Best of Real-Time Analytics — 2023
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TigerData (Creators of TimescaleDB)·3y
Real-time Analytics in Postgres: Why It's Hard (and How to Solve It)
Enabling real-time data analytics is a key capability of modern applications, but PostgreSQL materialized views have limitations, such as inefficient refreshes, lack of automatic refreshes, and not showing up-to-date results. Timescale's continuous aggregates solve these limitations by providing automatic and efficient refreshes, as well as combining materialized data with raw data. Continuous aggregates are suitable for real-time analytics, live dashboards, reporting, and more.
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Jakarta EE·2y
Kafka Streams Tutorial
Kafka Streams is a powerful and lightweight library for building real-time streaming applications and microservices. It enables developers to consume, process, and produce data streams from Kafka topics. Kafka Streams is ideal for real-time data processing scenarios such as real-time analytics, event-driven architectures, and fraud detection.
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Tinybird·3y
Event sourcing with Kafka: A practical example
The article discusses event sourcing with Kafka and how Tinybird simplifies the implementation. Event sourcing is a robust way to determine the current state of a system by analyzing past events. Kafka provides a distributed and scalable event log, making it ideal for event sourcing. Tinybird, a real-time data platform, offers snapshotting and real-time analytics capabilities that enhance event sourcing. Best practices for implementing event sourcing with Kafka are provided, along with a practical example of implementing event sourcing with Kafka and Tinybird.