Technology datasets

Software logs, user behavior, API data

  • 83 ready-made datasets
  • 8 formats, Excel to Parquet
  • First download free
What the 83 cover1 square = 1 dataset
  • Security & access18
  • Remote collaboration17
  • IoT & infrastructure16
  • Product & app usage15
  • Support & incidents14
  • Everything else3
Grouped by dataset title and tags.

83 datasets · page 4 of 4

Showing all 11 on this page

IoT & infrastructure

Smart City Sensor Event Streams

Aggregates real-time event streams from diverse IoT sensors deployed across urban

This dataset aggregates real-time event streams from diverse IoT sensors deployed across urban environments, capturing air quality, noise, light, and pedestrian flow metrics with precise geolocation and timestamping. It enables advanced urban monitoring, anomaly detection, and data-driven resource allocation for smart city initiatives. The dataset's granular, multi-modal structure supports both operational dashboards and in-depth analytics for city planners and researchers.

16 cols

  • sensor_type
  • location_latitude
  • location_longitude
  • air_quality_pm25
  • +12
Open in factory
sensor_type
air_quality
location_latitude
40.748817
location_longitude
-73.985428

Product & app usage

Software Feature Usage Frequency

User, feature, session, platform, and application version context

This dataset provides detailed, event-level tracking of software feature usage, including user, feature, session, platform, and application version context. It enables product teams to analyze feature adoption, identify usage trends, and prioritize development or training efforts based on real user behavior.

10 cols

  • feature_name
  • usage_count
  • platform
  • app_version
  • +6
Open in factory
feature_name
Dashboard View
usage_count
1
platform
web

Support & incidentsTop 10 most opened

IT Service Ticket Classification

IT service tickets, combining structured metadata (such as priority

This dataset contains detailed records of IT service tickets, combining structured metadata (such as priority, category, and assignment) with rich ticket descriptions suitable for natural language processing. It enables automated ticket triage, prioritization, and advanced analytics for IT support operations, making it ideal for machine learning and process optimization.

20 cols

  • ticket_subject
  • created_datetime
  • updated_datetime
  • department
  • +16
Open in factory
ticket_subject
Laptop battery fails to charge
department
Finance
category
hardware

Product & app usage

User Engagement Frequency Dataset

Session frequency, feature adoption, and activity recency

This dataset provides detailed user-level engagement metrics, including session frequency, feature adoption, and activity recency, enabling identification of power users and analysis of retention patterns. With cohort, platform, and geographic segmentation, it is ideal for optimizing user engagement strategies, tracking feature adoption, and supporting data-driven product decisions.

11 cols

  • power_user_flag
  • total_sessions
  • average_session_duration_minutes
  • days_active_past_30
  • +7
Open in factory
power_user_flag
true
total_sessions
112
average_session_duration_minutes
23.6

Product & app usage

Feature Request Prioritization Log

Detailed descriptions, scoring for business value and stakeholder impact

This dataset provides a comprehensive log of software feature requests, including detailed descriptions, scoring for business value and stakeholder impact, prioritization metrics, and workflow status. It enables technology teams to prioritize development efforts, align stakeholders, and plan product roadmaps with data-driven insights.

17 cols

  • requester_name
  • priority_score
  • title
  • stakeholder_impact_score
  • +13
Open in factory
requester_name
Jessica Lee
priority_score
2.96
title
Add dark mode to dashboard

Support & incidents

Bug Report Resolution Timelines

Detailed descriptions, severity, priority, assignment

This dataset provides comprehensive logs of software bug reports, including detailed descriptions, severity, priority, assignment, and timestamps for each stage of the bug lifecycle. It enables in-depth analysis of resolution timelines, developer efficiency, and project quality, making it valuable for process optimization and engineering management.

