Using Materialize and Redpanda to Analyze Raspberry Pi Temperature Data
Bobby Iliev ・ Jan 11th, 2022 ・ Views

Using Materialize and Redpanda to Analyze Raspberry Pi Temperature Data


This is a self-contained demo using Materialize to process data IoT devices data directly from a PostgreSQL server.

The demo builds up on the How to build AdonisJS API to store your Raspberry Pi Temperature tutorial.

The data is generated by a Raspberry Pi temperature mock service simulating 50 devices reporting to an AdonisJS API mock service.

Finally, we will create a sink to let us stream the data out of Materialize to a Redpanda topic.

mz-raspberry-pi-temperature diagram


Before you get started, you need to make sure that you have Docker and Docker Compose installed.

You can follow the steps here on how to install Docker:


In this demo, we’ll look at monitoring the temperature of a set of Raspberry Pi devices and extracting some insights from them, and streaming the data out to an external source.

Raspberry Pi Mock

The main source of data is a Raspberry Pi Mock service, that simulates 50 devices reporting their CPU temperature to a mock API service built with AdonisJS.

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The mock service generates about ~25 new requests to the mock API service every second.

For more information on how the mock services works along with the AdonisJS API, you can follow the How to build AdonisJS API to store your Raspberry Pi Temperature tutorial.

API Mock service and PostgreSQL

The API mock service receives the data from the 50 simulated Raspberry Pi and stores the data from each request in a PostgreSQL instance.

The data that is being received with each request is:

  • The name of the Raspberry Pi device.
  • The timestamp when the temperature was measured.
  • The temperature of the device, in celsius.

The Mock API will save all data in a table called sensors. The columns of the sensors table are:

  • name
  • timestamp
  • temperature


Materialize presents an interface to ingest the temperature data from the PostgreSQL database.

In this demo, we are going to use Materialize to:

  • Create a PostgreSQL source
  • Materialize the PostgreSQL data, which will be retained all in memory.
  • Provide a SQL interface to query the temperature data. We will connect to Materialize through mzcli, which is our forked version of pgcli.
  • Explore the Materialize data via Metabase.

Running the demo

Clone the repository:

git clone

Access the directory:

cd mz-raspberry-pi-temperature

Build the Raspberry Pi Mock images:

docker-compose build

Start all of the services:

docker-compose up -d

Access Materialize

docker-compose run mzcli

Create Materialize Source:

To create a PostgreSQL Materialize Source execute the following statement:

CONNECTION 'user=postgres port=5432 host=postgres dbname=postgres password=postgres'
PUBLICATION 'mz_source';

A quick rundown of the above statement:

  • MATERIALIZED: Materializes the PostgreSQL source’s data. All of the data is retained in memory and makes sources directly selectable.
  • mz_source: The name for the PostgreSQL source.
  • CONNECTION: The PostgreSQL connection parameters.
  • PUBLICATION: The PostgreSQL publication, containing the tables to be streamed to Materialize.

Create a view:

Once we've created the PostgreSQL source, in order to be able to query the PostgreSQL tables, we would need to create views that represent the upstream publication’s original tables. In our case, we only have one table called sensors so the statement that we would need to execute is:

CREATE VIEWS FROM SOURCE mz_source (sensors);

To see the available views execute the following statement:


Once that is done, you can query the new view directly:

SELECT * FROM sensors;

Next, let's go ahead and create a few more views.

Creating more materialized views

If you wish you can enable timing so we could actually see how long it takes for each statement to be executed:

  • Example 1: Create a materialized view to show the total number of sensors data:

Querying the mz_count view:

SELECT * FROM mz_count;


(1 row)

Time: 2.299 ms
  • Example 2: Create a view to show the average temperature of all sensors:
CREATE MATERIALIZED VIEW mz_total_avg AS SELECT avg(temperature::float) FROM sensors;

Query the mz_total_avg:

SELECT * FROM mz_total_avg;


(1 row)

Time: 2.984 ms
  • Example 3: Create a view to show the average temperature of each separate sensor:
    SELECT name::text, avg(temperature::float) AS temp 
    FROM sensors
    GROUP BY (name);

Let's again query the average view: `

SELECT * FROM average LIMIT 10;


     name     |        temp
 raspberry-1  |  58.60756530123859
 raspberry-2  |  58.95694631912029
 raspberry-3  | 58.628198038515066
 raspberry-4  |  59.40673999174753
 raspberry-5  | 59.079367226960734
 raspberry-6  |  58.96244838239402
 raspberry-7  |   58.4658871719401
 raspberry-8  |   58.9830811196705
 raspberry-9  | 59.398486896836936
 raspberry-10 | 59.669463513068024
(10 rows)

Time: 2.353 ms

Feel free to experiment by creating more materialized views.

Creating a Sink

Sinks let you send data from Materialize to an external source.

For this demo, we will be using Redpanda.

Redpanda is a Kafka API-compatible and Materialize can process data from it just as it would process data from a Kafka source.

Let's create a materialized view, that will hold all of the devices with an average temperature of more than 60 celsius:

    SELECT * FROM average WHERE temp > 60;

If you were to do a SELECT on this new materialized view, it would return only the devices with an average temperature of above 60 celsius:

SELECT * FROM mz_high_temperature;

Let's create a Sink where we will send the data of the above materialized view:

CREATE SINK high_temperature_sink
    FROM mz_high_temperature
    INTO KAFKA BROKER 'redpanda:9092' TOPIC 'high-temperature-sink'
    CONFLUENT SCHEMA REGISTRY 'http://redpanda:8081';

Now if you were to connect to the Redpanda container and use the rpk topic consume command, you will be able to read the records from the topic.

However, as of the time being, we won’t be able to preview the results with rpk because it’s AVRO formatted. Redpanda would most likely implement this in the future, but for the moment, we can actually stream the topic back into Materialize to confirm the format.

First, get the name of the topic that has been automatically generated:

SELECT topic FROM mz_kafka_sinks;



For more information on how the topic names are generated check out the documentation here.

Then create a new Materialized Source from this Redpanda topic:

FROM KAFKA BROKER 'redpanda:9092' TOPIC 'high-temperature-sink-u12-1637586945-13670686352905873426'

Make sure to change the topic name accordingly!

Finally, query this new materialized view:

SELECT * FROM high_temp_test LIMIT 2;

Now that you have the data in the topic, you can have other services connect to it and consume it and then trigger emails or alerts for example.


In order to access the Metabase instance visit http://localhost:3030 if you are running the demo locally or http://your_server_ip:3030 if you are running the demo on a server. Then follow the steps to complete the Metabase setup.

To connect to your Materialize database, specify the following connection properties:

Field             | Value
----------------- | ----------------
Database          | PostgreSQL
Name              | user_reviews
Host              | materialized
Port              | 6875
Database name     | materialize
Database username | materialize
Database password | Leave empty

Once ready you will be able to visualize your data just as you would with a standard PostgreSQL database.



This is a simple example of how to use the direct PostgreSQL connection with Materialize and stream data into a Kafka/Redpanda topic.

In most cases, you would not store your IoT devices data in a PostgreSQL database, but in an S3 bucket or a Kafka topic. So the setup could be similar to the following:

  • S3 Bucket example: IoT devices data with Materialize and S3

  • Redpanda/Kafka example: IoT devices data with Materialize and Redpanda

Stopping the Demo

To stop all of the services run the following command:

docker-compose down

Helpful resources:

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