Catalog
concept#Data#Analytics#Database#Time Series

Time Series Databases

Time series databases are specialized data stores for storing and analyzing time-ordered data.

Time series databases enable efficient processing and analysis of data captured over time points.
Established
Medium

Classification

  • Medium
  • Technical
  • Technical
  • Intermediate

Technical context

REST APIs for data accessBI tools for data analysisETL tools for data extraction and loading

Principles & goals

Use the right metrics for data analysis.Optimize data structure for time series data.Ensure data integrity throughout the process.
Build
Team, Domain

Use cases & scenarios

Compromises

  • Data loss due to insufficient backup.
  • Scaling issues with large data volumes.
  • Overloading system resources.
  • Perform regular data backups.
  • Maintain transparent documentation of all processes.
  • Implement continuous training for the team.

I/O & resources

  • Identify data sources
  • Define data collection methods
  • Provide infrastructure for data processing
  • Data analyses and reports
  • Real-time metrics
  • Data trends and predictions

Description

Time series databases enable efficient processing and analysis of data captured over time points. They are ideal for applications in finance, IoT, and research data analytics.

✔

  • Efficient storage of regular data.
  • Real-time analysis enables quick insights.
  • Optimization of decision-making based on data.

✖

  • Not suitable for unstructured data.
  • Higher costs compared to traditional databases.
  • Complexity in data migration.

  • Data Integrity

    Measurement of data accuracy and completeness.

  • Real-time Processing Time

    Time required to process data in real time.

  • Scalability Rate

    Ability of the database to scale with growing data volume.

Financial Analysis at ACME Corp.

ACME Corp. uses time series databases to monitor its investments and conduct market analyses.

IoT Data Processing at SmartHome Inc.

SmartHome Inc. continuously analyzes data from connected devices to understand user behavior.

Weather Forecasting at Meteorology Co.

Meteorology Co. uses time series databases for accurate weather forecasting based on historical data.

1

Train employees on using the systems.

2

Set up the necessary infrastructures.

3

Check data quality and integrity.

⚠️ Technical debt & bottlenecks

  • Using outdated software components.
  • Insufficient testing of the systems.
  • Lack of capacity management.
High latency for large queries.Limitations in adaptability.Challenges in data migration.
  • Processing too large amounts of data at once.
  • Storing data without validation.
  • Ignoring appropriate security measures.
  • Faulty implementation of data storage.
  • Lack of monitoring of system performance.
  • Insufficient planning for future scaling.
Knowledge in database managementSkills in data analysisUnderstanding of time series data
Real-time analytics are crucial.Scalability must be planned from the start.Data integrity is essential.
  • • Data must be in the correct format.
  • • Technical infrastructure requires special software.
  • • Operational processes must be considered.