DataOps topic
DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. While DataOps began as a set of best practices, it has now matured to become a new and independent approach to data analytics. DataOps applies to the entire data lifecycle from data preparation to reporting, and recognizes the interconnected nature of the data analytics team and information technology operations.
raccoon
Raccoon is a high-throughput, low-latency service to collect events in real-time from your web, mobile apps, and services using multiple network protocols.
versatile-data-kit
One framework to develop, deploy and operate data workflows with Python and SQL.
squirrel-core
A Python library that enables ML teams to share, load, and transform data in a collaborative, flexible, and efficient way :chestnut:
dagger
Dagger is an easy-to-use, configuration over code, cloud-native framework built on top of Apache Flink for stateful processing of real-time streaming data.
awesome-data-catalogs
📙 Awesome Data Catalogs and Observability Platforms.
whylogs
An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collecti...
meteor
Meteor is an easy-to-use, plugin-driven metadata collection framework to extract data from different sources and sink to any data catalog.
firehose
Firehose is an extensible, no-code, and cloud-native service to load real-time streaming data from Kafka to data stores, data lakes, and analytical storage systems.
console
Redpanda Console is a developer-friendly UI for managing your Kafka/Redpanda workloads. Console gives you a simple, interactive approach for gaining visibility into your topics, masking data, managing...
fast-data-dev
Kafka Docker for development. Kafka, Zookeeper, Schema Registry, Kafka-Connect, Landoop Tools, 20+ connectors