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7 Cutting-Edge Time Series Database Examples For 2024

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?


Time Series Database Example - What Is 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


Time Series Database Example - 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


Time Series Database Example - 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


Time Series Database Example - 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


Time Series Database Example - 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 “Digital Library of the Universe”


This extensive archive contains data from more than 20 planetary, heliophysics, and astronomy missions. It is an important resource for scientific research and public knowledge.


Challenges


For ESA, the major challenge was to manage and use the massive amounts of collected data:


  • Data diversity and volume: With so many data types, including geospatial and time-series data, the ESA needed a database system that could store, manage, and analyze this information, which could quickly scale into tens of terabytes.

  • Access and integration: A strong and adaptable database system was needed that scientists could use to access data widely and cross-reference data from different missions. This system needed to support a unified query language and a wide-ranging set of tools.


Solution


ESA implemented PostgreSQL with TimescaleDB for its time-series data needs:


  • PostgreSQL for diverse data management: As an open-source relational database, PostgreSQL could handle various data types, including relational metadata, geospatial data, and unstructured data. This made it ideal for the “Digital Library of the Universe”.

  • TimescaleDB for time-series scalability: TimescaleDB, a powerful extension of PostgreSQL, was selected for its efficient time-series data management. It provides automatic partitioning, high insert rates, and integration with PostgreSQL's ecosystem. This allows the ESA to effectively handle large-scale time-series datasets.


Results


Here’s how PostgreSQL and TimescaleDB helped ESA:


  • Enhanced data utilization: Now scientists can access and study the ESA's large datasets more effectively. This helps them make new discoveries and gain deeper insights into the universe.

  • Scalable and flexible data management: Using PostgreSQL and TimescaleDB together provides a scalable and flexible solution that meets the ESA's complex data storage and analysis needs. This setup supports both ongoing and future missions.


7. Kuayue Express Group's Evolution With TDengine


Time Series Database Example - Kuayue Express Group's Evolution With TDengine

Kuayue Express Group is a leading logistics and courier service in China. It has integrated TDengine, a specialized time-series database, to redefine its vehicle tracking and logistics management. This strategic move has streamlined their operations and also set a new benchmark for efficiency and performance in the logistics industry. 


Kuayue Express switched from a mixed database environment to TDengine which improved its real-time vehicle tracking data management. This resulted in remarkable improvements in their operations.


Challenges


Initially, Kuayue Express faced many issues:


  • Inefficient data handling: The original system relied on HBase for storage and Redis for real-time data processing causing scalability and performance bottlenecks.

  • Complex system management: The dual-database setup made it difficult to manage and integrate vast amounts of logistics data. This hindered real-time analysis and decision-making.


Solution


The adoption of TDengine helped them in a big way:


  • Streamlined data processing: When they migrated to TDengine, it consolidated real-time and historical data processing into one efficient platform that simplified the data architecture.

  • Enhanced performance: TDengine’s optimized time-series data handling improved data compression, storage efficiency, and query performance.


Results


Kuayue Express had major operational gains:


  • Resource optimization: When Kuayue Express shifted to TDengine, it reduced the server count and daily data storage needs, which saved them a lot of money.

  • System efficiency: The new system now supports the high-speed processing of vehicle tracking data. This enhances real-time monitoring and analysis capabilities.


Timeplus: Revolutionizing Real-Time Data Analytics


Time Series Database Example - Timeplus

Timeplus is a streaming-first data analytics platform with end-to-end capabilities. It transforms streaming and historical stored data processing across industries. Timeplus uses Proton’s open-source streaming database which lets data engineers and platform engineers tap into the value of streaming data with SQL. This makes it ideal for real-time data needs.


Timeplus provides seamless integration with a large number of data sources like Apache Kafka, Confluent Cloud, Redpanda, and CSV file uploads. With it, you can look into streaming patterns with SQL queries, send real-time insights and alerts, and create engaging dashboards and visualizations.


Timeplus uses a column-based data format, Timeplus Data Format (TDF) for high-performance storage. This format supports quick serialization and deserialization for efficient analytic computations. Vectorized data ensures top-notch performance during analytics.


Timeplus offers features specifically designed for time-series data management. It can efficiently handle large data volumes, like sensor information, financial records, and log files.


Timeplus’ Major Enhancements For Time-Series Databases


  • Windowed stream processing: Timeplus supports segmentation and analysis over defined periods which is crucial for time-based data analysis.

  • Time-based retention: Timeplus implements retention strategies based on data freshness to optimize storage management and large data volume handling.

  • Streaming predictive analytics: With Timeplus, you get forecasting capabilities based on real-time data streams. This helps identify trends and patterns in time-series data.

  • Event time processing & time-series index: Timeplus uses actual event times for data organization and analysis. This enhances event data retrieval and analysis efficiency.

  • Time-series data visualization & real-time analytics: Timeplus offers unparalleled real-time visualization and analysis for immediate insights from streaming data. This is essential for real-time information databases.

  • Streaming-first architecture & high performance: Timeplus uses a column-based data format and NativeLog storage for superior data ingestion and querying performance. This makes it ideal for managing high-velocity time-series data.


Conclusion


Every time-series database example we discussed shows how quickly this field is changing to meet the growing needs of modern data management. As you consider a time-series database for your projects, assess your requirements and objectives carefully.


Choosing the best database depends on scalability, ease of use, and compatibility with your existing systems. While cutting-edge features are exciting, prioritize stability, security, and proven performance if your project is mission-critical.


If you are looking for a high-performance, streaming-first data analytics platform, Timeplus is an excellent option. It provides instant data ingestion and analysis, advanced windowed processing, and precise event time indexing to deliver real-time visibility and actionable insights from time-stamped data. 


Timeplus makes time-series data analytics highly accessible so you can quickly integrate it into your existing workflows. Sign up for a free trial or request a live demo now.

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