Projects sharing features with Jstorm

Nextflow is a dataflow workflow engine and distributed computing framework used to build and execute data-intensive pipelines. It serves as a scientific workflow language that allows users to define reproducible data processing sequences, supporting any scripting language through shebang declarations. The system functions as a containerized pipeline orchestrator, utilizing container technologies to ensure software dependencies remain consistent across different environments. It decouples workflow logic from the underlying infrastructure, enabling the same pipeline to run on local machines, cl

Storm is a distributed stream processing framework designed to execute unbounded computations across a cluster to process real-time data streams. It functions as a data pipeline orchestrator that allows users to define and deploy declarative data flow graphs connecting streaming sources to processing components. The system operates as a multi-tenant distributed compute engine that isolates workloads and limits resource usage across shared clusters using dedicated pools and access control. It is also a secure distributed processing engine that employs encrypted node communication and SSL-secur

Mage AI is a Python-based data pipeline orchestrator and self-hosted data integrated development environment. It is designed for building, scheduling, and monitoring data workflows using a block-based pipeline design and interactive notebook interface. The platform distinguishes itself by integrating generative AI capabilities, allowing users to connect large language model providers via API to incorporate artificial intelligence into automated data streams. It also functions as an Apache Spark data processor, managing the kernels and infrastructure required for high-volume analytics and larg

DolphinScheduler is a distributed workflow orchestrator designed to manage and automate complex data processing pipelines. It functions as a data pipeline scheduler that coordinates multi-step tasks across distributed environments, ensuring reliable execution through defined dependencies and sequences. The platform utilizes a directed acyclic graph model to represent workflows, allowing users to define task relationships via a visual interface. It employs a master-worker architecture supported by a pluggable task plugin system, which enables the dynamic extension of task types without requiri

RisingWave is a cloud-native streaming database and real-time analytics engine that uses standard SQL to process continuous data streams. It functions as a streaming data lakehouse, combining the capabilities of a streaming SQL database with a platform that integrates streaming ingestion with open table formats. The system is distinguished by its use of the PostgreSQL wire protocol, allowing it to integrate with existing SQL tools and drivers. It employs a decoupled compute and storage architecture, persisting streaming state and materialized views in cloud object storage to enable independen

Druid is a distributed columnar store and online analytical processing database designed for real-time analytics. It functions as a SQL analytics platform and a streaming data ingestion engine, allowing for the analysis of large datasets with low latency to support interactive dashboards and high-concurrency operational workloads. The system integrates a streaming data ingestion engine that loads information via batch or streaming processes to enable immediate analysis of arriving data. It provides high-performance analytical processing to execute slice-and-dice queries on massive data volume

Maestro is a distributed job scheduler and containerized data pipeline tool designed to coordinate complex sequences of tasks. It functions as a Kubernetes workflow orchestrator and MLOps automation platform, utilizing directed acyclic graphs to manage task dependencies and execution order across computing clusters. The system distinguishes itself through the use of isolated container environments for each workflow step, ensuring consistent runtime dependencies. It incorporates an asynchronous event bus to coordinate state transitions and provides lifecycle hook integration that dispatches sy

Azkaban is a distributed workflow manager and DAG-based job orchestrator designed as an enterprise batch processor. It serves as a Java-based workflow engine that schedules and executes complex job sequences across a cluster of executor servers, with specific functionality for managing big data workloads on Hadoop clusters. The system distinguishes itself through a distributed executor model that coordinates state via a shared database to ensure high availability. It employs a plugin-based architecture that allows for custom job types and system functionality extensions, including the ability

Pinot is a distributed, columnar analytical database designed for high-concurrency, low-latency query processing. It functions as a real-time OLAP datastore, enabling interactive, user-facing analytics by ingesting and querying massive datasets from both streaming and batch sources. The system architecture relies on a centralized controller for cluster coordination and a distributed segment-based storage model to ensure horizontal scalability. The platform distinguishes itself through a hybrid ingestion pipeline that unifies real-time event streams and historical batch data into a single quer

