Automating ETL jobs on time series data with QuestDB on Google Cloud Platform

QuestDB is an open-source time-series database known for its ultra-low latency and high ingestion throughput, making it suitable for demanding workloads such as those on trading floors and mission control. The tutorial, contributed by Gábor Boros, demonstrates how to use cloud functions with QuestDB to create a custom ETL (Extract, Transform, Load) job for processing time-series data. The process involves setting up a Google Cloud Platform (GCP) environment, including creating a Compute Engine instance for QuestDB, configuring a storage bucket for data storage, and employing Google Cloud Functions to anonymize data by removing personally identifiable information (PII) before loading it into QuestDB. The tutorial guides users through creating and deploying these components, using Python for cloud functions to process data, and concludes with testing the setup by generating random data and verifying its storage and transformation in QuestDB. This approach exemplifies how to leverage cloud services and QuestDB for efficient data processing and analysis, ensuring data privacy and enabling scalable analytics capabilities.