Explore 11 tools and 3 datasets tagged with Real-time for Python data engineering.
Real-time data tools process and deliver data with minimal latency, typically in milliseconds to seconds. In Python data engineering, real-time systems are built with stream processing frameworks like Apache Kafka, Flink, and Spark Streaming to power live dashboards, alerting systems, fraud detection, and event-driven architectures.
Real-Time Analytics Database
A column-oriented, distributed data store designed for sub-second OLAP queries on event data. Druid is used for powering interactive analytical applications, real-time dashboards, and exploratory analytics on high-cardinality data.
Real-Time Streaming Data Platform
A framework for building real-time streaming data processing applications. SwimOS combines streaming data processing with a built-in state store and UI capabilities, enabling continuous intelligence applications that process and visualize data in real-time.
Real-Time Data Pipeline Platform
A no/low-code data pipeline platform that handles both batch and real-time data ingestion. Estuary Flow uses change data capture to stream data from databases and provides a visual interface for building and monitoring data pipelines.
Managed Real-Time Streaming
A fully managed, cloud-based service from AWS for real-time data streaming and processing. Kinesis enables collecting, processing, and analyzing streaming data at any scale, with integrations across the AWS ecosystem.
Open-Source Change Data Capture Platform
An open-source CDC platform that monitors databases and streams every committed row-level change as a structured event. Debezium reads directly from database replication logs — capturing inserts, updates, and deletes in real time with no polling and no impact on query performance.
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