Marple resources

Grafana has long been one of the go-to for engineers and developers seeking open-source observability. However, as teams scale and the need for clarity and speed intensifies, Grafana's strengths can sometimes become limitations.

When processing telemetry data, especially high-frequency time-series data, it is important to choose the right database.

A lot of engineering and R&D teams are switching to databases to store test and measurement data. In our conversation with Atlas Copco, we dove into the advantages this approach brings to your organisation.

However, as data volumes grow, so do the costs associated with running a database. This blog post will explore various strategies to control its costs without compromising on performance or functionality.

‍

I’m often frowned upon if I tell people that I like CSV files for storing time series data. The criticism is usually one of these three:

1. CSV files are huge
2. There is no standardised way to add metadata
3. Different sample rates are hard to combine in one file

And this is true. Despite this, the power of CSV becomes apparent when looking at data analysis from the practical perspective of an engineer. It is fast, easy to read and has rich tooling.

‍

Last month, we were featured in the Renumics’ list of the “Top 15 Data Analysis Tools for test engineers in 2024”. We have to say that we are incredibly grateful for this recognition and proud to stand out among other top tools like Microsoft Power BI, Grafana, and Tableau.

Marple, recently closed a new investment round to support their growth into the automotive and aerospace industry. The round was led by Network Venture Partners and joined by Birdhouse Ventures and imec.istart.

In 2024, we had many teams that made use of Marple for the data analysis of their telemetry data. And what an extraordinary year it was for “our Formula Student teams”. We've witnessed multiple records being smashed, showing these young engineers' incredible talent and dedication. And of course, we are very proud to be part of this story!

‍

In today’s fast-paced data-driven world, managing large volumes of data efficiently is critical for success. As companies deal with increasingly complex and high-frequency sensor data, the limitations of traditional file-based data management have become apparent.

Everybody who works with data - and we are talking about a big amount of it- knows that having the right tools to analyse and visualize data is crucial.

When engineering teams prototype or build new systems, they start capturing simulation or sensor data. The next thing they want to do is dive in the data, making visualisations and analysis. At this moment, the discussion often sparks again: should we buy an external tool, or build one ourselves?

In this blog post, we briefly dive into the advantages of both choices.

In the dynamic world of data analytics, Azure Time Series Insights (TSI) stood out as a comprehensive tool, combining data storage and analysis. However, as of July 7th, 2024, Azure TSI has been officially discontinued, pushing its users to seek viable alternatives to continue their time series data analytics operations.

‍

Time series data is an essential part of engineering, and visualizing it is crucial for understanding patterns, trends, and anomalies. There are many free online tools available to engineers for creating graphs from time series data, and each has its own strengths and weaknesses. In this blog post, we will compare Marple, Google Sheets, Jupyter Notebooks, Grafana, Metabase, and Plotly Chart Studio on various parameters and help you decide which tool is the best fit for your requirements.

In this post, we will walk you through the process of integrating the Marple API into your workflow, along with highlighting some of its key benefits and functionalities. We will cover how to set up the API, upload and import files, manage metadata, and share projects and sources, making your data management and visualization effortless.

PID controllers: whether you are a control engineer or not, I am sure you have heard about it.

Due to their simplicity and robustness, PID controllers are one of the most popular control methods. They are being used in many different industries and many different applications. PID controllers can be (and need to be) tuned specifically for the application. Tuning will also impact the performance of the controller: how quick it responds, how much overshoot, how it reacts to vibrations, etc …

‍

In this blog post, we explain why Marple is so good and fast at drawing plots of large amounts of time series data.

How do we visualise so many data points? The short answer is: we don’t.

We smartly select which data points to show, and give a small twist to how we show them.

Like this, we manage to quickly load plots that are accurate enough to analyse your data.

‍

Last Thursday, we were thrilled to host Wouter Plaetinck, flight test engineer at Lilium, who talked about the ongoing projects happening at Lilium and how they integrated Marple as an essential tool in their flight test department.

Lilium is a leading company in the development of an electric aircraft. They are currently developing a vertical take-off and landing jet that can take onboard passengers. Currently, they are testing with Phoenix, a jet that is pilotted from the ground. For this, they perform many test flights at their facility in Spain. Lilium has been quickly growing in the past years; four years ago, they were a company with 100 employees, but now it has become a company where more than 800 people work. As a result, their test capabilities have also grown. Wouter explains:

‍

Marple founder Nero measured his body weight every 15 minutes for a full day. How much dus it vary?

‍

Time series databases are a viable alternative to files for storing engineering data. InfluxDB and Timescale are the two most well known alternatives currently available. In this article we dive into their differences on Data model, Query language, Performance, and Stability. Our findings below are based on 40+ conversations we had over the past 3 years, with users of both databases.

