Open Source Big Data Tools
Sort By:
Big Data Tools
View 45 business solutionsBrowse free open source Big Data tools and projects for Mac and Linux below. Use the toggles on the left to filter open source Big Data tools by OS, license, language, programming language, and project status.
-
Veeam Data Platform v13.1Try Veeam Data Platform today. Experience the unified platform that's secure by design, portable by default, and proven to recover clean, fast, and anywhere.
-
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
1
pandas
Fast, flexible and powerful Python data analysis toolkit
pandas is a Python data analysis library that provides high-performance, user friendly data structures and data analysis tools for the Python programming language. It enables you to carry out entire data analysis workflows in Python without having to switch to a more domain specific language. With pandas, performance, productivity and collaboration in doing data analysis in Python can significantly increase. pandas is continuously being developed to be a fundamental high-level building block for doing practical, real world data analysis in Python, as well as powerful and flexible open source data analysis/ manipulation tool for any language. -
2
MOA - Massive Online Analysis
Big Data Stream Analytics Framework.
A framework for learning from a continuous supply of examples, a data stream. Includes classification, regression, clustering, outlier detection and recommender systems. Related to the WEKA project, also written in Java, while scaling to adaptive large scale machine learning. -
3
Vespa
The open big data serving engine
Make AI-driven decisions using your data, in real-time. At any scale, with unbeatable performance. Vespa is a full-featured text search engine and supports both regular text search and fast approximate vector search (ANN). This makes it easy to create high-performing search applications at any scale, whether you want to use traditional techniques or a modern vector-based approach. You can even combine both approaches efficiently in the same query, something no other engine can do. Recommendation, personalization and targeting involves evaluating recommender models over content items to select the best ones. Vespa lets you build applications which does this online, typically combining fast vector search and filtering with evaluation of machine-learned models over the items. This makes it possible to make recommendations specifically for each user or situation, using completely up to date information. -
4
Apache InLong
Apache InLong - a one-stop integration framework for massive data
Apache InLong is a one-stop integration framework for massive data that provides automatic, secure and reliable data transmission capabilities. InLong supports both batch and stream data processing at the same time, which offers great power to build data analysis, modeling and other real-time applications based on streaming data. InLong (应龙) is a divine beast in Chinese mythology who guides the river into the sea, and it is regarded as a metaphor of the InLong system for reporting data streams. InLong was originally built at Tencent, which has served online businesses for more than 8 years, to support massive data (data scale of more than 80 trillion pieces of data per day) reporting services in big data scenarios. The entire platform has integrated 5 modules: Ingestion, Convergence, Caching, Sorting, and Management, so that the business only needs to provide data sources, data service quality, data landing clusters and data landing formats. -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
-
5
Apache HBase
Get random, realtime read/write access to your Big Data
Use Apache HBase™ when you need random, realtime read/write access to your Big Data. This project's goal is the hosting of very large tables, billions of rows X millions of columns, atop clusters of commodity hardware. Apache HBase is an open-source, distributed, versioned, non-relational database modeled after Google's Bigtable. A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, Apache HBase provides Bigtable-like capabilities on top of Hadoop and HDFS. Thrift gateway and a REST-ful Web service that supports XML, Protobuf, and binary data encoding options. Support for exporting metrics via the Hadoop metrics subsystem to files or Ganglia; or via JMX. Convenient base classes for backing Hadoop MapReduce jobs with Apache HBase tables. -
6
GridDB
GridDB is a next-generation open source database
A cyber-physical systems is a system that collects a variety of data in physical space (the real world), analyzes and converts it into knowledge in cyberspace, and feeds the knowledge back to the real world to revitalize industry and solve social problems. GridDB is an open database that enables real-time processing of vast amounts of time-series data in physical space, which is necessary to realize a cyber-physical system. Multi-model architecture capable of supporting various data stores with time-series data-oriented and pluggable data stores for efficient real-time processing and management of huge amounts of time-series data at high frequency. Various architectural innovations, such as in-memory orientation with "memory as the main unit and disk as the secondary unit" and event-driven design with minimal overhead, have been incorporated to achieve processing capabilities that can handle petabyte-scale applications. -
