Awesome Big Data
Section: Scheduling · a data orchestrator for machine learning, analytics, and ETL.
Entry
Appears in 12 awesome lists
Cloud-native orchestration platform for developing and maintaining data assets including ML models. Declarative programming model with integrated lineage and observability. Apache 2.0 licensed.
Section: Scheduling · a data orchestrator for machine learning, analytics, and ETL.
Section: Tools · A data orchestrator for machine learning, analytics, and ETL.
Section: Workflow · An open-source Python library for building data applications.
Section: Workflow Engine · Data orchestrator with a declarative, asset-based programming model for building and observing data pipelines.
Section: Data Processing · A data orchestrator for machine learning, analytics, and ETL.
Section: 8. MLOps / LLMOps & Production · Cloud-native orchestration platform for developing and maintaining data assets including ML models. Declarative programming model with integrated lineage and observability. Apache 2.0 licensed.
Section: General · ] - A data orchestrator for machine learning, analytics, and ETL.
Section: Pipeline frameworks & libraries · Python-based API for defining DAGs that interfaces with popular workflow managers for building data applications.
Section: Data Pipeline · A data orchestrator for machine learning, analytics, and ETL.
Section: Job Schedulers · An orchestration platform for the development, production, and observation of data assets.
Section: Dagster (32 · 16K) - An orchestration platform for the development, production, and.. Apache-2 · (👨💻 700 · 🔀 2.3K · 📦 4.9K):
Section: Data Science and Analytics · An orchestration platform for the development, production, and observation of data assets.
Python module for building complex pipelines of batch jobs. Handles dependency resolution, workflow management, visualization, and Hadoop integration. Built at Spotify and battle-tested in production. Apache 2.0 licensed.
"Use airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it…
Workflow management system that makes it easy to take your data pipelines and add semantics like retries, logging, dynamic mapping, caching, failure notifications, and more.
| Python | - A scalable general purpose micro-framework for defining dataflows. You can use it to build dataframes, numpy matrices, python objects, ML models, etc. Embed Hamilton anywhere python runs, e.g. spark, airflow, jupyter, fastapi, python scripts, etc.
Unified analytics engine for large-scale data processing. In-memory cluster computing with high-level APIs in Python, Scala, Java, and R. Powers MLlib for distributed machine learning and Structured Streaming for real-time data. Apache 2.0 licensed.
Event-driven orchestration and scheduling platform for mission-critical workflows. Infrastructure-as-Code approach with declarative YAML, Git version control integration, and hundreds of plugins for data pipelines and ML workflows. Apache 2.0 licensed.
Stream processing framework with powerful batch and streaming capabilities. High-throughput, low-latency runtime with exactly-once processing guarantees. Ideal for real-time AI inference pipelines and event-driven ML applications. Apache 2.0 licensed.