Data Warehouse Pricing Explained: Usage + Costs Breakdown

A data warehouse is a central repository of data, but data warehousing comes at a cost.

This guide will help you estimate and understand the costs and usage of data warehouses, including their pricing models.

Why is data warehousing expensive?

Data warehousing can be expensive because it involves several complex components like infrastructure, people, opportunity costs, and resource-intensive data processing.

Traditional on-premises data warehouses need to store, process, and manage large volumes of data. For this, it has to invest significantly in hardware and software infrastructure. This includes the cost of servers, storage, and software licenses.
Enterprises need skilled staff to set up, maintain, and optimize the data warehouse. Usually, this staff includes analysts, data engineers, and database managers.
The rise of big data has made data warehousing even more expensive. Big data requires a vast amount of computing power and storage to process, store, and analyze data. This requires high-end hardware and software, which can be costly.
As a result, cloud-based data warehousing emerged as a cost-effective alternative. Traditional on-premise data warehousings are fading now.
Cloud-based solutions offer more flexible pricing models. This allows businesses to pay only for the resources they use. Cloud-based solutions can also reduce infrastructure and maintenance costs as well.

Related Read: Snowflake's Pricing Model Explained.

What are the key components of a data warehouse's cost?

Data storage and infrastructure: This includes the hardware and software required to store, manage and access the data.
BI and data visualization software: This is required to enable users to access and analyze the data.
ETL solutions: This is needed to extract, transform, and load data into the data warehouse.
Cost of internal headcount and external consultants: This is a significant component to consider as they are responsible for designing, building, and maintaining the data warehouse.

Related Read: Amazon Redshift's Pricing Model Explained

Data Storage and Infrastructure

Data storage and infrastructure are critical components of a data warehouse. The amount of data being stored and the level of connectivity required to access it determines the type of storage and infrastructure needed.
Cloud services are typically used for data storage, which offers cost-effective and scalable solutions. Cloud storage options are typically offered on-demand, allowing businesses to only pay for the amount of data they use.
Compute resources such as nodes and API are used to process data in the warehouse and provide access to the data. Cloud-based solutions for data storage and infrastructure eliminate the need for on-premises hardware and maintenance, reducing costs.

BI and Data Visualization Software

BI and data visualization software are essential components of a data warehouse, as they enable users to access and analyze the data. These tools help businesses to make data-driven decisions and to understand the value of their data.
Dashboards and reports are generated from the data warehouse to provide insights into the data. The type of BI and data visualization software required will depend on the use cases of the business and the level of sophistication required.
Cloud-based solutions offer cost-effective and scalable options for BI and data visualization software.

ETL Solutions

ETL solutions are required to extract, transform, and load data into the data warehouse. The amount of data being processed will determine the type of ETL solution required. Cloud-based ETL solutions are typically used to reduce costs and provide scalable solutions.
ETL solutions are often used in conjunction with data integration tools to manage data from multiple sources. These solutions help businesses to manage data quality and automate data integration processes. This overall reduces the time required to load enterprise data into the warehouse.

Internal headcount and external consultants

Internal headcount and external consultants are significant components of the cost of a data warehouse. The skilled professionals responsible for designing, building, and maintaining the data warehouse are a significant cost.
Data engineers, data analysts, and data scientists are often required to manage the data warehouse. Outsourcing these roles to external consultants can provide a more cost-effective solution.
However, there may be additional costs associated with hiring external consultants, such as training and onboarding. It is essential to factor in the cost of internal headcount and external consultants when estimating the cost of a data warehouse.

Top cloud data warehouse pricing models

1. Snowflake

Snowflake is a leading cloud-based data warehouse with a usage-based, pay-as-you-go pricing model. It separates storage and computing architectures to ensure a fair, transparent price. The pricing model is complex, with dozens of factors affecting costs. Therefore, cost estimation can be challenging.

Pricing is based on Snowflake credit usage, with no flat monthly fees. Compute resources are charged by credit, with per-terabyte flat rates for data storage and extra fees for some features.

There are three compute resource types. Those are

Virtual warehouses
Serverless compute hours
Cloud services

Virtual warehouses are clusters of Snowflake compute resources.

Most standard warehouses are for data analytics activities. Snowpark-optimized warehouses suit large-memory requirements like machine learning training datasets.

