Data plays a vital role in the growth of an organization. Companies spend large amounts of money on building data and big data infrastructures such as data vaults, data marts, data lakes, and data warehouses. These infrastructures are populated via multiple data sources using robust ETL pipelines that function throughout the day. A data infrastructure must operate 24/7 to provide real-time analysis and data-driven business insights.

As important as data may be, data storage infrastructures and the data warehousing process come with increased electricity consumption costs. In 2020, data centers globally had a combined energy consumption of about 400 terawatt-hours (TWh), and the rate has been growing exponentially. With the unprecedented growth in data and the increasing carbon emissions, sustainable data warehouse designs are a significant concern for global leaders.

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Why is IT Sustainability Important for a Lasting Future

Data centers consume energy worth 1% of the global energy requirement, and according to studies, by 2025, it will be equivalent to 1/5th of global consumption. Servers within a data center require large amounts of electricity to operate. Furthermore, these servers emit heat which escapes into the surrounding environment.

Shifting to a more green data warehouse design benefits an organization and the environment. Lower energy consumption would be lesser spending on electricity bills and cooling systems. This would also mean reduced carbon emissions and footprint, which are essential for a healthier, cleaner environment.

Measuring Carbon Emission of IT Equipment

Global emissions from cloud computing range from 2.5% to 3.7% of all global greenhouse gas emissions. The first step to a greener infrastructure is knowing what needs to be improved. Data centers primarily consist of large server racks which collect and store data every second.

Measuring carbon emissions from IT equipment is not a straightforward task, but getting an idea of where you are going wrong is easier. Every electrical equipment has an energy rating. Equipment built on new technologies is designed to consume lesser energy and be more energy efficient overall. If your data warehouse still runs on outdated machinery, it may be time for a change.

Utility consultant Mark Bramfitt suggests measuring carbon footprint by taking the electricity reading and multiplying it by the local carbon factor. This measurement will provide a much more accurate picture.

Green Renewable Storage and Processing Initiatives

Major organizations all around the globe have been researching methods to reduce their carbon footprint and have found significant success. The COP26 conference in November 2021 paved the way for several initiatives to counter adverse climate change and address climate risk. As a result, many businesses have shifted focus toward sustainable infrastructure by measuring and controlling the CO2 emissions from different aspects of the company.

However, in recent reports, the IT sector has been accused of not doing enough. Many environmentalists want IT companies to push for recycling and reusing older products rather than pushing out new hardware every few months. Another report from Working Group 21 (WG21) stresses that recent technological advances are not leaping in efficiency. Previously, the energy efficiency of electronic hardware doubled roughly every two years (Moore’s Law), but that curve has started to flatten.

Let’s talk about other, understated ways the software and cloud sector can reduce their carbon footprint.

Using Automation to Optimize Applications & Reduce Process Waste

When consulting firm McKinsey & Company analyzed the energy use of 70 large data centers, it found that only 6 to 12 percent of the energy consumption was used to perform computations. Yet data centers must operate 24/7 to remain relevant and offer maximum profit. Continuous running is not always necessary, and sometimes it is only a result of poor software programming or bad SQL optimization.

Sustainability is not only achieved through hardware optimization or via green electricity. By optimizing software, companies can reduce underutilized computing and carbon emissions. A promising ETL pipeline defines data flow and quality and carries out data ingestion in the shortest time possible. A poorly written SQL query may take hours to run, which consumes more energy and harms the environment. It is essential to revisit your data architecture, figure out the points of congestion and optimize query executions and data flow.

Major Cloud Providers Are Focusing on Green Data Centers

Global giants like Amazon and Microsoft have already started working towards carbon-neutral infrastructure. Amazon co-founded The Climate Pledge, under which they aim to reach net-zero carbon by 2040. Some of their steps include rethinking the data center construction strategy by using renewable energy and up to 100% recycled materials. Studies suggest that moving on-premises computing workloads, such as data processing and machine learning, to AWS can help lower workload carbon footprint by about 80%.