Apache Druid is a real-time analytics database and distributed columnar time-series store designed for sub-second analytical queries. It functions as a data platform featuring a distributed SQL query engine and a real-time data ingestion system for moving historical and streaming data from external sources. The system is distinguished by its ability to provide low-latency analytics under high concurrency to power operational dashboards. It implements a Kerberos-secured environment for user authentication and employs a shared-nothing cluster architecture to enable horizontal scaling. The plat

Apache Storm is a distributed stream processing framework and real-time data processing engine. It functions as a fault-tolerant distributed computing system designed to analyze data in motion across a cluster of machines for continuous stream computation. The system enables the creation of fault-tolerant data pipelines and scalable event processing by distributing workloads across a network of computing nodes. This architecture ensures low latency and high throughput for live data while allowing the system to recover automatically from individual node failures. The framework provides capabi

Dask is a parallel computing framework and distributed task scheduler designed to scale Python data science workflows from single machines to large clusters. It functions as a cluster resource manager that orchestrates computational logic by representing tasks and their dependencies as directed acyclic graphs. This architecture allows the system to automate the distribution of workloads across available hardware while managing complex execution requirements. The project distinguishes itself through a lazy evaluation engine that defers data operations until they are explicitly requested, enabl

Luigi is a Python framework designed for building and managing complex batch data pipelines. It functions as a workflow orchestration engine that organizes tasks into directed acyclic graphs, ensuring that jobs execute in the correct logical order based on their dependencies. By utilizing a centralized scheduler, the system coordinates task execution across distributed environments, tracks global workflow state, and prevents redundant processing by verifying the existence of output targets before triggering any work. The project distinguishes itself through a robust state-tracking mechanism t

Ray is a distributed computing framework designed to scale Python and Java applications across clusters by abstracting task scheduling and resource management. It functions as a resource-aware execution engine that manages task dependencies, placement, and fault tolerance across networked compute nodes. At its core, the system provides a stateful actor model, allowing developers to define classes that run in dedicated processes to maintain and mutate internal state across remote method calls. The framework distinguishes itself through a robust cross-language interoperability layer, enabling f

Chronos is a distributed, fault-tolerant job scheduler designed for managing containerized workloads within a cluster. It functions as a task orchestrator that automates the execution of recurring background jobs and complex, multi-step workflows across distributed computing resources. The system distinguishes itself through its ability to manage directed acyclic graph dependencies, ensuring that tasks are triggered only upon the successful completion of prerequisite jobs. It utilizes a leader-follower consensus architecture to maintain high availability and state persistence, while relying o

ZenML is an extensible machine learning orchestration framework designed to manage the end-to-end lifecycle of data pipelines and AI agent workflows. It functions as a durable orchestrator that executes machine learning tasks as directed acyclic graphs, ensuring that every step is containerized for consistent performance across local, cloud, and hybrid infrastructure. By decoupling pipeline code from underlying compute and storage backends, the platform allows developers to define infrastructure-agnostic stacks that remain portable across diverse environments. The project distinguishes itself

SparkInternals is a technical reference and architecture guide detailing the internal design and implementation of the Apache Spark distributed computing engine. It serves as a study of big data engine analysis, focusing on how the system manages cluster execution and the interaction between driver nodes, executors, and workers. The project provides a detailed breakdown of how logical plans are converted into physical execution stages. It specifically analyzes the mechanics of data shuffle operations, memory management, and the coordination of distributed job scheduling. The documentation co

ZenML is an orchestration platform designed for building, deploying, and monitoring reproducible machine learning pipelines and agentic workflows. It provides a unified framework that manages the entire lifecycle of machine learning assets, from data processing and model training to the deployment of persistent inference services. By decoupling pipeline logic from underlying compute and storage, the platform enables teams to transition workflows seamlessly from local development environments to production-grade cloud infrastructure. The platform distinguishes itself through a service-oriented

