Cloud Resource Energy Efficiency Scores
This dataset provides granular energy efficiency metrics for cloud resources across major providers, including energy consumption, carbon emissions, and actionable optimization recommendations. Designed for tech startups and SaaS platforms, it supports ESG reporting, benchmarking, and sustainability-driven cloud operations.
Sample rows
preview · 8 of 75 rows · all 17 columns| resource_idstring | workload_typestring | efficiency_scorefloat | provider_namestring | account_idstring | workload_namestring | regionstring | instance_typestring | measurement_period_startdatetime | measurement_period_enddatetime | energy_consumed_kwhfloat | carbon_emissions_kgfloat | renewable_energy_percentagefloat | optimization_recommendationstring | esg_reportableboolean | created_atdatetime | updated_atdatetime |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| res-10001 | Web Server | 87.2 | AWS | acct-2023-01 | Customer Web Portal | us-east-1 | t3.medium | 2024-04-01T08:15:00Z | 2024-04-01T20:15:00Z | 11.6 | 5.8 | 78 | Scale down unused instances during low traffic hours | true | 2024-04-02T10:30:00Z | 2024-04-07T14:22:00Z |
| res-10002 | Batch Job | 72.5 | Google Cloud | acct-2023-02 | Analytics Batch Job | europe-west3 | n2-standard-4 | 2024-03-25T10:00:00Z | 2024-03-25T18:00:00Z | 24.3 | 10.7 | 86 | Schedule jobs for off-peak grid times | true | 2024-03-26T09:00:00Z | 2024-03-26T09:01:00Z |
| res-10003 | Database | 65.3 | Azure | acct-2023-03 | User Profile Database | westus2 | Standard_D4_v4 | 2024-03-15T07:00:00Z | 2024-03-15T19:00:00Z | 17.4 | 9.5 | 62.5 | Upgrade to newer instance types for better efficiency | true | 2024-03-16T12:23:00Z | 2024-03-16T13:23:00Z |
| res-10004 | ML Training | 54.4 | IBM Cloud | acct-2023-04 | ML Model Training - Alpha | eu-de | bx2-2x8 | 2024-04-08T09:00:00Z | 2024-04-08T17:00:00Z | 38.2 | 21.9 | 41 | Switch to GPU-optimized instances with lower power draw | true | 2024-04-08T17:01:00Z | 2024-04-08T17:10:00Z |
| res-10005 | Storage | 79.5 | AWS | acct-2023-05 | Image Storage Service | ap-southeast-2 | m6i.large | 2024-04-03T21:00:00Z | 2024-04-04T09:00:00Z | 12.8 | 6.9 | 92.5 | Enable lifecycle policies for unused objects | true | 2024-04-04T09:05:00Z | 2024-04-04T09:05:00Z |
| res-10006 | Database | 69.8 | Oracle Cloud | acct-2023-06 | Inventory DB | uk-london-1 | VM.Standard.E3.Flex | 2024-04-06T06:00:00Z | 2024-04-06T14:00:00Z | 21.2 | 10.3 | 58 | Consolidate database workloads | true | 2024-04-06T15:01:00Z | 2024-04-06T15:20:00Z |
| res-10007 | Web Server | 92.1 | AWS | acct-2023-07 | API Gateway | us-west-2 | t2.micro | 2024-04-07T07:05:00Z | 2024-04-07T19:05:00Z | 10.2 | 4.6 | 98 | Implement caching for frequent API calls | true | 2024-04-07T19:06:00Z | 2024-04-07T19:40:00Z |
| res-10008 | Batch Job | 61.7 | Google Cloud | acct-2023-01 | Data Lake ETL | asia-south1 | e2-standard-8 | 2024-04-02T22:00:00Z | 2024-04-03T06:00:00Z | 32.7 | 15.1 | 33 | Optimize ETL query logic | true | 2024-04-03T07:01:00Z | 2024-04-03T08:15:00Z |
| res-10009 | Web Server | 81.6 | Azure | acct-2023-08 | Customer Support Web | northeurope | Standard_B2ms | 2024-04-05T09:00:00Z | 2024-04-05T17:00:00Z | 14.5 | 8.1 | 88 | Reduce autoscaling thresholds | true | 2024-04-05T17:01:00Z | 2024-04-05T17:10:00Z |
