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.

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Most common databases for telemetry data

Marple DB

MarpleDB is a high-performance database designed specifically for storing and querying large volumes of time-series data. Built on PostgreSQL and object storage in Parquet, it optimises data organisation, streamlines ingestion, and enables fast, efficient analysis and insights.

Pros:

• File-Based Data Ingestion: Supports automatic ingestion from various file formats such as CSV, MAT, TDMS, and MDF, with built-in plugins for common data flows.
• Realtime Support: Built-in realtime queueing system to handle measurements from 1Hz to 10kHz or more, making it ideal for applications like motorsport telemetry, automotive engineering, and aerospace testing.
• Automatic Data Partitioning: Organizes data into partitions for optimized storage and accelerated query performance.
• Queryable Cold Storage: Allows direct access to the cold storage (Parquet) for data mining use cases across thousands of tests.
• Lot's of hosting options: Offers both SaaS, Virtual Private Cloud (VPC) and self-managed licensing.
• Seamless Integration with Marple Insight: Connects effortlessly with Marple Insight for advanced analytics and deeper insights into your data.

Cons:

• Growing Ecosystem: Marple DB currently offers SDK for both MATLAB and Python, but for other integrations like Julia and R, you still need to call the API directly.
• Engineering Niche: Marple DB is highly optimised for engineering use cases. For other types of data such as inventory keeping or documenting the R&D process, you would need to store this type of data in a separate database.

Best Fit:

MarpleDB is ideal for teams needing a fast, scalable, and telemetry-focused database. Its file-based data ingestion and deep integration with Marple Insight enable hassle-free, real-time analytics and visualisation, without any additional setup.

InfluxDB

InfluxDB remains one of the most recognised databases for time-series data, thanks 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 Insight 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 Insight can improve Kusto’s analytics with easy-to-use visuals, making it suitable for real-time analysis in business settings.

Tigerdata (formerly TimescaleDB)

TigerData, formerly known as TimescaleDB, builds on PostgreSQL to handle time-series data.

• Pros:
  • SQL-Based: Using PostgreSQL as its foundation, Tigerdata is easy to use for teams that are already familiar with PostgreSQL.
  • High Scalability: With built-in support for partitioning, replication, and sharding, Tigerdata can grow to handle large time-series datasets.
  • Flexible Deployment: Open-source TimescaleDB edition is available for self-hosted use.
• Cons:
  • Still Evolving: While it is based on PostgreSQL, TimescaleDB is relatively new and is still developing its features and ecosystem.
  • Limited Integrations: Tigerdata may need extra effort from an IT-savvy person to connect with specific applications or visualisation tools.

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

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 Insight provides real-time insights, making it a great choice for time-sensitive applications.

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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:

• Marple DB is purpose-built for telemetry data, offering speed, scalability, and seamless integration with Marple Insight.
• 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..
• Tigerdata is a time-series version of PostgreSQL and is great if you want SQL support and don’t mind handling some integration.
• 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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