March 2021 Summaries

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.

QuestDB is an open-source time-series database designed for high-performance demands, featuring ultra-low latency, high ingestion throughput, and a versatile storage engine. It supports Parquet and SQL, ensuring data portability and AI readiness without vendor lock-in. The guide, contributed by Yitaek Hwang, details how to deploy QuestDB using the official Helm chart with Google's GKE Autopilot, incorporating Prometheus for basic monitoring and Velero for automated backups, illustrating a production-ready environment. The tutorial outlines the benefits of GKE Autopilot, such as automated Kubernetes operations and cost-effective pod usage, and provides step-by-step instructions to set up QuestDB on Kubernetes, modify configurations for monitoring, and implement disaster recovery strategies. It highlights QuestDB's development focus on enterprise features and integration capabilities, underscoring its potential for future enhancements in monitoring and DevOps features.

QuestDB is an open-source, next-generation database optimized for market data, offering high ingestion throughput and efficient SQL analytics. This guide, contributed by Kovid Rathee, details setting up data alerting using Grafana and QuestDB, focusing on real-time stock price monitoring with the IexFinance API. By employing tools like Docker, Slack, and Python, users can fetch, stream, and visualize stock data, and configure alerts for changes in stock prices. The tutorial guides users through setting up necessary infrastructure, creating data visualizations, and establishing notification channels, with a focus on integrating Grafana's alerting system to notify Slack channels based on user-defined conditions. The example provided demonstrates tracking Tesla's stock price, with suggestions for script enhancements such as handling multiple stocks and improving error management for when markets are closed.