| res-10010 | Storage | 93.8 | Oracle Cloud | acct-2023-09 | Backup Storage | us-phoenix-1 | VM.Standard2.2 | 2024-03-30T17:00:00Z | 2024-03-31T05:00:00Z | 7.8 | 4.2 | 100 | Archive infrequent backups to cold storage | true | 2024-03-31T05:01:00Z | 2024-03-31T05:10:00Z |
| res-10011 | ML Training | 51.2 | IBM Cloud | acct-2023-10 | ML Model Training - Beta | us-south | bx2-4x16 | 2024-04-09T13:00:00Z | 2024-04-09T21:00:00Z | 44.6 | 25.9 | 45 | Switch to preemptible instances for short jobs | true | 2024-04-09T21:01:00Z | 2024-04-09T22:01:00Z |
| res-10012 | Other | 76.4 | Other | acct-2023-11 | Compliance Logging | ca-central-1 | custom-4x8 | 2024-03-29T06:00:00Z | 2024-03-29T18:00:00Z | 13.2 | 7.1 | 80 | Reduce log retention period | true | 2024-03-29T18:01:00Z | 2024-03-29T18:10:00Z |
| res-10013 | Web Server | 83.2 | AWS | acct-2023-12 | Internal Report Server | eu-central-1 | c5.large | 2024-04-10T07:00:00Z | 2024-04-10T23:00:00Z | 15.9 | 8.7 | 74 | Migrate to serverless architecture | true | 2024-04-11T01:01:00Z | 2024-04-11T01:10:00Z |
| res-10014 | Batch Job | 59 | Google Cloud | acct-2023-13 | Ad Event Collector | us-central1 | e2-highmem-2 | 2024-03-28T14:00:00Z | 2024-03-28T22:00:00Z | 29 | 13.3 | 29 | Batch events for fewer jobs | true | 2024-03-28T22:01:00Z | 2024-03-28T22:30:00Z |
| res-10015 | Database | 67.1 | Azure | acct-2023-14 | Document DB | australiaeast | Standard_E2s_v3 | 2024-03-21T08:00:00Z | 2024-03-21T16:00:00Z | 18.4 | 9.9 | 60 | Upgrade to SSD-based storage | true | 2024-03-21T16:01:00Z | 2024-03-21T17:01:00Z |
| res-10016 | ML Training | 55.9 | IBM Cloud | acct-2023-15 | Model Training - Gamma | jp-tok | bx2-2x8 | 2024-04-11T10:00:00Z | 2024-04-11T18:00:00Z | 41.5 | 23.2 | 36 | Use spot instances for non-critical training | true | 2024-04-11T18:01:00Z | 2024-04-11T18:15:00Z |
| res-10017 | Storage | 90.5 | AWS | acct-2023-16 | Static Content Storage | us-east-2 | r4.large | 2024-04-12T11:00:00Z | 2024-04-12T19:00:00Z | 8.4 | 4 | 95 | Enable versioning and auto-delete old objects | true | 2024-04-12T19:01:00Z | 2024-04-12T19:31:00Z |
| res-10018 | Batch Job | 60.1 | Google Cloud | acct-2023-17 | Customer Analytics | southamerica-east1 | n2-highcpu-8 | 2024-03-23T20:00:00Z | 2024-03-24T04:00:00Z | 35.9 | 17 | 38 | Use distributed queries to lower compute | true | 2024-03-24T04:01:00Z | 2024-03-24T04:05:00Z |
| res-10019 | Other | 82.4 | Azure | acct-2023-18 | Session Cache | southafricanorth | Standard_F4s_v2 | 2024-03-26T11:00:00Z | 2024-03-26T19:00:00Z | 9.6 | 5.2 | 100 | Reduce maximum cache size | true | 2024-03-26T19:01:00Z | 2024-03-26T19:02:00Z |
| res-10020 | ML Training | 56.6 | IBM Cloud | acct-2023-19 | ML Training - Delta | eu-gb | bx2-2x8 | 2024-04-13T13:00:00Z | 2024-04-13T21:00:00Z | 37.8 | 21.3 | 40 | Limit training epochs for early convergence | true | 2024-04-13T21:01:00Z | 2024-04-13T21:10:00Z |
What the 75 rows show
from the 75-row sampleML Training (workload type) stands out: mean efficiency_
- 78.4median efficiency_
score - 29.9median energy_
consumed_ kwh - 13.1median carbon_
emissions_ kg - 78.0median renewable_
energy_ percentage
Median 78.4, from 45.3 to 99.0.