7
ElasticJob
Distributed scheduled job framework
ElasticJob is a distributed scheduling solution consisting of two separate projects, ElasticJob-Lite and ElasticJob-Cloud. ElasticJob-Lite is a lightweight, decentralized solution that provides distributed task sharding services. ElasticJob-Cloud uses Mesos to manage and isolate resources. It uses a unified job API for each project. Developers only need code one time and can deploy at will. Support job sharding and high availability in distributed system. Scale out for throughput and efficiency improvement. Job processing capacity is flexible and scalable with the allocation of resources. Execute job on suitable time and assigned resources. Aggregation same job to same job executor. Append resources to newly assigned jobs dynamically. Using ElasticJob can make developers no longer worry about the non-functional requirements such as jobs scale out, so that they can focus more on business coding. -
8
HugeGraph
A graph database that supports more than 100+ billion data
HugeGraph is a convenient, efficient, and adaptable graph database compatible with the Apache TinkerPop3 framework and the Gremlin query language. HugeGraph supports fast import performance in the case of more than 10 billion Vertices and Edges Graph, millisecond-level OLTP query capability, and can be integrated into big data platforms like Hadoop or Spark for OLAP analysis. The main scenarios of HugeGraph include correlation search, fraud detection, and knowledge graph. Not only supports Gremlin graph query language and RESTful API but also provides commonly used graph algorithm APIs. To help users easily implement various queries and analyses, HugeGraph has a full range of accessory tools, such as supporting distributed storage, data replication, scaling horizontally, and supports many built-in backends of storage engines. -
9
XCharts
A charting and data visualization library for Unity
A charting and data visualization library for Unity. Unity data visualization chart plugin. A UGUIpowerful, easy-to-use, parameter-configurable data visualization chart plug-in. It supports ten built-in charts. A powerful, easy-to-use, configurable charting and data visualization library for Unity. Visual configuration of parameters, real-time preview of effects, and pure code drawing without additional resources. Support ten built-in charts such as line chart, column chart, pie chart, radar chart, scatter chart, heat map, ring chart, candlestick chart, polar coordinate, parallel coordinate and so on. Supports 3D column charts, funnel charts, pyramids, dashboards, water level charts, pictographic column charts, Gantt charts, rectangular tree charts and other extended charts. Line graphs such as line graphs, curve graphs, area graphs, and stepped line graphs are supported. -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
-
10
QuickRedis
QuickRedis is a free forever redis gui tool
QuickRedis is a free forever Redis Desktop manager. It supports direct connection, sentinel, and cluster mode, supports multiple languages, supports hundreds of millions of keys, and has an amazing UI. Supports both Windows, Mac OS X and Linux platform. -
11
X10
Performance and Productivity at Scale
X10 is a class-based, strongly-typed, garbage-collected, object-oriented language. To support concurrency and distribution, X10 uses the Asynchronous Partitioned Global Address Space programming model (APGAS). This model introduces two key concepts -- places and asynchronous tasks -- and a few mechanisms for coordination. With these, APGAS can express both regular and irregular parallelism, message-passing-style and active-message-style computations, fork-join and bulk-synchronous parallelism. Both its modern, type-safe sequential core and simple programming model for concurrency and distribution contribute to making X10 a high-productivity language in the HPC and Big Data spaces. User productivity is further enhanced by providing tools such as an Eclipse-based IDE (X10DT). Implementations of X10 are available for a wide variety of hardware and software platforms ranging from laptops, to commodity clusters, to supercomputers. -
12
json-scada
A portable SCADA/IoT platform centered on the MongoDB database server.
Standard IT tools applied to SCADA/IoT (MongoDB, PostgreSQL/TimescaleDB,Node.js, C#, Golang, Grafana, etc.). MongoDB as the real-time core database, persistence layer, config store, SOE historian. Portability and interoperability over Linux, Windows, x86/64, ARM. Horizontal scalability, from a single computer to big clusters (MongoDB-sharding), Bare Metal, Docker containers, VM, cloud, or hybrid deployments. Unlimited tags, servers, and users. HTML5 Web interface. UTF-8/I18N. Protocols: IEC61850 Client, IEC60870-5-101/103/104 Client and Server, DNP3 Client, ICCP/TASE2, OPC-UA Client/Server, Modbus, MQTT/Sparkplug-B, Telegraf (various data sources for monitoring like CPU, SNMP, etc.), MCP server. Github project https://github.com/riclolsen/json-scada Requirements for Windows Installer: Windows 10/11 64 bits or Server 2016, Windows PowerShell. -
13
FastoNoSQL
FastoNoSQL it is GUI platform for NoSQL databases.
