One of the nice things about working at a young start up is that you get to wear multiple hats. For instance, at Hi Fidelity Genetics / Technologies, my title was data scientist, but there were times where I got to be a data engineer as well, which was a lot of fun! Here I will describe our data stack and offer a few lessons learned along the way.


Hi Fidelity Genetics developed a root phenotyping device called RootTracker. The device has electrodes arrayed around a root crown in a cylindrical fashion. As seen in the image below, there were 12 vertical, printed circuit board (PCB) “paddles”, each of which had 22 electrodes. The device measured voltage at these electrodes for three different charging parameters at regular intervals. These voltages were ultimately transformed into root detections — that is the device detected a root at a certain time in a certain place. (You can read more about the set up in our paper “Capturing in-field root system dynamics with RootTracker”.)

The devices used a radio to send data to a base station. This data was aggregated across several devices, which was then uploaded as a single file to Amazon Web Services (AWS). There are probably several ways we could have improved our data management at the point of the base station, but we will take the single file uploads from the base station as our starting point for describing our data stack. The whole thing evolved in a rather organic way and what I describe below mostly describes where we ended, as opposed to where we started. I will also make some modifications to avoid delving into historical or technical details.


Instead of giving a technical description of the schema, let me describe the objects that were modeled, since that provides a better overview. As mentioned previously, we had Devices that measured voltages. Each paddle on a device sent a Measurement that reported the device and paddle that the measurements came from, the charging parameters, and 22 voltages, one for each sensor on a paddle. A Trial refers a specific experiment. Each Trial was associated with a set of device Deployments, effectively linking a Device with a Trial and recording some additional information in the process, like the location of the Device. Each Device possessed firmware, which controlled how the device operated along with statically set network information. Because that firmware could change, there were separate Software records that tracked the firmware version and the configuration of the Device.

In agricultural experiments, it is standard to have a “field book” that tracks the Treatments used across the field. A Treatment was always a categorical (potentially ordered) variable.

An algorithm processed the raw data to produce detections. (The story of the detection algorithm is quite interesting in and of itself.) The first version of our Detections recorded the time, paddle, and electrode where it occurred. Later, we realized that we also needed to include quality control metrics to identify periods when the detection algorithm might be unreliable. That led to the a more generic notion of Features, which were a time-indexed multidimensional array derived from the raw data.


Like our discussion of the schema above, we describe how to interact with objects from the schema — the API — at a high level.

In some cases, the API was very transparent. For instance, one could get Trials, Devices, Deployments using a variety of filters. Inserting those objects was (mostly) just a matter of populating their fields.

In other cases, the API was doing a lot under the hood. For instance, our field scientist designed the experiments and kept track of additional experiment data using a traditional field book, which we can think of as a spreadsheet. Each row of the field book was one experimental unit, which were plants when conducting a RootTracker trial. The columns of the field book were effectively covariates — treatments or observations, like the variety, growth stage, location, or device barcode of a plant. One could send a row from a field book to the API to update or insert the treatments. However, the treatments were recorded in a key-value fashion, so in actuality the API would be updating or inserting several records in one go. Conversely, when exporting a field book, one would have to gather many records to recapitulate a single row of the field book.

One could get Measurements transparently by the device barcode, time, and charging parameters, but we had a bespoke procedure for inserting Measurements since they came from a tarballed file of aggregated measurements. In that case it made more sense to process a whole file together, instead of each measurement within a file individually.


Based on our teams’ experience, we used SQL for our database (PostgreSQL and SQLAlchemy, specifically). Eventually, we also developed an alternative approach for storing the Measurements and Features — using Apache Iceberg / Tabular.

We employed the OpenAPI framework to specify our API. We found the free Swagger editor to be sufficient for our purposes. We used Flask to implement the API. While our system was only used internally, we did ultimately develop the capability to control access to the API using Keycloak.

An aside… obviously, cost is an important factor when deciding how to put together a system. On AWS, it is more expensive to run an SQL database than it is to retrieve data from S3 storage. Why is that and what is the difference?

When you run an Instance on AWS, it is like turning on a computer. You have a CPU, RAM, and a hard drive. The hard drive is the key for our current discussion. A hard drive has a file system. You can combine several hard drives using RAID to create a larger file system with insurance against failure; however, there is a limit to how big you can make the file system. That becomes problematic if you are doing something like crawling the web, as the data collected is so copious.

To overcome that problem, a new approach was developed with Apache Hadoop being the canonical software. Hadoop uses commodity hardware to scale a file system to arbitrary size. To do that the Hadoop file system gives up some of the POSIX requirements that are found in a typical file system. Hadoop also allows one to compute statistics and similar computations for a dataset on this file system, which is nontrivial when you consider that it is a highly decentralized system.

In terms of how all this relates to options on AWS, the hard drive one uses for an AWS Instance or for a persistent SQL database uses the old school hard drive, whereas AWS S3 is effectively Hadoop (or a successor of Hadoop).

Naturally, one still wants to organize data stored on something like S3. The original solution to that was Apache Hive. Data is stored as flat files, which possess some sort of schema. Those files can be indexed on a value that is constant within the file or on metadata about fields of the file. Data can be retrieved using an SQL-like language. Apache Iceberg is a successor to Hive that aims to solve some of its shortcomings. The bottom line is that one can store, retrieve, and compute statistics for large amounts of structured data using a more cost effective form of storage, which can also obviate any worries about how large that data may grow.


Looking back on this, the idea of design keeps wandering through my mind.

If we think about architecture, like architecture of buildings, an architect needs to know about the limitations of the materials used in the design, but does not need to know how to do carpentry, masonry, etc. The architect provides the blueprint and then the subcontractors do the work. In some sense though, the building is done when the blueprint is done.

Within the context of a system, you could make a similar split. The blueprint is the database schema and API documentation. The subcontractors are the people and software used to implement the API and data storage. I am sure there are cases where the requirements of the system are so clear that one can write the “blueprint” down ahead of time. But in our case, when one is creating something totally new and trying to do it quickly, it becomes necessary to draw the blueprint and build the house simultaneously. It is not surprising that when doing that there is a cost, which is refactoring the codebase or even rewriting portions of it.