Serverless compute hours are used for specific processes. Cloud services work on overhead tasks.

Snowflake charges data storage separately from compute resources.

Storage covers

Data warehousing
Data loading internal stages
Backups,
Temporary and transient tables

Snowflake pricing varies depending on the cloud provider, region, and edition. There are four pricing plans: Standard, Enterprise, Business Critical, and Virtual Private Snowflake. The payment structure includes On-Demand for flexible scaling and Capacity for large discounts.

You can follow optimization strategies to reduce Snowflake costs. Some examples are

Pinpoint heavy usage
Use resource monitors
Use the right warehouse size
Reduce the queue
Use built-in optimizations

Snowflake is an attractive option for organizations of all sizes. Its separate architecture and pay-as-you-go pricing model make it flexible and scalable.

2. Amazon Redshift

Amazon Redshift is a cloud-based data warehouse. It's a scalable and cost-effective platform built for high-performance data warehousing in SQL.

Redshift pricing varies based on the number and type of nodes used, the size of the cluster, and the region.

For data sets under one terabyte, users can expect to pay around $0.25-0.35/hour for the lowest-power, 2-vCPU dc2 nodes.

For data sets over one terabyte, users will have to pay around $1.09-1.20/hour for the lowest power. For the highest power, users will have to pay around $13-14.50/hour.

Redshift Managed Storage prices come only with RA3 nodes and are billed at a per-gigabyte rate monthly. Redshift Serverless bills based on Redshift Processing Units (RPUs) at a price of $0.35 to $0.50 per hour in the US.

You will freely get concurrency scaling and Redshift spectrum features with Redshift Serverless.

Redshift Concurrency Scaling lets you scale your processing power. It offers one Concurrency Scaling hour per 24-hour day for free.

Redshift Spectrum offers a simple pricing model of around $5.00 per terabyte of data scanned. This may slightly vary based on the region.

Redshift ML offers its price based on the total number of created cells. It starts at $20 per million cells for the first 10 million cells.

Redshift offers three payment methods. Those are No upfront, pay monthly; Partial upfront; and All upfront. Finally, users will pay additional fees for transferring data in and out of Amazon Redshift.

3. Google BigQuery

Google BigQuery is a popular data warehouse. However, its pricing can be confusing.

There are two main pricing models for BigQuery. Those are on-demand pricing and flat-rate pricing. With on-demand pricing, you pay for the amount of data you analyze.

With on-demand pricing, you have up to 2,000 slots per project. The price varies by region. However, most US customers pay around $5-7 for an additional 1TB.

In the flat-rate model, you will be charged for the number of slots you use. It's based on ongoing capacity. Think of compute bandwidth to understand the idea. The longer your commitment, the bigger the discount you'll see in your monthly cost. You have to buy 100 slots minimum. Then you can buy more slots in increments of 100.

The flat-rate model comes with three different commitment levels. Those are flex slots, monthly, and annual.

Flex slots require a commitment for only an initial 60 seconds. , You will pay about $4-$5.50 per hour or $3,000-$4,000/month
Monthly commitment costs about $2,000-$2,7000/month
Annual commitment costs about $1,700-$2,300/month.

BigQuery's pricing also depends on your region. For data querying, the US regions include Oregon, Los Angeles, Salt Lake City, and a few more. For data storage, BigQuery offers two additional regions: Columbus and Dallas.

In addition to query and storage costs, BigQuery has additional features. They come with their own pricing model. These include Data Transfer Service, BigQuery ML, BigQuery Omni, and BI Engine.

It's important to understand the pricing structure BigQuery. it helps to choose the best plan for your business needs and budget.

4. PostgreSQL

PostgreSQL is a popular open-source database platform for a data warehouse solution. It has been around since 1986.

One of the most significant advantages of PostgreSQL is its cost-effectiveness. As an open-source DBMS, it is completely free to use. You can freely use it even for commercial purposes. There is no cost to start with. There is no ongoing payment like with SaaS solutions. You don't have to pay to upgrade software with new patches.

The cost of running PostgreSQL depends largely on hardware and personnel costs. If you choose to host Postgres yourself, pricing starts at $0 and can go up to nearly any amount you're willing to spend.