- string 8
- float 4
- datetime 4
- boolean 1
Columns
17 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 8 columns | |||
resource_id | string | Unique identifier for the cloud resource being measuredunique | res-10001 |
provider_name | string | Name of the cloud provider (e.g., AWS, Azure, Google Cloud)6 values | AWS |
account_id | string | Identifier for the customer or organization account within the cloud provider | acct-2023-01 |
workload_name | string | Name or description of the workload running on the cloud resource | Customer Web Portal |
workload_type | string | Type of workload (e.g., web server, database, ML training, batch job)6 values | Web Server |
region | string | Geographical region where the cloud resource is hosted | us-east-1 |
instance_type | string | Type or SKU of the cloud resource (e.g., t2.micro, n1-standard-4) | t3.medium |
optimization_recommendation | string | Actionable recommendation for improving the energy efficiency of the resourceoptional | Optimize ETL query logic |
| Numbers 4 columns | |||
energy_consumed_kwh | float | Total energy consumed by the resource during the measurement period (in kilowatt-hours)0 or more | 11.6 |
carbon_emissions_kg | float | Estimated carbon emissions generated by the resource during the measurement period (in kilograms CO2 equivalent)0 or more | 5.8 |
efficiency_score | float | Calculated energy efficiency score for the resource (higher is better, normalized 0-100)0 to 100 | 87.2 |
renewable_energy_percentage | float | Percentage of energy sourced from renewables during the measurement period (0-100)0 to 100 · optional | 78 |
| Dates and times 4 columns | |||
measurement_period_start | datetime | Start date and time for the energy efficiency measurement period | 2024-04-01T08:15:00Z |
measurement_period_end | datetime | End date and time for the energy efficiency measurement period | 2024-04-01T20:15:00Z |
created_at | datetime | Timestamp when this record was created | 2024-04-02T10:30:00Z |
updated_at | datetime | Timestamp when this record was last updatedoptional | 2024-04-07T14:22:00Z |
| True or false 1 column | |||
esg_reportable | boolean | Indicates if this measurement is suitable for ESG reporting purposesoptional | true |
Use it for
A technology dashboard
Efficiency_
score by workload_ type and a breakdown of provider_ name. Excel, Power BI or Tableau. Why do the 13 ML Training rows have a mean efficiency_
score of 57.2? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Resources75res-1000187.2Web Serv…res-1000272.5Batch Jobres-1000365.3Database
A software demo
Believable resources with provider_
name, account_ id and workload_ name to fill a screen in front of a buyer.
This dataset75 rows17 columns
Yours10,000 rows17 columnsregion: UK only
blueprint · cloud-resource-energy-efficiency-scores
Behind this dataset
Same schema. As many rows as you need.
These 75 rows came out of a blueprint — 17 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.
Rules it was built with
- Each row represents a unique cloud workload
- Energy consumption calculated per workload over a 24-hour period
- Includes only resources deployed in production environments
- Scores normalized across different cloud vendors
- Carbon emission estimates based on provider-reported data
- Must flag workloads exceeding industry average energy use
Open the blueprint in Data Factory
1 credit per row. New accounts start with 25 free credits.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- cloud-resource-energy-efficiency-scores