Gui managment admin tool for: Redis Memcached SSDB LevelDB RocksDB UnQLite LMDB UpscaleDB ForestDB -
14
BIRT Report Designer
Open Source Reporting & Data Visualization Platform
BIRT is an open source technology platform used to create data visualizations and reports that can be embedded into rich client and web applications. Developers who use BIRT Designer are able to access information from multiple data sources easily and quickly in order to create reports and applications with stunning data visualizations. Actuate now provides a free report server, BIRT iHub F-Type, to deploy BIRT content so developers don't have to build their own infrastructure. With a flexible Open Data Access framework, developers can write custom data drivers to access data from any source, including Big Data sources like Apache Hadoop, Cassandra, and MongoDB, along with all traditional relational databases, Flat Files, XML data streams, and data stored in proprietary systems. Built for embedding, BIRT includes APIs for data access, chart generation, output formats, content execution, and integration within larger applications. -
15
gravitino
Unified metadata lake for data & AI assets.
Apache Gravitino is a high-performance, geo-distributed, and federated metadata lake. It manages metadata directly in different sources, types, and regions, providing users with unified metadata access for data and AI assets. -
16
LEACrypt
TTAK.KO-12.0223 Lightweight Encryption Algorithm Tool
The Lightweight Encryption Algorithm (also known as LEA) is a 128-bit block cipher developed by South Korea in 2013 to provide confidentiality in high-speed environments such as big data and cloud computing, as well as lightweight environments such as IoT devices and mobile devices. LEA is one of the cryptographic algorithms approved by the Korean Cryptographic Module Validation Program (KCMVP) and is the national standard of Republic of Korea (KS X 3246). LEA is included in the ISO/IEC 29192-2:2019 standard (Information security - Lightweight cryptography - Part 2: Block ciphers). This project is licensed under the ISC License. Copyright © 2020-2021 ALBANESE Research Lab Source code: https://github.com/pedroalbanese/leacrypt Visit: http://albanese.atwebpages.com -
17
FastoRedis
Cross-platform open source Redis DB management tool
FastoRedis (fork of FastoNoSQL) — is a cross-platform open source Redis management tool (i.e. Admin GUI). It put the same engine that powers Redis's redis-cli shell. Everything you can write in redis-cli shell — you can write in FastoRedis! Our program works on the most amount of Linux systems, also on Windows, Mac OS X, FreeBSD and Android platforms, on desktops and embedded devices. -
18
An introduction to Data Analysis in R
A guide for learning the basic tools on data anaylisis with R
An Introduction to Data Analysis in R [Book] A guide for learning the basic tools on data anaylisis: process, visualize and learn from your data using R programming. This repository holds the necessary data sets for the book "An introduction to Data Analysis in R", to be published by Springer series Use R!. The book can be purchased in XXX. The book is meant as an introductory guide to manipulate data sets in the Big Data paradigm. One of the main goals of this book is to take the analyst from the very first moment when she/he contacts with data to the final conclusion and presentation of results of analysis. We take into account the variety of fields where data analysis occurs nowadays. We pay special attention to the different ways to obtain data and how to make it manageable before starting the analysis. The data analysis includes most of the basic visualization options and some advanced extra options. Finally, basic statistics is used to learn from the processed data. -
19
NCHC-Storm
NCHC's Storm Team
Sharing the applications of storm which developed by NCHC's Storm Team. -
20
Random Bits Forest
RBF: a Strong Classifier/Regressor for Big Data
We present a classification and regression algorithm called Random Bits Forest (RBF). RBF integrates neural network (for depth), boosting (for wideness) and random forest (for accuracy). It first generates and selects ~10,000 small three-layer threshold random neural networks as basis by gradient boosting scheme. These binary basis are then feed into a modified random forest algorithm to obtain predictions. In conclusion, RBF is a novel framework that performs strongly especially on data with large size. -
21
.NET for Apache Spark
A free, open-source, and cross-platform big data analytics framework