PostgreSQL can be used as a data warehouse tool and can be hosted either through a self-hosted or managed platform. The cost of hosting PostgreSQL is dependent on the type of hosting.

One option is to self-host PostgreSQL, which is essentially free. However, there will be a cost for hardware and maintenance.

Another option is to use a managed platform. Some examples are GCP, AWS, and Azure. These platforms handle the overhead of provisioning, storage capacity management, security, etc.

Their pricing costs depend on the compute, storage, and networking requirements.

Here are some examples of managed platform pricing structures.

Google Cloud SQL for PostgreSQL offers CPU and memory resources. These cost between $0.04-0.05/hour for each vCPU$0.007-0.009/hour for each gigabyte of memory.
SSD storage costs between $0.17-0.20 for 1GB per month.
HDD storage costs between $0.09-0.11 for 1GB per month.

Moreover, Managed platforms have advantages such as increased scalability, and improved reliability.

5. Azure

Microsoft Azure Data Warehouse provides a cost-effective solution for implementing an enterprise-level data warehouse. It saves the high upfront costs of hardware installment.

The pricing model consists of computing and storage charges. Computing charges are based on Data Warehouse Units, which can be scaled up or down easily within the Azure system.

You only pay for the computing time that the data warehouse is up and running, and when paused, no charges apply. For example, for the East US region, SQL Data Warehouse costs $1,125/month for 100 DWU and up to $67,500/month for 6000 DWU.

Storage charges apply even when the data warehouse is paused, but there is no limit to how much data you can store. For the East US region, 1TB of storage costs $135/month, while 1PB costs $135,170/month.

Azure also offers a disaster recovery solution, Geo-Redundant Storage. This stores data copies for data recovery in the event of a disaster.

Top ETL Tools to consider for your Data Warehouse

1. Portable

Portable is an exceptional cloud-based no-code ETL tool. It offers over 300 connectors for hard-to-find data sources. It has an unparalleled collection of cloud-based e-commerce connectors. As a result, it specializes in solving e-commerce problems with data.

Its support for commonly used data warehouses and databases like

Snowflake
Google BigQuery
Amazon Redshift
PostgreSQL
MySQL

This makes it an ideal solution for businesses of all sizes.

With Portable, you can move data in near real-time and trigger syncs manually as often as you want for free. The most popular option is to set up scheduled data flows that automatically replicate data on a cadence.

Portable has unlimited data volumes and a daily sync frequency of $200/month. Portable also offers custom connectors with a very fast turnaround time.

Finally Portable offers hands-on technical support. When something breaks, the Portable team is ready on call.

2. Fivetran

Fivetran is an ELT tool founded in 2012 for hyper-growth companies.

It offers 160+ data source connectors, change data capture support for databases, and data warehouses/data lakes as destinations.
The pricing model is based on Monthly Active Rows, which can be cost-effective for low volumes, but very expensive at high volumes, making it difficult to forecast and predict the pricing.
Fivetran provides professional services for enterprise use cases, with a core focus on complex data models like Oracle, SAP, and Workday.
The platform offers strong technical support, but building custom connectors can be a significant amount of work, and prices can be challenging to predict.

3. Airbyte

Airbyte is an ELT platform with over 300 no-code connectors. It offers low-maintenance data pipelines. It's an ideal fit for data engineers looking for an open-source framework to customize integrations in-house.

Users are in full control of data transformations, which can be customized using SQL or dbt. Airbyte provides ready-to-query schemas for orchestration and data transformation.

With Airbyte, connectors run as Docker containers, so you can use the language of your choice. Airbyte charges are based on credits consumed.

It has a free plan for the open-source version and cloud hosting is charged separately. Cloud pricing includes data hosting, management, multiple workspaces, and more. It comes with a 14-day free trial and a monthly charge of $2.50 per credit.

4. Stitch Data

Stitch Data is a cloud-based data integration platform. It is designed to simplify the ETL process from various sources into a central data warehouse.

Its key features include a

User-friendly interface
Automatic schema mapping
Support for a wide range of data sources and types of data
Robust API

Stitch Data is designed to be intuitive and user-friendly. It is accessible to both technical and non-technical users.

Moreover, automatic schema mapping allows users to easily match data from different sources. This can save a significant amount of time and effort.