This article provides an in-depth analysis of energy usage in Belgium, including patterns in energy consumption and production, and an examination of the different types of energy sources being used. The article includes dynamic graphs and statistical analysis of the data.

‍

Learn how a solar racing team uses data to gain insights from their solar panels, batteries and race strategy.

Variable stars have captured the attention of astronomers and amateur stargazers alike for centuries. Discover the data behind these fascinating astronomical objects.

We are excited to announce that we are joining the InfluxData ecosystem. InfluxDB is the leading time series platform for storing test and measurement data. Marple can now be coupled to an InfluxDB database, giving engineers the power of both. In this blog post, we dive deeper into what the integration offers and how to set it up.

Engineers traditionally capture sensor data with loggers. Most of these devices store their data in files: csv, mat, tdms, hdf5, … But in recent years, we have seen our customers expand their workflows to also ingest it in time series databases, such as InfluxDB. Having the sensor output in a database gives a clean interface from which other tooling can consume the data. We believe this is a great step forward in opening up the data to web-based tooling for engineers.

But sensor data is a unique use case for InfluxDB. The data is time series, but it is often not one continuous stream of data. Measurements are frequently done as part of an ad hoc test or larger testing campaign. These tests typically have a start and end time and can be sampled as fast as 10 kHz. Visually analysing this kind of data asks for tooling that is highly interactive.

‍

Hello there!

We've had a busy couple of months since our last blog post. In this post we want to give you an update on two elements: our second test period and our first summer interns. Let's go!

We've had a very successful second test period in May-June with more than 100 users testing our tool and providing valuable feedback. In total almost one hundred billion data points were analyzed by Marple during this period. That's amazing. We've seen new use cases that we find very interesting and open a new range of opportunities. We're currently brainstorming about our product to see if we can capitalize on these opportunities. But more on that in our next blog post.

‍

Marple supports the Agoria Solar Team

The Agoria Solar Team is a team of KU Leuven students from various engineering studies who work together to create a new solar car every 2 years. With this solar car, we participate in international solar challenges against teams from all around the world. I am the race strategist in the team, and my function is to decide on the most optimal way to drive during te race, to achieve the best results.

‍

We are very happy to announce that Marple has joined the imec.istart accelerator programme! Imec.istart is a startup accelerator in Belgium with a focus on tech startups. Therefore, it is a perfect fit for our company. The accelerator is a branch of IMEC, a renowned research center with a focus on microelectronics.

Imec.istart will support us in various ways including funding, coaching & mentoring, workshops and access to its large network. To top it off, we will be moving our offices to a co-working space in Antwerp. We look forward to joining a community of fellow startups when the corona dust has settled down.

Matthias & Nero

‍

‍

Marple haalt half miljoen euro op om ingenieurs razendsnel inzicht te geven in hun data.

ANTWERPSE TECH-STARTUP MARPLE HAALT HALF MILJOEN OP

De Antwerpse tech-start-up Marple heeft in een tweede investeringsronde 500.000 euro aan groeikapitaal opgehaald. De investeringsronde was gevuld na minder dan 1 maand door een gezonde interesse bij private investeerders. Ook Imec en VLAIO namen deel aan de investeringsronde. Het verse kapitaal moet Marple helpen het team uit te breiden en verder aansluiting te vinden op de markt van software voor R&D ingenieurs.

‍

Marple raises half a million euro to give engineers lightning-fast insight into their data

ANTWERP TECH START-UP MARPLE RAISES HALF A MILLION EUROS

Antwerp-based tech start-up Marple has raised 500,000 euros in growth capital in a second investment round. The investment round was filled after less than one month due to healthy interest from private investors. Imec and VLAIO also participated in the investment round. The fresh capital will help Marple expand its team and further connect with the software market for R&D engineers.

‍

Hola los Marple aficionados!

We at Marple like fast cars. More so, we love the engineers who design them!

That's why we're really excited to announce that Marple is working together with the beautiful students of the Formula Student Team Delft (FSTD).

FSTD is designing an electric race car from scratch. Yes, that's as cool as it sounds!

‍

Hi there!

We have some exciting news again, this time about our product. We are moving from a desktop solution to a server based solution and make Marple a web tool.

A server based approach has many benefits:

‍

Beloved Marple-enthusiasts,

We're back with another blog post, this time on your request!

Two weeks ago we asked you what kind of data you wanted us to visualise with Marple.

Cycling came out as a clear winner. Convenient! We had already planned to kick-off the Marple summer in Spain for a work-from-where-the-sun-shines week. Now we had an excuse to also bring our bikes.

‍

If you remember what we said a couple a couple of blogs ago we are supporting the Formula Student Team Delft (FSTD) with a sponsorship (aren't we generous)! For those who don't know, Formula Student is a worldwide competition for students to build and race their own electric race car.