.NET for Apache Spark provides high-performance APIs for using Apache Spark from C# and F#. With these .NET APIs, you can access the most popular Dataframe and SparkSQL aspects of Apache Spark, for working with structured data, and Spark Structured Streaming, for working with streaming data. .NET for Apache Spark is compliant with .NET Standard - a formal specification of .NET APIs that are common across .NET implementations. This means you can use .NET for Apache Spark anywhere you write .NET code allowing you to reuse all the knowledge, skills, code, and libraries you already have as a .NET developer. .NET for Apache Spark runs on Windows, Linux, and macOS using .NET Core, or Windows using .NET Framework. It also runs on all major cloud providers including Azure HDInsight Spark, Amazon EMR Spark, AWS & Azure Databricks. -
22
Apache Doris
MPP-based interactive SQL data warehousing for reporting and analysis
Apache Doris is a modern MPP analytical database product. It can provide sub-second queries and efficient real-time data analysis. With it's distributed architecture, up to 10PB level datasets will be well supported and easy to operate. Apache Doris can meet various data analysis demands, including history data reports, real-time data analysis, interactive data analysis, and exploratory data analysis. Make your data analysis easier! Support standard SQL language, compatible with MySQL protocol. The main advantages of Doris are the simplicity (of developing, deploying and using) and meeting many data serving requirements in a single system. Doris mainly integrates the technology of Google Mesa and Apache Impala, and it is based on a column-oriented storage engine and can communicate by MySQL client. -
23
Apache Hudi
Upserts, Deletes And Incremental Processing on Big Data
Apache Hudi (pronounced Hoodie) stands for Hadoop Upserts Deletes and Incrementals. Hudi manages the storage of large analytical datasets on DFS (Cloud stores, HDFS or any Hadoop FileSystem compatible storage). Apache Hudi is a transactional data lake platform that brings database and data warehouse capabilities to the data lake. Hudi reimagines slow old-school batch data processing with a powerful new incremental processing framework for low latency minute-level analytics. Hudi provides efficient upserts, by mapping a given hoodie key (record key + partition path) consistently to a file id, via an indexing mechanism. This mapping between record key and file group/file id, never changes once the first version of a record has been written to a file. In short, the mapped file group contains all versions of a group of records. -
24
Apache Polaris
Apache Polaris, the interoperable, open source catalog
Apache Polaris is an open-source metadata catalog and data management service designed to manage Apache Iceberg tables in modern data lakehouse environments. It provides a centralized catalog that allows multiple compute engines and analytics systems to interact with the same datasets through a standardized interface. By implementing the Iceberg REST catalog API, Polaris enables distributed data platforms to access shared table metadata without tightly coupling storage systems and query engines. This design allows organizations to run queries on the same Iceberg tables using tools such as Apache Spark, Flink, Trino, and other analytics engines while maintaining consistency across platforms. Polaris also focuses on data governance, security, and interoperability within large-scale cloud data architectures. Because Iceberg tables often exist across many services in a distributed ecosystem, the catalog helps coordinate metadata, schemas, and access policies in a unified system. -
25
Apache RocketMQ
Distributed messaging and streaming platform with low latency
Apache RocketMQ is a distributed messaging and streaming platform with low latency, high performance and reliability, trillion-level capacity and flexible scalability. Messaging patterns including publish/subscribe, request/reply and streaming. Financial grade transactional message. Built-in fault tolerance and high availability configuration options base on DLedger. A variety of cross language clients, such as Java, C/C++, Python, Go. Pluggable transport protocols, such as TCP, SSL, AIO. Built-in message tracing capability, also support opentracing. Versatile big-data and streaming ecosytem integration. Message retroactivity by time or offset. Reliable FIFO and strict ordered messaging in the same queue. Efficient pull and push consumption model. Million-level message accumulation capacity in a single queue. Multiple messaging protocols like JMS and OpenMessaging. Flexible distributed scale-out deployment architecture. Lightning-fast batch message exchange system.
