive and simple to use, so there is little overhead other than the cost of the tool itself.
Whether you are using a data warehouse automation (DWA) tool to build your data warehouse or going for a custom coding method, you will need a qualified development team to work on the project. A typical data warehouse development team involves ETL developers, data engineers, data analysts, data architects, information technology managers, and database administrators.
Each resource plays a different role in the project, and based on the approach that you choose, the type and number of people you need will vary. For example, when opting for an off-the-shelf DWA tool, you will need fewer resources in your development team, but if you are going for a custom-coded solution, you will need a large team comprising of several ETL developers, data engineers, data modelers, DBAs, and others to work on the project.
Other Costs: Maintenance, Training, and Support
The last but often forgotten cost component of a data warehouse is related to the maintenance, training, support (both technical and non-technical), and upgrades. In most cases, you have three options to manage these other costs:
- Hire an internal team that can handle maintenance, support, and training for the data warehouse.
- Outsource such services and tasks to a third-party or vendor.
- In the case of a full-service cloud-based data warehouse, you can choose to add training and support to the monthly fee.
Your chosen approach depends on your unique requirements and budget, but generally, each has its own advantages and disadvantages in different scenarios.
Data Warehouse Cost Estimation for Each Component
To help you estimate the cost of building your data warehouse, here is a breakdown of the average costs for each component listed above.
Note that these are just data warehouse cost estimates; the exact costs will depend on your data warehouse project’s size and complexity.
Data Warehouse Storage Costs
Storage is perhaps the most expensive data warehouse component, especially if you are working with huge volumes of data. Based on whether you are going with on-premise and cloud storage, you will have different cost options.
When using cloud storage, you pay per gigabyte or terabyte of data and can scale your storage as and when necessary. Since there is no upfront investment or operational costs involved in the form of hardware, space, infrastructure, or on-site engineers, the only cost associated with the cloud is the amount that you pay each month (or each year). The most popular cloud storage solutions (such as Amazon S3, Microsoft Azure, and Google Cloud Storage) cost between $18.8 to $26.6 per terabyte (TB) per month ($225.6 to $319.2 per year).
On the other hand, you need to make some upfront investment for on-premise storage to set up and configure the hardware and infrastructure that you will need to store your data. This will include server machines, storage disks, network accessories, and engineers who will manage and configure everything. The upfront costs can start from as little as $3500 and go up based on the volume of data and redundancy you need. Other than the upfront cost, the operational costs such as electricity and on-site support and maintenance costs can be $1000+ month ($12,000 per year).
ETL & Data Integration Costs for Data Warehousing
For ETL, you can choose to use open-source tools like SSIS to hand-code your own data integration solution. While this will not involve licensing costs, you will need to hire developers and data architects to develop this solution. Depending on the number of resources you need, custom-coded ETL pipelines can cost more than $100,000+ and take months or years of development.
As opposed to this, if you choose to go with a dedicated ETL tool, like Astera Centerprise, or a DWA solution with built-in ETL functionality, such as Astera DW Builder, the costs will depend on whether you are purchasing a fixed-price license or a variable-price pay-per-use model. In either case, the costs average between $100 to $4,000+ per month, depending on the tool and your requirements.
BI & Data Analysis Costs
The average business intelligence solution costs anywhere between $600 to $6,000 per year. You can find several BI tools in the market, such as Microsoft PowerBI, Tableau, Domo, Qlik, and others. Like with DWA tools, you can either pay a fixed license price for these tools or pay as per usage.
On the other hand, if you are looking for a budget option, you can go for open-source and free tools such as the ELK (Elasticsearch, Logstash, and Kibana) stack or D3.js. However, just like with most open-source solutions, you will need to have technical resources available to customize and maintain your visualizations and reports for you.
- Labor Costs for the Data Warehousing Project
We listed the key types of human resources that you will need in a data warehouse project earlier in this blog post. Here is a breakdown of the salaries of such resources:
- Information Technology Managers: $88,767 per year
- Developers: $72,048 per year
- Database Architects: $114,515 per year
- Data Architects: $120,248 per year
- Data Engineers: $92,352 per year
- Data Analysts: $61,212 per year
It is worth mentioning here that these are just average figures for such roles, and the actual rates will depend on the market rate in the region/country you are hiring the resources. You will also need to factor in costs such as recruitment and training into this.
Other Costs related to the Data Warehouse
The cost of maintenance and support is usually difficult to estimate upfront. This might include fixing broken ETL flows when sources change, updating the data warehouse to integrate new systems, changing configurations for entities alongside regular testing, and others. However, on average, businesses allocate anywhere between 56% to 72% of their annual data warehouse budget to maintenance and support. So, let’s assume you have a budget of $100,000 per year for your data warehouse initiative after it has been deployed and configured, in which case, $56,000 to $72,000 per year would be spent on maintenance, training, and support of the data warehouse.
