The demand for time-series databases (TSDBs) is higher than ever. Tracking sensor data, monitoring financial transactions, or analyzing user behavior – the list of time-series database examples and applications is endless.
The best way to understand if these time-series databases are relevant to your business is to know how different companies use them to store time-series data and streamline their processes. Learning from the experiences of others can help you find new and innovative ways to use TSDBs in your processes.
We put this guide together where we will go through 7 time-series database examples to see how you can use its strengths for your business.
What Is A Time-Series Database?
A time-series database (TSDB) is a powerful tool for managing time-stamped data points like sensor, network, and financial data. Its design focuses on capturing, storing, and analyzing data that changes over time. This distinguishes it from relational databases.
What makes TSDBs stand out is they can efficiently manage the volume and velocity of data generated across various industries. They provide different features to handle complex data lifecycle management, like:
Efficient data compression: This reduces storage costs while maintaining quick access to historical data.
High write and read throughput: Ensuring that all the data generated is captured and available for analysis in real-time.
Time-based queries: Helps in time-series data analysis over any given period which is crucial for trend analysis and forecasting.
You can benefit from a time-series database to analyze time-series data. It provides unique advantages that traditional relational and NoSQL databases don’t support.
Whether it is for monitoring environmental conditions through sensor data, tracking financial market movements, or managing network data for telecommunications, TSDBs provide a robust platform for data-driven decision-making.
7 Time Series Database Examples For Advanced Data Management
Here are 7 examples that will show how businesses use time-series databases to innovate solutions and improve operational efficiency.
1. Robinhood's Real-Time Anomaly Detection System
Robinhood is an innovative platform that offers commission-free investing. It played a major role in democratizing finance. To enhance the safety and reliability of its trading platform, Robinhood developed a real-time anomaly detection system.
This system uses InfluxDB, a time-series database, alongside Faust, a stream processing engine, to store and query data for monitoring and mitigating risks efficiently.
Challenges
Robinhood dealt with an extensive amount of time-series data. This made it challenging to detect anomalies as the data volume grew. It became further complicated as they needed real-time processing to quickly identify and resolve issues.
Robinhood needed a robust risk monitoring mechanism that could handle the platform’s vast and complex data without constant manual oversight.
Solution
Robinhood's solution involved a combination of technology and statistical methods:
Adoption of InfluxDB: They chose InfluxDB for its superior data ingestion and processing capabilities, ideal for handling time-series data. Its schema-less design and fast data write/read operations supported Robinhood’s real-time anomaly detection needs.
Statistical anomaly detection: Using a normal distribution model, Robinhood set up a system to alert on data points lying outside 3 standard deviations. This approach helped detect anomalies in non-stationary data and let Robinhood adaptively monitor a wide range of metrics.
Results
This system brought transformative outcomes for Robinhood:
Enhanced risk management: With the implementation of the anomaly detection system, Robinhood could quickly identify and respond to unusual patterns, ensuring platform reliability and user confidence.
Scalability and Flexibility: The system's architecture provided Robinhood with the scalability needed to accommodate growing data volumes. It also offered the flexibility to introduce different anomaly detection methods as business needs evolved.
2. ADLINK's IoT Solution For Predictive Maintenance
ADLINK is a leader in edge computing. It collaborated with a defense contractor to tackle the machine downtime challenge through predictive maintenance. It integrated InfluxDB into its IoT gateway solution for real-time data collection and analysis that could help in proactive maintenance decisions.
Challenges
ADLINK aimed to solve 2 main challenges for their client:
Enhancing operational efficiency: The unpredictability of machine malfunctions caused major revenue loss. They needed a solution that could offer real-time insights for predictive maintenance. The cost of unplanned maintenance visits was around $100,000 annually.
Streamlining data integration: To diagnose machine issues and plan maintenance, they had to effectively connect and analyze data from various sources.
Solution
ADLINK used InfluxDB to offer a comprehensive IoT solution:
InfluxDB for data storage and analysis: ADLINK selected InfluxDB for its high performance and local operation capabilities. These were crucial for addressing data privacy concerns and managing time-series data from IoT devices.
Comprehensive IoT gateway solution: ADLINK connected various data sources to InfluxDB and provided real-time data visualization with Grafana and Node-Red. This setup allowed for effective sensor data monitoring and analysis which helped in predictive maintenance.
Results
This solution deployment had major benefits:
Significant reduction in downtime: The predictive maintenance solution caused a considerable decrease in both planned and unplanned maintenance shutdowns. This enhanced machine performance and reduced maintenance costs.
Operational efficiency and cost savings: Real-time data visualization provided more accurate diagnostics and maintenance scheduling. As a result, it improved operational efficiency and reduced costs for the defense contractor.
