Marple DB
Marple DB is a high-performance data lakehouse designed for processing and standardising time series data from measurement files. Built on top of Parquet and PostgreSQL, it is designed to handle extreme data sizes and measurement frequencies.
Telemetry Data Storage Done Right
Fastest data ingestion available from files (CSV, MDF, MAT...) at 10M datapoints/s- Standardise your data for AI/ML use cases with post-processing and UNS
- Built on open industry standards (Parquet, PostgreSQL, Apache Iceberg)
- SaaS, Virtual Private Cloud (VPC) and self-managed deployment options
Convert measurement files to a Queryable Lakehouse
Plug & play file plugins
Marple DB has built-in plugins for most popular file types such as MDF/MF4, CSV, MAT and many more. That's not enough? 100% customise your own plugin.
Standardise data
Unify data from different sources to enable AI and Data Mining use cases. Use pre-processing to transform data, or map channel names to a Unified Namespace (UNS) using aliasing.
Automatic ingestion
Automate data importing using our SDKs, reducing manual effort and boosting productivity.
Realtime support
Enable live monitoring by appending to datasets in realtime from Python, MATLAB or bare HTTP.
Built on Open Standards
Cold storage with Parquet on Apache Iceberg
Marple DB conforms the Apache Iceberg standard, making compatible with industry-standard query engines like Spark, Trino and PyIceberg.
No lock-in
Marple DB stores your data but you keep 100% ownership. If you ever decide to leave, you can copy the Iceberg storage to anywhere you want.
Hot storage on PostgreSQL
PostgreSQL is the most reliable and robust database engine currently available. Marple DB uses this as a caching layer to achieve x10 speedups for visualising time series.
Scalability for the future
Combining hot, cold and archive storage makes it possible to scale into petabytes of data in a cost-effective way.
.png)
Extreme Performance
Massive ingestion speed
Data ingestion rate goes up to 10 million datapoints / second, outperforming every other solution for importing data from measurement files.
High-frequency support
Sampling rates of up to 200kHz+ tested and verified, enabling advanced use cases like vibration analysis.
High cardinality
Use 200k+ channels per file without loss in performance when querying or ingesting.
Huge files
Support for MAT, MDF, .... files up to 25 GB, with customers having 100 billion+ datapoints per file.
Data Mining example using MATLAB and Python
One of our developers takes you through a use case showing the power of Marple DB. We show data ingestion using the Python SDK, and querying using MATLAB.
Read the SDK documentationExplore all Marple DB features
Optimized for Time Series Data
Marple DB is purpose-built for high-frequency telemetry data, handling measurements from 1Hz to 10kHz or more. Optimized for hundreds or thousands of sensors, it ensures fast, ad-hoc querying through a PostgreSQL layer enhanced with clever data segmentation for seamless scalability.
Convert Data Files to Database
Effortlessly transform time series data from files into a performant database structure with Marple DB. Retain file metadata within the database, use standard plugins for common data flows, or fully customize the setup to match your unique needs.
Integrates Well with Marple Insight
Marple DB connects seamlessly with Marple Insight for effortless data exploration. Its data structure is designed for optimized querying, ensuring smooth workflows. While tailored for Marple Insight, it also integrates easily with other tools in your stack.
Setup Data Streams
Marple DB supports 10 standard plugins for popular file types and enables you to create or modify plugins to suit your needs. Customize and configure data flows for various file types, ensuring smooth integration with your telemetry data sources.
Marple helped us make quick data-driven decisions on complex setup parameters and heavily reduced downtime during testing.
Max Ritzer
Marple simplifies collaboration between engineers. Also, thanks to being a central storage for measurement data, DeepDrive got rid of different locations to store data
Christopher Roemmelmayer
Marple is without doubt the champion in engineering data visualisation. It’s fast, easy to use and focuses on the features I need. Their customer support is super responsive.
Wouter Plaetinck
Deploying Marple into our infrastructure was a smooth and seamless process. The team has been incredibly responsive and collaborative in shaping the solution with us.
Gregor Hannappel
At Hardt Hyperloop, Marple is the point of reference whenever we need multiple eyes looking at a problem. Just a quick share and colleagues can look at exactly the data in question. This is a game changer in data-driven development!"
Gert Spek
We use Marple dashboards right after the flight. Everyone sees the data and can take action fast, it keeps the test program moving.
Stephen Hardiman
Having all data immediately available and accessible for our engineers was a major step forward. Engineers on and off track could follow what was happening with the car and make decisions in real-time
Robbin Baauw
The use of projects in Marple and the option to visualise databases dynamically, has made our life much easier.
Tine Wildiers
At Vertical we use Marple to analyse our rig and flight test data. Marple is the perfect tool for the job: Marple’s interface allows people of varying technical ability to work together using the same tools while not skimping on the in-depth analysis functions users need.
Samuel Meijer
