April 2022 Summaries

QuestDB is an open-source time-series database designed for high-performance workloads, offering ultra-low latency, high ingestion throughput, and a flexible storage engine with native support for Parquet and SQL. It integrates with popular tools via the PostgreSQL wire protocol, making it suitable for real-time analytics, monitoring, and telemetry. Cube, a headless business intelligence platform, organizes data into a structured model, enabling consistent data metrics across applications through APIs like REST, GraphQL, and SQL. When used together, QuestDB and Cube create a robust data pipeline that efficiently handles time-series data, as demonstrated with a crypto price analysis platform. This collaboration allows for fast, consistent, and reliable data processing, supporting use cases across embedded analytics, business intelligence, and machine learning. The synergy between QuestDB's rapid data handling and Cube's modeling capabilities offers a powerful solution for managing real-time data in various applications.

QuestDB is an advanced open-source database designed for high-performance market data analysis, particularly suitable for handling tick data with premium ingestion throughput and efficient SQL analytics. The platform showcases its capabilities through a live demo featuring historical NYC taxi rides and a geospatial dataset of 250,000 ships, along with a new real-time dataset of cryptocurrency market data from the Coinbase Exchange. Utilizing the Cryptofeed Python library for data ingestion and Grafana for visualization, QuestDB processes columns such as price, side, amount, and timestamp for BTC-USD and ETH-USD trades, offering rapid query responses in milliseconds despite the large volume of data. The demo includes example SQL queries that utilize QuestDB's time-series extensions, such as LATEST ON and SAMPLE BY, to extract insights like the latest cryptocurrency prices, candle charts, volume-weighted average prices, and implied BTC-ETH exchange rates. The platform encourages users to explore these queries and join the community for further development and collaboration.