On the downside, Stitch Data's pricing model can be a bit complex. It may not be suitable for smaller businesses or startups with limited budgets. Additionally, some users have reported issues with data latency and performance. This is more common when dealing with large datasets.

5. Hevo Data

Hevo Data is a cloud-based data integration platform. It provides an end-to-end solution for managing data pipelines. Its key features include a

User-friendly no-code interface
Support for 150+ data connectors
Real-time data migration
Ability to handle large data volumes

You can use Hevo Data for the complete automation of ELT, or ETL processes. In fact, you can use it even for Reverse ETL workloads. This makes it easy to extract, transform, and load data from various sources into a data warehouse.

One of the primary advantages of Hevo Data is its ease of use. The no-code interface allows non-technical users to create and manage data pipelines easily.

On the downside, Hevo Data may not offer good support for long-tail connectors. It also requires manual mapping for changes between different types of tools.

Hevo Data's pricing model includes a free plan with limited features. It also has a Starter plan that starts at $239/month, and a custom Business plan.

6. Apache Nifi

Apache NiFi is an open-source data integration tool. It provides a web-based interface for creating, monitoring, and managing data flows.

Its key features include

Drag-and-drop user interface
Support for a wide range of data sources
Real-time data processing, and
Data encryption

One of the main advantages of Apache NiFi is its flexibility. Apache NiFi is ideal for large-scale data integration projects. The reason is it can handle a wide variety of data sources.

On the downside, Apache NiFi can be challenging to set up and configure. Its learning curve may be steep for non-technical users.

Apache NiFi is an open-source platform, which means it is free to use and distribute. However, users may need to incur additional costs for hosting and maintenance. Additionally, some third-party vendors offer commercial support and consulting services for Apache NiFi.

7. Talend

Talend Open Studio is a popular open-source ETL tool. It is known for its versatility in supporting multiple data sources and targets. It offers many features, including data profiling, data quality, and data governance.

Talend Open Studio offers a user-friendly UI to create ETL workflows. It also has over 900 connectors and components for different data sources.

Talend Open Studio is very capable of handling complex ETL processes. It supports various data processing modes. Some examples are batch and real-time processing, in-memory processing, and parallel processing.

Additionally, Talend Open Studio includes advanced data mapping and transformation capabilities. This makes it easier for users to transform and integrate data from disparate sources.

The disadvantages of Talend are there is a learning curve for this tool. Some users have also noted that the tool can be resource-intensive. For example, it may require more memory and processing power. than other similar tools.

8. Informatica PowerCenter

Informatica PowerCenter is an enterprise-level ETL tool to streamline data management processes. It offers a range of data integration features.

With Informatica PowerCenter, users can extract data from various sources. Then transform them into a structured format. Finally, load them into a target system.

The tool supports various data processing modes such as

Batch processing
Real-time processing
Incremental processing

Informatica PowerCenter is able to perform data profiling, data quality, and data governance tasks. These features help users ensure that their data is accurate, complete, and well-managed.

However, Informatica PowerCenter can be expensive for smaller organizations.

9. Microsoft SQL Server Integration Services (SSIS)

Microsoft SQL Server Integration Services is a powerful data integration and transformation tool. It is designed to handle complex data integration tasks. Some key features of SSIS include

A data profiling task to analyze the quality and completeness of data.
Range of tools for data cleansing, such as the Data Cleansing transformation.
Tight integration with other Microsoft products. This allows users to work with different sources from the Microsoft ecosystem seamlessly.

SSIS has a user-friendly interface that allows users to drag and drop components to create data integration workflows. However, this interface can be intimidating for non-technical users. One of the most significant disadvantages of SSIS is that it can be resource-intensive. It requires significant processing power and memory to handle large datasets.

10. Oracle Data Integrator (ODI)

Oracle Data Integrator is a comprehensive ETL tool for data integration and transformation. ODI is a popular choice for businesses looking to streamline data integration processes. It supports a wide range of data sources and targets, making it versatile.

ODI includes features for data profiling, data quality, and data governance. It also supports advanced data transformation and mapping capabilities. This allows easy manipulation of data from different sources.

ODI can be deployed on-premises or on a cloud platform as a service (PaaS). It integrates well with Oracle databases, providing seamless integration for Oracle-based applications.

ODI has a relatively high cost of ownership due to licensing, hardware, and maintenance costs.