Awesome Big Data
Section: SQL-like processing · is a Query Optimization Framework for Spark and Shark.
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Appears in 8 awesome lists
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.
Section: SQL-like processing · is a Query Optimization Framework for Spark and Shark.
Section: Tools · A unified engine for large-scale data processing and analytics.
Section: Stream Processing · Unified analytics engine whose Structured Streaming API provides scalable, fault-tolerant stream processing on the Spark SQL engine.
Section: Java · Spark is a fast and general engine for large-scale data processing.
Section: 1. Core Frameworks & Libraries · 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.
Section: Data Stream Processing · Micro-batch processing for streams using the apache spark framework as a backend supporting stateful exactly-once semantics.
Section: Distributed Computing · Apache Spark Python API.
Section: Computation · | Scala | - Apache Spark - A unified analytics engine for large-scale data processing
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…
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.
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.
A fast in-process analytical database that has zero external dependencies, runs on Linux/macOS/Windows, offers a rich SQL dialect, and is free and extensible.
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.