In most cases, this involves the cost of the in-house technical team that will be managing and maintaining your data warehouse. Like with the other cost components, the approach you use to build your data warehouse greatly impacts (cloud vs. on-premises, custom-code ETL pipelines vs. drag-and-drop interface, etc.) how much these other costs will be.
What is the Average Cost of Data Warehouse Implementation?
To sum up all the data warehouse costs into one concise section, using the breakdown we have provided above for all the components of a data warehouse, here are some estimated (average) figures on how much a data warehousing implemention will cost you per year:
Storage (Cloud): $273 per TB per year (at $22.7 per TB per month).
Storage (On-premise): $12,000 per year (at $1,000 per month).
Business Intelligence/Visualization Software: $3,000 per year (at $250 per month).
ETL Tool: $24,600 per year (at $2,050 per month).
Human Resources: $268,079 per year (assuming that you need, at minimum, a database architect, a data analyst, and a data engineer).
Other Costs & Maintenance: $64,000 per year.
Summing these figures up, the average cost of building a data warehouse with cloud storage is about $359,951 per year, and with on-premise storage is about $372,279 per year. However, the true cost will depend on a variety of factors discussed in the earlier sections. Therefore, the questions to ask when you are building a data warehouse are:
- How much storage do we need?
- Do we want to store my data on the cloud or on-premises?
- Should we build a data warehouse from scratch or buy a Data Warehouse as a Service (DWaaS) solution?
- Should we use a pay-per-use ETL tool or write our own custom solutions? Are there any considerations for building a real-time data warehouse?
- How many resources will we need to manage, maintain, and support our data warehouse project?
Based on your answers, the actual costs for building a data warehouse for your organization can be anywhere between $1,000 to $200 million per year.
Is Data Warehouse a Profitable Investment?
Now, let’s move on ahead to the ROI considerations for building a data warehouse. Because before making such an investment, it is important to know the advantages of building a data warehouse and whether such an initiative will give you the profitable returns that you are expecting.
A decade or so ago, the cost-benefit analysis of a data warehousing project was not very high. Back then, there were two key challenges. First, data warehouses were built from scratch, and on-premise implementation was the only choice organizations had, which led to a lot of money being spent on hardware and human resources. Second, the failure rate of data warehousing projects was quite high and sometimes, it would take development teams years and years to get the implementation right, leading organizations to spend millions with low ROI.
Today, the scenario is different. The cost of building a data warehouse is much less than what it used to be a decade ago. For storage, you can opt for the cloud to significantly cut down costs. Similarly, instead of having a dedicated team to write code and SQL queries for data models, ETL pipelines, and business intelligence suite, you can opt for industry-leading tools that are available at a fraction of the cost.
Using modern data warehouse tools, like Astera DW Builder, you can cut down the cost factor and provide much of the boilerplate that you need to get started with a data warehousing project. You spend less on the overhead of infrastructure, human resources, and maintenance and more on what actually matters, i.e., extracting hidden insights from your business data that can mean the difference between success and failure.
All in all, while a data warehouse is a sizeable investment, it also promises high returns if done right. The business intelligence, analytics, and reports that you source from a data warehouse are of great value, allowing you to make smarter, more informed decisions to drive your business’s success.
Astera DW Builder Reduces Data Warehousing Costs Impactfully
After looking at the various cost factors, it makes sense to adopt a powerful tool with agile data warehousing capabilities. It can mean the difference between success and failure for your modern data warehouse initiative. If you are looking to cut down on the project costs while ensuring higher quality of implementation, then Astera DW Builder is the right choice for your technology stack.
Astera DW Builder is a unified platform for data warehouse automation and management. The software provides you with an all-in-one solution for creating dimensional data models, developing ETL pipelines, streamlining deployment to cloud data warehouses (such as Amazon RDS and Snowflake), and intelligently managing and maintaining your data warehouse. Since DW Builder uses metadata-driven architecture to automate several EDW processes for you, all your team needs is to focus on what you require from the data warehouse rather than how you will build it in a cost-effective manner.
Since the platform offers a no-code environment for development, it makes it possible to set up, configure, and implement your data warehouse with a small team, saving you from hiring a large workforce. In addition, Astera DW Builder offers a transparent, yearly subscription pricing model that helps you better manage and control your data warehouse costs.
Ready to cut down on your data warehousing costs and give Astera DW Builder a try? Reach out to us today to schedule a demo or talk to our team.