But work does not end here as Amazon has many more challenges ahead, including powering all operations using only renewable energy by 2025. Following in their footsteps, many other companies have started taking steps to introduce green data storage solutions. Some of the most notable of these firms are Facebook, Google, and Netflix.

5 Design Tips For Building a Sustainable Data Warehouse

Building a sustainable data warehouse or a data lake is not that hard, but it does require some specific steps to be followed. Engineers must keep certain aspects in mind and specific use cases to be defined to ensure that the result is safe for the environment. Some of the data center design strategies are discussed below.


Planning is always the key player in any successful venture. A data warehouse design should be thoroughly thought out, including the data to be fed and the business requirements. It always matters to think ahead for the following characteristics:

  • Data sources: All touchpoints from data will be ingested.

  • Types of data to be ingested: Whether the warehouse will have structured or unstructured data or both.

  • Schema definition: Metadata management for all relational tables.

Such information becomes a menace to management when the data volume grows outside bounds. If incorrect data is collected due to irresponsible management, it continues to waste energy, not to mention the additional time and resources required to fix the inaccuracies.


Standardization refers to the data structure within the relational database system. Database standardization transforms the entire database into a single frame. The following factors can identify a poor database structure:

  • Databases and Tables have repeating/same names.

  • Tables contain duplicate rows.

  • Tables do not have indexes built on them.

  • No or weak relation between multiple related tables.

A standardized structure helps with query execution, and data is loaded and processed faster. This reduces the load and energy consumption of servers, and other computing machines.


The more comprehensive the database documentation, the better it is for a long-term use case. The documentation contains database metadata, table schemas, and information regarding stored procedures. It also documents relationships between tables which is very helpful in the long run. Documentation helps save time in debugging issues and provides a guideline for new hires and trainees.

Understand Use Cases

It is essential to remember how the stored data is to be used, and the database design is structured accordingly. Firms can use data for business intelligence, machine learning, and data analysis within an organization. The Database design should be accommodating for all such purposes so that no additional tweaks are required in the future.


Data analytics is an essential responsibility of any database engineer. Organizations must analyze and understand and perform data modeling accordingly. Data should be processed, cleaned, and stored in appropriate tables. It also helps to identify all data sources to be prepared for the types encountered.

These practices positively impact data quality and save time while data warehousing.

Sustainable Data Strategy Ensure Sustainable Data Warehouse Design

A sustainable data warehouse design contributes to an organization’s responsibility of looking out for the planet. A sustainable data strategy defines and streamlines the data integration and processing pipelines. Data strategy sustainability is managed via smaller tasks that are discussed below.

Sustainable Data Collection

A sustainable data collection strategy involves data acquisition with minimum resource utilization and a pipeline that offers future scalability. Since data warehouses collect and store data all the time, a sustainable approach would mean reducing carbon emissions significantly. Automation and optimized SQL queries minimize servers' workload and reduce energy usage.

With sustainability, data collection should also focus on the easy integration of new data sources in the future.

Data Management Tools

Data management tools have a lot of benefits. These tools automate the process of data collection, processing, cleaning, and aid migration between different data stores. These tools are highly optimized for data-related tasks. Not only do they reduce the workload on data engineers, but they perform their job with efficiency, ensuring minimum resource utilization. They can free up processing cores when they are unused, reducing unnecessary energy wastage.

Setting the Right KPIs to Measure Sustainability

All the effort toward green data warehousing would be fruitless if we don’t track progress correctly. Organizations need to define the right KPIs to make sure they are making a positive impact. These KPIs should be coupled with milestones that are to be achieved in a said amount of time. Tracking the units of electricity consumed per week is a great way to ensure energy is saved. If you have a cloud data warehouse, you can request an energy consumption report from your providers, such as Amazon or Microsoft.

Automate Your Data Integration is a data platform that offers data integration from various data platforms. Our fantastic data ingestion and migration pipelines ensure that your data warehouse is only a few clicks away. We offer integrations from sources like Google Analytics, Amazon S3, and HDFS. On top of integration and warehousing, integrate also provides data analytics to help you understand your data and power your decision-making.

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