Top 5 Time Series Databases for High-Frequency Telemetry Data

When processing telemetry data, especially high-frequency time-series data, it is important to choose the right database. Whether you monitor a racing car in real time, tracking sensor data from flight testing, or analysing the simulation results, the database you use must be fast, scalable, and intuitive. In this blog post we break down what parameters you should consider when choosing a database for telemetry data. Comparing the good, the bad and the ugly of some of the most popular time series databases.

Key factors to consider when choosing a database for telemetry data

1. Flexibility

The ability to adapt your database to different projects is very important. Databases that offer flexible structures and data models will give you the versatility needed to meet data diversity and real-time analytics requirements.

2. Scalability

As your telemetry data grows, you need a database that scales efficiently, both vertically (by increasing server capacity) and horizontally (by adding more servers). High-frequency data collection requires a database that can handle growing data volumes without compromising performance.

3. Functionality

The database you choose should support advanced functions like real-time analytics, data aggregation, and complex queries. It should also integrate easily with other tools in your ecosystem, including visualisation and reporting platforms.

4. Performance

Fast read and write operations are crucial for telemetry data. Your database needs to handle the ingestion of large volumes of data and provide quick access for querying and visualisation without lag.

5. Cost

While performance is critical, cost is an important factor too. Consider not only the initial setup costs but also ongoing costs, such as storage, maintenance, and scaling fees. Balancing cost with performance is essential for long-term sustainability.

Most common databases for telemetry data

InfluxDB

InfluxDB is very much recognised as a strong choice for time-series data due to its specific optimisations and flexible data management.

  • Pros:
    • Optimised for Time-Series: InfluxDB’s structure is purpose-built for time-series data, allowing efficient data storage and access.
    • Horizontally Scalable: InfluxDB supports sharing across multiple nodes, making it scalable for large datasets.
    • Flexible Data Retention: You can set retention policies to manage data storage based on importance and relevance.
  • Cons:
    • Custom Query Language: While newer versions support SQL, the traditional InfluxQL query language can be a challenging that are familiar with SQL.
    • Strict Data Model: Older versions of InfluxDB require each data point to include metadata for querying. Although recent updates have introduced a tabular model, there may still be a learning curve.
    • Maturity: Frequent updates and major changes in recent years have led to a fragmented ecosystem, requiring developers to keep up with its rapid evolution.

Best Fit: If you’re already using InfluxDB, tools like Marple can be easily integrated for real-time visualisation, making InfluxDB an effective choice for monitoring IoT data and sensor-based applications.

Azure Data Explorer (ADX)

Azure Data Explorer (ADX) is a powerful option designed for handling high-volume telemetry and real-time data analytics.

  • Pros:
    • High Scalability: ADX can easily grow both by adding more machines (horizontal scaling) and increasing the power of existing machines (vertical scaling). It also uses sharding and partitioning, making it flexible for large operations.
    • Native Integration with Azure: For those already using the Azure ecosystem, connecting ADX to other Azure services is quite easy.
    • Purpose-Built for Real-Time Data: ADX is built for fast data input and analysis, specifically optimised for telemetry and time-series data.
  • Cons:
    • Custom Query Language: ADX uses Kusto Query Language (KQL), which is powerful but requires a learning curve, can be tricky to learn, especially for those who only know SQL.
    • Cost: While ADX offers many features and can grow with your needs, it can be expensive, making it less suitable for smaller projects.
    • Azure-Dependent: Because ADX is part of the Azure ecosystem, moving data or applications outside of Azure can be difficult.

Best Fit: ADX is a great option if you’re already using Azure services. Marple can improve Kusto’s analytics with easy-to-use visuals, making it suitable for real-time analysis in business settings.

TimescaleDB

TimescaleDB builds on PostgreSQL’s strengths, with additional features that help manage time-series data effectively.

  • Pros:
    • SQL-Based: TimescaleDB uses SQL, making it easy to use for teams that are already familiar with PostgreSQL.
    • High Scalability: With built-in support for partitioning, replication, and sharding, TimescaleDB can grow to handle large time-series datasets.
    • Open Source: TimescaleDB is free to use, allowing flexibility without licensing fees.
  • Cons:
    • Still Evolving: While it is based on PostgreSQL, TimescaleDB is relatively new and is still developing its features and ecosystem.
    • Limited Integrations: TimescaleDB may need extra effort to connect with specific applications or visualisation tools.

Best Fit: TimescaleDB is an excellent option if you’re already using PostgreSQL and want a database optimised for time-series without major structural changes. Marple improves Timescale’s analytics by adding visual insights to SQL queries, making it perfect for those looking for a reliable, SQL-based time-series solution.

PostgreSQL

While PostgreSQL isn’t specifically designed for time-series data, it's a strong ecosystem and its flexibility makes it a viable option for those willing to create their own solution.

  • Pros:
    • Flexible and Scalable: can grow vertically and horizontally using partitioning, sharding, and replication.
    • Open Source with Strong Community Support: PostgreSQL is a well-established, license-free database with a strong developer community.
    • SQL-Based: Its native SQL support and general familiarity make it easy for most development teams to adopt.
  • Cons:
    • Limited Visualisation Tools: Unlike specialised time-series databases, PostgreSQL lacks built-in visualisation support, often needing extra tools for data insights.
    • Custom Data Modelling: Using PostgreSQL for time-series requires creating a custom schema and data structure, which can take a lot of effort.
    • Not Optimised for Time-Series: It requires significant work to handle time-series data efficiently, and other databases may better match for real-time data flows.

Best Fit: If you’re already using PostgreSQL and have developer resources, it can serve as a time-series solution with additional tools like Marple for advanced analytics. Consider it if you have an existing investment in PostgreSQL and need flexibility.

QuestDB

QuestDB is a fast and lightweight option for time-series data, offering high data ingestion speeds and SQL compatibility.

  • Pros:
    • High Ingestion Speed: QuestDB is optimised for high-frequency data ingestion, making it great for applications that need rapid data collection and analysis.
    • SQL-Compatible:QuestDB supports standard SQL, making it easy for teams familiar with SQL to adopt.
    • Efficient Query Performance: Its design allows for quick processing of queries, specially with time-series data.
  • Cons:
    • Smaller Ecosystem: QuestDB has a smaller user base, which may limit available resources and third-party integrations.
    • Limited Integrations: Although it's improving, QuestDB’s ecosystem lacks the depth of integration found in more established time-series databases.

Best Fit: QuestDB is ideal for projects that need high data ingestion rates with minimal setup. Its integration with Marple provides real-time insights, making it a great choice for time-sensitive applications.

Conclusion

Choosing the right database for high-frequency time-series data depends on your project's specific needs. Here are some points to consider, after the previous analysis:

  • InfluxDB s designed specifically for time-series data, you need to understand its unique structure and data model, which can limit its flexibility.
  • ADX offers powerful scaling and integrates well within the Azure ecosystem, but it might be less suitable if you want cross-platform options..
  • TimescaleDB  is a time-series version of PostgreSQL and is great if you want SQL support and don’t mind handling some integration.
  • PostgreSQL is very flexible and great for custom requirements, but it may not work perfectly for time-series data right away.
  • QuestDB is a good choice if you need fast data ingestion and SQL compatibility, but it has a smaller ecosystem.

In the end, each database has its own strengths, and your decision will depend on factors like compatibility, scalability, and your team's experience with different query languages. All of these databases work well with tools like Marple for analytics, but your final choice may still boil down to cost, customisation needs, and long-term support.

Ready to take your time-series data analysis to the next level? Try Marple today and see how we can help you unlock real-time insights from your telemetry data, no matter the database you're using.

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