Energy
19 columns
120 rows
8 formats

Cloud Infrastructure Energy Efficiency Logs

This dataset provides granular logs of energy consumption, efficiency metrics, and operational context across cloud infrastructure environments. It enables technology firms to analyze sustainability performance, optimize data center operations, and support ESG reporting with detailed, actionable data on energy sources, workloads, and carbon emissions.

last updated 28 Nov 2025
by GoMask

The brief that made it

Sustainability and ESG reporting for cloud operations

Sample rows

preview · 8 of 120 rows · all 19 columns

log_idstring	operation_typestring	utilization_percentagefloat	workload_typestring	timestampdatetime	data_center_idstring	data_center_namestring	location_countrystring	location_citystring	server_idstring	server_typestring	energy_consumed_kwhfloat	energy_sourcestring	renewable_percentagefloat	puefloat	carbon_emissions_kgfloat	workload_idstring	temperature_celsiusfloat	notesstring
LOG001	compute	98.3	ML training	2024-04-01T09:12:00Z	DC_USA01	Silicon Valley Datacenter	USA	San Jose	SRV-SJ01-blade	blade	1445.6	grid	12.5	2.05	452.45	WL-001-compute	33.6	High performance ML workload; considerable energy draw and emissions.
LOG002	compute	91.6	batch processing	2024-04-02T10:18:00Z	DC_IN02	Mumbai HyperCloud	India	Mumbai	SRV-MB02-blade	blade	1112.2	grid	10.1	1.88	343.5	WL-002-compute	35.7	Batch processing during peak hours; moderate renewable integration.
LOG003	compute	82.1	quantum simulation	2024-04-02T17:25:00Z	DC_DE03	Frankfurt GreenDC	Germany	Frankfurt	SRV-FR03-rack	rack	293.8	wind	100	1.18	0	WL-003-compute	18.9	Quantum simulation workload running on green power.
LOG004	networking	2.1	network monitoring	2024-04-03T13:09:00Z	DC_CA01	Toronto CloudHub	Canada	Toronto	SRV-TOR01-rack	rack	21.9	hydro	95.4	1.12	0.03	WL-004-network	15.2	Low network utilization period; mostly renewable energy.
LOG005	storage	77	backup archival	2024-04-04T11:33:00Z	DC_FR01	Paris MetroDC	France	Paris	SRV-PA01-tower	tower	456.2	solar	88.7	1.37	15.2	WL-005-storage	20.1	Backup archival workload; efficient solar usage.
LOG006	compute	59.8	analytics	2024-04-04T16:45:00Z	DC_UK01	London DockCloud	UK	London	SRV-LD01-blade	blade	121.4	wind	99.2	1.09	0	WL-006-compute	17.9	Analytical workload; near-complete wind energy usage.
LOG007	compute	100	video streaming	2024-04-05T09:00:00Z	DC_BR01	Rio MegaDC	Brazil	Rio de Janeiro	SRV-RJ01-blade	blade	1897.2	grid	2.6	3.15	620.4	WL-007-compute	39.8	Mega event; extremely high energy and emissions.
LOG008	storage	68.4	archival	2024-04-06T07:18:00Z	DC_SG01	Singapore DataGrid	Singapore	Singapore	SRV-SG01-rack	rack	294.7	hydro	100	1.23	0	WL-008-storage	22.4	Archival workload; fully renewable hydro energy.
LOG009	compute	97.6	batch processing	2024-04-06T21:17:00Z	DC_MX01	Mexico City CloudZone	Mexico	Mexico City	SRV-MEX01-blade	blade	1207.5	grid	7.2	2.19	364.8	WL-009-compute	36.1	Heavy batch processing; grid-powered, high emissions.
LOG010	networking	0.2	IoT gateway	2024-04-07T08:45:00Z	DC_NO01	Oslo EcoDC	Norway	Oslo	SRV-OS01-rack	rack	2.3	wind	100	1	0	WL-010-network	-3.4	Cold standby; IoT gateway, minimal energy draw.
LOG011	storage	42.9	database ops	2024-03-28T12:21:00Z	DC_DE04	Berlin CloudPark	Germany	Berlin	SRV-BER04-tower	tower	188.2	solar	79.2	1.45	18.2	WL-011-storage	19.7	Database operations with solar energy support.
LOG012	compute	99.9	ML training	2024-03-29T14:30:00Z	DC_CN01	Shanghai CloudCore	China	Shanghai	SRV-SH01-blade	blade	1335.9	grid	13.7	2.08	415.2	WL-012-compute	38.5	ML training workload at peak utilization.
LOG013	storage	51.8	backup archival	2024-03-30T14:00:00Z	DC_CA02	Vancouver HydroDC	Canada	Vancouver	SRV-VAN02-rack	rack	239.5	hydro	100	1.15	0	WL-013-storage	17.6	Hydro-powered backup archival with low emissions.
LOG014	compute	91	web hosting	2024-03-30T21:02:00Z	DC_IN03	Bangalore CloudEdge	India	Bangalore	SRV-BLR03-blade	blade	1029.1	grid	19.4	1.93	318.6	WL-014-compute	32.7	Web hosting during business peak; grid energy.
LOG015	compute	64.1	analytics	2024-03-31T08:45:00Z	DC_DE05	Hamburg GreenDC	Germany	Hamburg	SRV-HAM05-rack	rack	301.6	wind	97.6	1.11	0	WL-015-compute	19.1	Data analytics on wind power; low emissions.
LOG016	compute	56.2	ML training	2024-03-31T20:10:00Z	DC_UK02	Manchester CloudDC	UK	Manchester	SRV-MAN02-blade	blade	157.2	solar	100	1.07	0	WL-016-compute	17.8	ML training powered exclusively by solar.
LOG017	storage	81.9	backup archival	2024-03-28T07:13:00Z	DC_FR02	Marseille DataVault	France	Marseille	SRV-MARS02-tower	tower	352.8	solar	90.2	1.44	8.5	WL-017-storage	21.2	Efficient archival storage; solar-dominated.
LOG018	compute	95.2	web hosting	2024-03-29T08:10:00Z	DC_USA02	Dallas CoreDC	USA	Dallas	SRV-DAL02-blade	blade	1086.8	grid	7.9	2.23	331.7	WL-018-compute	34.5	Web hosting workload; high grid dependency.
LOG019	compute	96.5	batch processing	2024-03-29T19:00:00Z	DC_IN04	Delhi MetroDC	India	Delhi	SRV-DEL04-blade	blade	1278.6	grid	13.1	2.01	397.1	WL-019-compute	38.3	Batch processing at high utilization; grid energy.
LOG020	compute	99.8	video streaming	2024-03-30T23:59:00Z	DC_BR02	Sao Paulo CloudZone	Brazil	Sao Paulo	SRV-SP02-blade	blade	1622.3	grid	3.8	2.97	552.7	WL-020-compute	39.2	Peak streaming event; very high energy usage.