Apache Druid is a real-time OLAP database and distributed analytics engine. It functions as a columnar time-series database designed for high-performance analytical queries and the real-time ingestion of streaming and batch datasets. The system provides a framework for high-concurrency analytics, allowing multiple simultaneous users to execute SQL and native queries across large-scale data. It supports mixed data ingestion, combining real-time streaming and batch loading into a single system for unified analysis. The platform includes capabilities for distributed cluster management, enabling

This project is a collection of educational resources and reference implementations for the Apache Flink stream processing framework. It provides a learning resource focused on mastering distributed stream processing through implementation guides, performance tuning tutorials, and practical examples. The repository features detailed walkthroughs for building real-time data pipelines using the DataStream and Table APIs. It includes specific integration examples for connecting Apache Flink with Kafka brokers and Elasticsearch indices, as well as reference implementations for real-time deduplica

Redpanda is a distributed event streaming engine designed to serve as a high-performance, drop-in replacement for existing event-driven architectures. It provides a foundation for building and scaling applications that require reliable data movement, analytical querying, and strict operational compliance across both cloud and self-managed environments. The platform distinguishes itself through a shared-nothing architecture that utilizes thread-per-core execution and a non-blocking asynchronous input/output engine to maximize throughput. It maintains data consistency through a consensus-based

StreamPark is a centralized management platform designed to coordinate the deployment, monitoring, and operational lifecycle of distributed stream processing and batch applications. It functions as a control plane and orchestrator for data pipelines, specifically providing management capabilities for Apache Flink and Hadoop YARN environments. The platform distinguishes itself through a low-code approach to task deployment and a multi-engine execution adapter that supports diverse processing runtimes. It facilitates real-time data pipeline management by combining streaming SQL analytics with a

Otter is a distributed database synchronization system and change data capture tool designed to replicate data between databases across multiple geographic regions. It functions as a synchronization orchestrator and ETL data pipeline that mirrors records and associated files in real time. The system employs incremental log parsing to capture database changes and utilizes a consistency-based convergence algorithm and loop-avoidance logic to manage bi-directional replication. It processes data through a pipeline of selection, extraction, transformation, and loading to handle joins and format co

Apache Pulsar is a cloud-native distributed pub-sub messaging system designed for high-performance data ingestion. It functions as a geo-replicated data streamer and a multi-tenant event streaming platform, providing a serverless stream processing engine and a tiered storage messaging broker. The system distinguishes itself by separating serving layers from storage layers to allow independent scaling of compute and data retention. It features native geo-replication to synchronize messages across different geographical regions and employs a multi-layered tenant isolation model using authentica

QuestDB is a high-performance, distributed time-series database designed for the ingestion, storage, and analysis of massive datasets. It functions as a real-time analytics platform that utilizes a columnar storage engine to optimize disk input and output, enabling efficient analytical scans and complex windowing operations on streaming data. The platform distinguishes itself through specialized capabilities for handling asynchronous time-series streams, including advanced join algorithms that align disparate data sets based on precise timestamp lookups. It supports high-volume ingestion thro

Apache Flink is a distributed processing engine designed for both high-throughput, low-latency data streams and finite batch workloads. It functions as a stateful stream processor and a SQL stream processing engine, providing a unified runtime to execute relational queries and event-based transformations. The system is distinguished by its ability to manage persistent operator state to ensure exactly-once processing guarantees and consistency during failures. It features specialized capabilities for complex event processing to detect temporal patterns and handles out-of-order events using eve

This project is a reference library of architectural blueprints, study materials, and design patterns for building scalable, high-availability distributed systems. It serves as a technical guide for scalability engineering, providing structural solutions for common engineering challenges. The repository focuses on distributed systems design, covering essential patterns for data replication, consensus algorithms, and transaction management. It distinguishes itself by offering detailed blueprints for specialized domains, including real-time data streaming, large-scale data storage, and high-ava