17 cols

  • resolved_at
  • resolution_time_hours
  • title
  • severity
  • +13
Open in factory
resolution_time_hours
7.25
title
App crashes on login
severity
critical

Support & incidents

Customer Service Call Resolution

Caller and agent details, issue categories, resolution status, escalation

This dataset provides comprehensive tracking of customer service calls, including caller and agent details, issue categories, resolution status, escalation, and customer satisfaction ratings. It enables organizations to analyze service quality, monitor agent performance, and identify trends or bottlenecks in call resolution processes. Ideal for improving response times, optimizing support operations, and enhancing customer experience.

18 cols

  • customer_name
  • call_datetime
  • call_duration_seconds
  • resolution_status
  • +14
Open in factory
customer_name
Maria Gomez
call_duration_seconds
398
resolution_status
resolved

Product & app usage

Software Feature Usage Frequencies

The frequency of software feature usage across users, organizations

This dataset logs the frequency of software feature usage across users, organizations, platforms, and time periods, enabling detailed analysis of user engagement and feature adoption. It supports granular tracking of how often each feature is used, by whom, and in what context, empowering data-driven decisions for feature development and prioritization.

11 cols

  • feature_name
  • usage_count
  • usage_period_start
  • usage_period_end
  • +7
Open in factory
feature_name
Login
usage_count
12
usage_period_start
2024-05-01

Support & incidents

IT Incident Severity Escalations

IT support incidents, capturing severity levels, escalation history

This dataset provides detailed logs of IT support incidents, capturing severity levels, escalation history, and resolution timelines. It enables organizations to analyze incident trends, monitor escalation patterns, and identify resource bottlenecks, supporting continuous improvement in IT service management.

16 cols

  • incident_title
  • severity_level
  • escalation_count
  • last_escalated_at
  • +12
Open in factory
incident_title
Unable to connect to VPN
severity_level
Medium
escalation_count
1

Support & incidents

Product Issue Escalation Logs

Escalation history, resolution details, and customer feedback

This dataset provides comprehensive logs of customer-reported product issues, including escalation history, resolution details, and customer feedback. It enables support teams to monitor issue lifecycles, analyze escalation patterns, and improve product and service quality through actionable insights.

22 cols

  • product_name
  • issue_type
  • escalation_level
  • escalated_datetime
  • +18
Open in factory
product_name
VisionX Monitor
issue_type
defect
escalation_level
1

Support & incidents

Bug Fix Prioritization Dataset

Detailed descriptions, categorization, prioritization, workflow status

This dataset provides a comprehensive record of software bugs, including detailed descriptions, categorization, prioritization, workflow status, assignment, and resolution information. It enables technology teams to efficiently triage, track, and resolve issues, supporting high-quality software delivery and informed resource allocation. The dataset is ideal for analytics, process optimization, and quality assurance in software development.

23 cols

  • priority
  • title
  • reported_by
  • status
  • +19
Open in factory
priority
Critical
reported_by
alice.chen@example.com
status
Open

What’s inside. Column by column.

Columns and codes found in the sample rows of all 83 technology datasets.

Most common columnsDatasets, of 83
  1. device_type24
  2. user_id21
  3. device_id16
  4. status14
  5. log_id13
  6. event_id12
  7. incident_type12
  8. event_type11
  9. priority11
A person from the User Accounts datasetGenerated
first_name
Jamie
last_name
Walker
date_of_birth
1986-11-12
address_city
Manchester

Generated

Standard codes in the sample rowsDatasets, of 83
  1. ISO 3166 countries27
  2. IMEI devices1

Not quite what you need? Describe it.

One sentence in. A dataset with that pattern out.

  • 3+ handoffs breach 3×
  • Real HTTP status codes
  • Duplicate tickets
Preview 20 rows freeNo signup. No card.
SLA breaches by reassignments
0%10%20%5%06%18%217%3+1
Sample rows for: Support tickets where tickets reassigned 3 or more times breach SLA three times as often, with real HTTP status codes and a few duplicate tickets.
reassignmentserror_codeprioritysla_breached
3HTTP 503P1yes
0HTTP 401P3no
4HTTP 500P2yes
4HTTP 500P2yes

A pattern you asked for Everything else

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