Extending the Attain of Information Logging – Information


SparkFun CTO Kirk Benell supplies a high-level introduction to the SparkFun DataLogger IoT, outlining the foremost performance the product supplies. Study right here about its interactive menu, next-level software program, and what it’s good to know to get began.





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SparkFun has lengthy provided embedded merchandise that target logging info from a linked system or sensor. These ready-to-use merchandise enabled fast knowledge logging of a linked machine to a neighborhood storage (SD card) or to an connected serial machine, whereas delivering a low-power, versatile knowledge logging resolution that required minimal configuration and no firmware improvement effort. Later variations additionally added the potential for computerized machine recognition, delivering a real, plug-and-play knowledge logging resolution.

Our present knowledge logging options are unbelievable, however the knowledge logging market has developed past native knowledge logging, with entry to linked knowledge providers now anticipated. To fulfill this want, SparkFun launched the SparkFun DataLogger IoT product line, with our first providing being the DataLogger IoT – 9DOF product.

SparkFun DataLogger IoT

The SparkFun DataLogger IoT delivers a ready-to-use linked machine centered on knowledge logging of knowledge obtained from the robotically detected SparkFun qwiic sensors linked to the board. Simply configured through an on-board configuration menu system, The DataLogger IoT helps knowledge logging to a wide range of linked knowledge providers in addition to the native SD card and output to a linked serial machine.

The DataLogger IoT system requires no improvement effort to function or use – join supported sensors, energy on the machine, and configure through a serial console primarily based interactive menu or by offering a settings JSON file. As soon as configured, settings a persistent throughout machine restarts, enabling dynamic deployment and use throughout all kinds of environments and conditions.

Key options of the SparkFun DataLogger IoT embrace:

  • It’s a prepared to make use of machine – no improvement required.
  • Easy, interactive machine settings through a serial menu interface.
  • Automated machine detection, with intensive SparkFun qwiic board help – 50+ * merchandise obtainable at product launch.
  • Native knowledge logging to the on-board SD card, or to the serial console.
  • Wi-Fi community connectivity built-in, with help for a wide range of linked knowledge logging providers.
  • A built-in firmware replace functionality, enabling over-the-air firmware updates immediately from the machine.

Able to Use

The SparkFun DataLogger IoT is delivered ready-to-use, requiring no firmware improvement or {hardware} engineering. Delivering a real plug-and-play expertise, the DataLogger IoT the setup and deployment of a linked knowledge logging system takes minutes.

The configuration of a linked knowledge logging machine utilizing the SparkFun DataLogger IoT is simple, normally taking the next steps to deploy:

  • Connect the specified SparkFun qwiic sensors to the DataLogger IoT board.
  • If utilizing an information service that requires safety keys, add these keys to an SD card and insert the cardboard into the DataLogger IoT board.
  • Join the DataLogger IoT board to a pc through a USB-C cable and join an interactive serial console software (Tera Time period or my favourite, minicom from a terminal) to the DataLogger System.
  • Within the serial console, as soon as the startup sequence is full, press any key to deliver up the DataLogger IoT menu system.
  • From the menu system, configure the machine as desired. This could embrace particular sensor settings, Wi-Fi credentials and linked knowledge service configuration.

When accomplished, the settings are saved, and the machine is able to use.

Interactive Menu System

The DataLogger IoT is configured through an interactive menu system accessed through the serial console. As soon as linked to the machine and the machine is began, the menu system is introduced with a single key press, presenting a hierarchical menu that begins with choices for system settings and machine settings.

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To navigate the menu system, entries are chosen by urgent the corresponding variety of the specified entry. Every menu has a “again” possibility, usually the “b” key or “x” to exit the menu from the top-level menu entry. The Escape key will even abort the present menu web page.

The settings web page has all kinds of choices for the DataLogger IoT, together with basic machine operation, community connectivity, knowledge service configuration and system replace choices.

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Every entry presents choices for that specific class or setting.

Choosing the System Settings possibility from the principle menu, presents a listing of the units presently linked to the DataLogger IoT board. Choosing a specific machine presents the particular settings to the machine.

For instance, chosen the units for the DataLogger IoT – 9DOF board current the next menu, which lists the on-board units for the board:

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When a setting is modified, it’s utilized to the working system and saved when exiting the menu system so the updates are utilized if the system is restarted.

