awesome-dataops

A multi-model database that supports both graph and relational data models.

Can be used to capture, store and process time-series data in real-time.

A sorted, distributed key-value store that provides robust and scalable data storage.

Open source column based DBMS designed to handle large amounts of data.

An open-source document-oriented NoSQL database, implemented in Erlang.

Designed to quickly ingest massive quantities of event data, and provide low-latency queries.

A virtual distributed storage system.

Object storage built to retrieve any amount of data from anywhere.

A distributed file system.

A software defined distributed storage that can scale to several petabytes.

Object storage for companies of all sizes, to store any amount of data.

Open source tool that transforms your object storage into a Git-like repository.

Easily collect, process, and analyze video and data streams in real time.

A framework that simplifies common aspects of big data such as data ingestion.

Open-source distributed event streaming platform used by thousands of companies.

Distributed pub-sub messaging platform with a flexible messaging model and intuitive API.

A parallel bulk data loader that helps data transfer between various storages.

Collects events from various data sources and writes them to files.

Accelerate your time to insights with fast, easy, and secure cloud data warehousing.

Facilitates reading, writing, and managing large datasets residing in distributed storage.

An open source, distributed analytical data warehouse for big data.

Serverless, highly scalable, and cost-effective multicloud data warehouse.

Data discovery and metadata engine for improving the productivity when interacting with data.

Provides open metadata management and governance capabilities to build a data catalog.

Open-source DMS (data management system) for powering data hubs and data portals.

LinkedIn's generalized metadata search & discovery tool.

A federated, open-source data catalog for all your big data and small data.

Service for the collection, aggregation, and visualization of a data ecosystem's metadata.

A platform to programmatically author, schedule, and monitor workflows.

An extensible, scalable and reliable system to manage complex Hadoop workloads.

Batch workflow job scheduler created at LinkedIn to run Hadoop jobs.

An orchestration platform for the development, production, and observation of data assets.

Python module that helps you build complex pipelines of batch jobs.

A workflow management system, designed for modern infrastructure.

A data serialization system which is compact, fast and provides rich data structures.

Manages the storage of large analytical datasets on DFS.

Open table format for huge analytic datasets.

A self-describing type-aware columnar file format designed for Hadoop workloads.

A columnar storage format which provides efficient storage and encoding of data.

An open source project that enables building a Lakehouse architecture on top of data lakes.

A unified model for defining both batch and streaming data-parallel processing pipelines.

An open source stream processing framework with powerful capabilities.

A framework for writing applications which process vast amounts of data.

An easy to use, powerful, and reliable system to process and distribute data.

A distributed stream processing framework which uses Apache Kafka and Hadoop YARN.

A unified analytics engine for large-scale data processing.