This pipeline processes real-time rocket launch telemetry data using Kafka, PySpark, InfluxDB, and Grafana. The pipeline ingests raw sensor data from a local file, processes the data using PySpark, and stores the results in InfluxDB. The processed data is then visualized using Grafana.

• Install Docker and Docker Compose on your system
• Log in to Docker so that Docker can pull images from Docker Hub
• From the root of the project directory, build the Docker images with docker-compose build
• Run the Docker containers using docker-compose up
• Connect to Grafana by visiting http:https://localhost:3000 in a web browser.
• Use the Kafka producer script to send sensor data to Kafka with python3 kafka_producer.py

The pipeline will start ingesting data from the Kafka topic and processing it using PySpark. The processed data will be stored in InfluxDB and visualized in Grafana.

Upon running the project, Docker Compose uses docker-compose.yml to start the individual services

Dockerfile uses pip to install the packages (Kafka, PySpark, InfluxDB, Grafana) for the entire pipeline within the container so that the user doesn't have to manually or globally install those on their local machine. Then it copies over the scripts and runs them.

Producer.py ingests data from a local JSON file with dummy data and publishes it to a Kafka topic. In a production setting, it would receive and publish live sensor data in place of the dummy data.

• Kafka is a distributed event streaming platform that lets you read, write, store, and process events (aka records or messages) across many machines. I use Kafka here as a producer (i.e. a client application to write data to Kafa) to store the streaming data while it's waiting to be processed by structured-streaming for further transformation/aggregation.

structured-streaming.py then reads from that Kafka topic, transforms and aggregates rolling averages for every minute, and writes each the data to persist it to InfluxDB.

• This script is a consumer (i.e. a client application reading data from Kafka)

I use InfluxDB as a persistent store, mainly because it is uniquely geared towards handling the high-cardinality nature of time-series sensor data. For example, sensor_name might have many different values, like altitude_sensor_1, altitude_sensor_2, acceleration_sensor_1, acceleration_sensor_2, temperature_sensor_1, temperature_sensor_2, etc. The amount of these types of groupings can quickly become very large in a hardware system, with many sensor types and channels with different data types, units, and sampling rates. InfluxDB creates an index for each unique tag value, so it lets you quickly locate across many different sensor types and tags. Additionally, InfluxDB partitions by time range ...

• Breadth vs length (across time ranges and channels)
• I use InfluxDB as a store because of its relability compared to some alternatives these days that purport to serve as data stores (e.g. Redis and Memcache)

Kafka (and seemingly lots of other technologies like Redis or other caches) seem to be