What the 120 rows show

from the 120-row sample

Networking (operation type) stands out: mean utilization_percentage is 1.5, against 72.9 for the rest.

68.0median utilization_percentage
3server types
5energy sources
15location countries
186.4median energy_consumed_kwh
95.5median renewable_percentage

Mean utilization_percentage by operation_type120 rows

utilization_percentage120 rows, in bands of 10

Median 68.0, from 0.0 to 100.0.

workload_type120 rows · top 10 of 16 values

ML training22
backup archival20
analytics14
batch processing11
network monitoring8
database ops8
archival7
quantum simulation6
web hosting6
video streaming5

19 columns by typefrom the column list below

string 12
float 6
datetime 1

Columns

19 columns in three groups

blueprint · 19 columns
column	type	description	example
Text 12 columns
`log_id`	string	Unique identifier for each energy efficiency log entryunique	LOG001
`data_center_id`	string	Unique identifier for the data center where the measurement was taken	DC_USA01
`data_center_name`	string	Human-readable name of the data centeroptional	Mumbai HyperCloud
`location_country`	string	Country where the data center is located	USA
`location_city`	string	City where the data center is locatedoptional	San Jose
`server_id`	string	Unique identifier for the server or rack being measured	SRV-SJ01-blade
`server_type`	string	Type or model of server (e.g., blade, rack, tower)3 types · optional	blade
`operation_type`	string	Type of cloud operation (e.g., compute, storage, networking)compute · storage · networking · other	compute
`energy_source`	string	Primary energy source used (e.g., grid, solar, wind, hydro)grid · solar · wind · hydro · other · optional	grid
`workload_id`	string	Identifier for the specific workload or application running during measurementoptional	WL-001-compute
`workload_type`	string	Type of workload (e.g., ML training, web hosting, batch processing)optional	ML training
`notes`	string	Additional notes or comments about the measurement or conditionsoptional	High performance ML workl…
Numbers 6 columns
`energy_consumed_kwh`	float	Total energy consumed during the measurement period, in kilowatt-hours0 or more	1445.6
`renewable_percentage`	float	Percentage of energy from renewable sources (0-100)0 to 100 · optional	12.5
`pue`	float	Power Usage Effectiveness metric for the data center during the measurement period1 or more · optional	2.05
`carbon_emissions_kg`	float	Estimated carbon emissions produced during the measurement period, in kilograms0 or more · optional	452.45
`utilization_percentage`	float	Percentage of server or resource utilization during measurement (0-100)0 to 100 · optional	98.3
`temperature_celsius`	float	Ambient temperature in the data center at time of measurement, in degrees Celsiusoptional	33.6
Dates and times 1 column
`timestamp`	datetime	Date and time when the energy measurement was taken	2024-04-01T09:12:00Z

Use it for

An energy dashboard
Utilization_percentage by operation_type and a breakdown of workload_type. Excel, Power BI or Tableau.
Why do the 16 networking rows have a mean utilization_percentage of 1.5?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Logs120
LOG00198.3compute
LOG00291.6compute
LOG00382.1compute
A software demo
Believable logs with timestamp, data_center_id and data_center_name to fill a screen in front of a buyer.

This dataset120 rows19 columns

Yours1,000 rows19 columnslocation_country: UK only

blueprint · cloud-infrastructure-energy-efficiency-logs

Behind this dataset

Same schema. As many rows as you need.

These 120 rows came out of a blueprint — 19 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 daily log from a unique cloud server or data center.
Includes metrics: power usage effectiveness (PUE), total energy consumed (kWh), renewable energy percentage, and cooling system efficiency.
Outlier values for PUE (>2.0) flagged for site audit.
Renewable energy percentage must be between 0 and 100.
Monthly aggregates available for trend analysis and ESG reporting.

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: cloud-infrastructure-energy-efficiency-logs

What should your data show?

No signup. No card.

Sample rows

What the 120 rows show

Columns

Use it for

An energy dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Energy

What should your data show?