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.
Ease of use is an essential factor when choosing a time series data visualization tool. Marple has been designed specifically for engineers, making it easy to use and navigate. Its interface is straightforward, and its features are intuitive, making it an excellent option for those who want to visualize their data without having to spend time learning how to use a complicated tool.
Google Sheets is another tool that is easy to use. Due to being a spreadsheet, it has a familiar interface for most people. Plotly Chart Studio provides a similar experience, combining a spreadsheet with a UI to control the underlying plotly.py library.
Jupyter Notebooks is a coding-based tool, which requires some knowledge of coding. However, once you have a good understanding of coding, it is an efficient and flexible tool for creating visualizations.
Grafana and Metabase are more advanced tools and require significant time and effort to learn. They are powerful tools, but their learning curve can be steep. They require a database to be set up to visualise your data.
Performance becomes important when working with large datasets. Marple has been optimized for handling large datasets and can handle millions of data points with ease (explained in this blog post how it’s done).
Google Sheets can handle datasets of a couple of megabytes, but it can become slow and cumbersome when working with large datasets. Jupyter Notebooks can handle large datasets as long as your server has enough memory to process the data.
Grafana and Metabase are more suited for real-time data visualization and are optimized for handling data streams, but may not be as efficient when working with large datasets.
Plotly Chart Studio has some limitations when it comes to handling large amounts of data. It is more suitable for small datasets, and larger datasets may require additional processing power or a different tool.
Google Sheets provides different chart options, with customization. It is possible to create a wide range of visualizations with Google Sheets, but it can take more time to customize them to your exact needs.
Marple also offers a range of visualization options, such as time series plots, scatter, map and frequency plots. The graphs can be customized, picking different colors, line styles, and boundaries for the y-axis.
Jupyter Notebooks, Grafana, Metabase, and Plotly Chart Studio offer advanced visualization options and customization. These tools are more suited for those who want to create complex visualizations and have the skills to do so.
Integration is another essential factor to consider when choosing a tool for time series data visualization. Marple integrates seamlessly with other tools commonly used by engineers, such as MATLAB, Python, and InfluxDB. This makes it easy to import data and use it in conjunction with other tools.
Google Sheets, Jupyter Notebooks, and Plotly Chart Studio also have various integration options, making it possible to import and export data to other tools.
Grafana and Metabase integrate best with databases, making them less flexible in terms of working with files (.csv, .mat, .tdms, .mdf, .hdf5, …)
Data management is important when you want to save your work for later.
Google Sheets is part of Google Drive, allowing you to upload and organise your files that way. Jupyter Notebooks provides a similar experience, where you can put your files in folders.
Marple takes this a step further, having the ability to add metadata to files. Metadata can be a project the file belongs to, some information about how the data was recorded, or anything else that you want to add.
Grafana and Metabase put the burden of organizing the data on the database that you are using behind it. The data management capabilities depend on what database you use and how you implemented it in your workflow.
Plotly Chart Studio show your data in a spreadsheet, but has no capabilities to organise it further.
Collaboration is about sharing your work with others. Google Sheets allows for easy collaboration with others, as multiple people can edit a document simultaneously. This can be useful when working on a project with a team, as everyone can work on the same document without worrying about version control.
Jupyter Notebooks also supports collaboration through platforms such as GitHub. Grafana and Metabase have some collaboration features, but they are more limited than Google Sheets and Jupyter Notebooks. Plotly Chart Studio also allows you to share your work, if you sign up.
Marple supports collaboration through a lot of features:
There are lots of options available for free to make online graphs from time series data. Depending on your use case, one might be better suitable than the other.
If your data can easily be put in a database, Grafana and Metabase are flexible options that can be highly customized. They require some knowledge of SQL to get the most out of your data.
Jupyter Notebooks provides a Python-based approach. In contrast to scripting on your local machine, you can share your work with others. It is the most flexible solution, but also the most effort.
Google sheets is the perfect solution for small datasets. Spreadsheets are familiar to most users, and graphs can be customized to your liking. Plotly Chart Studio provides a similar experience, but lacking a way to organize the data.
Marple is the clear winner if you are looking for performance and ease of use. If you use one of the supported file formats, it should work out of the box.