3. Optimizing Abios's Data Performance With VictoriaMetrics
Abios is a leader in the Esports data and technology industry. Operating under a Data-as-a-service (DaaS) model, Abios exclusively delivers the following to B2B clients:
Statistics
Compliance solutions
Content visualizations
Probability calculations
Historical and live data
However, Abios faced challenges with high cardinality data that impacted query performance and system reliability.
Challenges
Abios faced 2 main challenges:
High cardinality data management: Identifying specific request patterns and tagging operational data with business insights resulted in high cardinality challenges. This caused query timeouts and performance bottlenecks.
Scalability and performance: They needed a system that could efficiently handle large volumes of time-series data without compromising on query performance or resource usage.
Solution
Abios chose VictoriaMetrics as a streamlined and effective solution for their data management challenges. VictoriaMetrics is a fast, scalable time-series database optimized for monitoring, troubleshooting, and analyzing the growth of large-scale systems. Here’s how they benefited from this transition:
Scalable time-series database: VictoriaMetrics’s design was inspired by ClickhouseDB's MergeTree table. This provided a robust solution for handling high-cardinality data and complex queries.
Cost-effective and efficient operation: VictoriaMetrics had superior query processing and resource utilization performance. This made it highly cost-effective for Abios. Its modular design, comprehensive documentation, and ease of deployment further facilitated the switch.
Results
The results were exactly what Abios was looking for:
Enhanced query performance: Abios saw a big improvement in query performance that previously caused timeouts. This now ensured smooth and reliable data analysis.
Operational efficiency and reduced costs: Along with optimized system performance, VictoriaMetrics helped save big on infrastructure costs because of its efficient resource management and lower CPU usage.
4. Aviso's AI-Driven Sales Boost For Fortune 500 Companies
Aviso integrated artificial intelligence (AI) and time-series database technology to transform how Fortune 500 companies approach sales. This shift helps these companies to turn vast amounts of data into actionable insights. Sales teams then use these insights to make well-informed decisions for increased deal closures.
Challenges
Fortune 500 companies faced 2 main issues:
Data overload and static analysis: Even though companies had a lot of data, they had trouble using static analysis methods. These methods couldn't give them insights beyond the current state. As a result, they missed out on predictive analytics and spotting trends.
Inaccurate sales forecasting: Traditional forecasting methods relied heavily on human judgment which resulted in inaccuracies and missed opportunities.
Solution
Aviso's solution combines time-series databases and AI algorithms:
Time-series database utilization: Aviso uses time-series databases to analyze data over time and uncover patterns and trends that static data analysis misses.
AI-driven insights: Using AI algorithms, Aviso generates accurate win probabilities for sales opportunities. This gives sales teams a clear understanding of where to focus their efforts.
Results
Aviso's technology had impressive outcomes:
Increased deal closures: Companies using Aviso have seen an average 20% increase in deals closed per quarter, thanks to the precision and actionable insights that the AI-driven Aviso WinScores provides.
Enhanced sales strategy: With AI-generated insights and predictions, sales teams can refine their strategies and focus on the most promising opportunities for maximum success.
5. Density Real Estate Management With TimescaleDB
Density is a pioneer in utilizing technology to understand the use of physical spaces. It has recently tapped into time-series databases to revolutionize real estate management.
Through custom-built sensors, Density’s platform provides vital workplace analytics that help large real estate portfolio companies make better decisions. These insights optimize space usage and help reduce carbon emissions.
Challenges
Density faced 2 primary hurdles:
Understanding space utilization: Understanding complex space usage patterns without compromising privacy was a major challenge. They needed to capture detailed sensor data for actionable insights.
Data management and analysis: With a growing global footprint, managing and analyzing time-stamped data from custom-built sensors became crucial. They needed a scalable solution that could handle massive data volumes without sacrificing performance.
Solution
To address these challenges, Density picked TimescaleDB for its advanced data management capabilities:
Efficient data processing: With TimescaleDB’s large dataset handling capabilities, Density could process data from infrared and radar sensors. This processing power provided accurate space utilization counts and valuable metrics generation like dwell time and usage patterns.
Privacy-friendly analytics: Density used non-invasive sensor technologies and processed data at the edge to ensure privacy while still providing actionable insights.
Results
The integration of TimescaleDB into Density’s solution had impressive results:
Optimized space usage: Companies can now use their real estate more effectively with major cost savings and reduced environmental impact.
Enhanced decision-making: Detailed analytics on space usage allow businesses to make informed decisions and improve employee experience and operational efficiency.
6. Space Exploration: The ESA's Use Of PostgreSQL & TimescaleDB
The European Space Agency (ESA) is an intergovernmental organization of 22 member states. It started a mission to explore space for the global citizens’ benefit. One of its departments, the ESDC (ESAC Science Data Center), selected PostgreSQL with TimescaleDB to develop its