It’s value noting the DataLogger IoT additionally helps a json primarily based settings import mechanism, enabling fast configuration of latest or reset DataLogger IoT by putting the file on an SD card that’s inserted into the board.

Automated System Detection

One of many key targets of the DataLogger IoT is fast configuration and use. And a key factor in assembly this objective is the automated detection and configuration of units linked to the DataLogger IoT.

On launch the DataLogger IoT is ready to robotically detect and configure over 50 SparkFun qwiic boards. The DataLogger IoT firmware contains the drivers for every of the boards, in addition to logic to detect and configure every machine. On startup, the DataLogger IoT scans the I2C bus for identified qwiic units and if a tool is detected, masses the suitable driver, and configures the machine robotically. That is an extremely highly effective and handy characteristic that helps make the DataLogger IoT a precious device.

An instance of this in motion is proven within the beneath menu entries. By simply including a SparkFun Setting Combo board to the DataLogger IoT, the System Settings menu robotically contains the board sensors – the CCS811 and the BME280.

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The record of supported units for the DataLogger IoT is offered in an on-line supported machine record. As SparkFun releases extra qwiic units sooner or later, the units that make sense can be added to the DataLogger IoT firmware.

Native Information Logging

Whereas the DataLogger IoT is targeted on linked knowledge logging capabilities, it delivers core, native knowledge logging capabilities. Specifically logging knowledge to the on-board SD card in addition to to an connected serial console machine.

The system helps two codecs for the output knowledge: CSV and JSON. Enabling knowledge output and the specified format for every output vacation spot is configured throughout the menu system of the DataLogger IoT.

For writing knowledge to the SD card, the DataLogger IoT makes use of quick SD entry (MMC), minimizing knowledge delays when writing to the playing cards file system. Moreover, log recordsdata are rotated primarily based on a consumer set time interval, serving to to simplify knowledge administration and group.

Community Connectivity and Information Providers

The important thing characteristic of the DataLogger IoT is its community connectivity and the entry to community providers this permits. With community help included, the DataLogger IoT implements a wide range of network-based providers that tremendously improve the pliability and talent of the information logging system. This contains Community Time Protocol (NTP) help, for correct time labeling of recorded knowledge, and a wide range of knowledge logging community providers.

On the preliminary launch of the DataLogger IoT board, the next knowledge logging providers are supported:

  • Generic MQTT help
  • Generic HTTP submit help
  • AWS IoT
  • Azure IoT
  • ThingSpeak
  • MachineChat

When an information service is enabled, every time knowledge is logged by the DataLogger IoT, the information is distributed to the information service.

A fantastic instance of logging output from the DataLogger is through the ThingSpeak service, which presents the output graphically. The next is the output of a BME280 qwiic machine posted to ThingSpeak from a DataLogger IoT board. The straightforward plots present knowledge carried out throughout pre-release testing and the output generated as we speak, whereas scripting this weblog submit.

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System Updates

SparkFun is dedicated to including options and offering updates to the DataLogger IoT at a repeatedly scheduled tempo. This enables for well timed bug fixes, in addition to including new units and community providers as they develop into obtainable.

To help straightforward updates, with the DataLogger IoT having community entry, firmware updates “over-the-air” (OTA) are supported. Accessed through the menu system, the firmware checks for the most recent firmware obtainable, and if a more moderen model is out there, supplies the power to obtain and apply the brand new firmware immediately from the machine.

If the community connectivity of a DataLogger IoT will not be enabled, updates are additionally obtainable offline utilizing the on-board SD card as a supply for the replace operation.

A New Possibility for Related Information Logging

Offering a ready-to-use system for linked knowledge logging, the SparkFun DataLogger IoT delivers a brand new and distinctive resolution to the SparkFun catalog. Implementing plug-and-play machine detection for over 50 SparkFun qwiic merchandise and supporting a variety of community knowledge providers, the DataLogger IoT supplies fashionable capabilities with out requiring engineering work or firmware customization. Simply add the specified sensor, configure the system, and start logging.


Additional Sources

Try some extra info on the DataLogger and what it is able to in these weblog posts!


Are you engaged on one thing with the DataLogger? We wish to see! Shoot us a tweet @sparkfun, or present us on Instagram, Fb or LinkedIn.



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