The Big-Data Ecosystem Table

framework for distributed processing. Integrates MapReduce (parallel processing), YARN (job scheduling) and HDFS (distributed file system)

Hydra is a distributed data processing and storage system originally developed at AddThis. It ingests streams of data (think log files) and builds trees that are aggregates, summaries, or transformations of the data. These trees can be used by humans to explore (tiny queries), as part of a machine learning pipeline (big queries), or to support live consoles on websites (lots of queries).

Mozilla’s utility library for Hadoop, HBase, Pig, etc.

a compute service that runs your code in response to events and automatically manages the compute resources for you

Super-fast Parallel In-memory Calculation Engine

A RESTful web service that runs microtasks across multiple crowds

a novel mini-batch execution model that generalizes OLA to support general OLAP queries with arbitrarily nested aggregates using efficient delta maintenance techniques

Apache Spark was developed thinking in Apache YARN. However, up to now, it has been relatively hard to run Apache Spark on Hadoop MapReduce v1 clusters, i.e. clusters that do not have YARN installed. Typically, users would have to get permission to install Spark/Scala on some subset of the machines, a process that could be time consuming. SIMR allows anyone with access to a Hadoop MapReduce v1 cluster to run Spark out of the box. A user can run Spark directly on top of Hadoop MapReduce v1 without any administrative rights, and without having Spark or Scala installed on any of the nodes.

is a simple Java API for tasks like joining and data aggregation that are tedious to implement on plain MapReduce. The APIs are especially useful when processing data that does not fit naturally into relational model, such as time series, serialized object formats like protocol buffers or Avro records, and HBase rows and columns. For Scala users, there is the Scrunch API, which is built on top of the Java APIs and includes a REPL (read-eval-print loop) for creating MapReduce pipelines.

DataFu provides a collection of Hadoop MapReduce jobs and functions in higher level languages based on it to perform data analysis. It provides functions for common statistics tasks (e.g. quantiles, sampling), PageRank, stream sessionization, and set and bag operations. DataFu also provides Hadoop jobs for incremental data processing in MapReduce. DataFu is a collection of Pig UDFs (including PageRank, sessionization, set operations, sampling, and much more) that were originally developed at LinkedIn.

high-performance runtime, and automatic program optimization

framework for in-memory data model and persistence

Apache Top-Level open source project, allowing you to do advanced analytics beyond MapReduce. Many data analysis techniques such as machine learning and graph algorithms require iterative computations, this is where Bulk Synchronous Parallel model can be more effective than “plain” MapReduce.

high-performance, integrated and distributed in-memory platform for computing and transacting on large-scale data sets in real-time

MapReduce is a programming model for processing large data sets with a parallel, distributed algorithm on a cluster. Apache MapReduce was derived from Google MapReduce: Simplified Data Processing on Large Clusters paper. The current Apache MapReduce version is built over Apache YARN Framework. YARN stands for “Yet-Another-Resource-Negotiator”. It is a new framework that facilitates writing arbitrary distributed processing frameworks and applications. YARN’s execution model is more generic than the earlier MapReduce implementation. YARN can run applications that do not follow the MapReduce model, unlike the original Apache Hadoop MapReduce (also called MR1). Hadoop YARN is an attempt to take Apache Hadoop beyond MapReduce for data-processing.

Pig provides an engine for executing data flows in parallel on Hadoop. It includes a language, Pig Latin, for expressing these data flows. Pig Latin includes operators for many of the traditional data operations (join, sort, filter, etc.), as well as the ability for users to develop their own functions for reading, processing, and writing data. Pig runs on Hadoop. It makes use of both the Hadoop Distributed File System, HDFS, and Hadoop’s processing system, MapReduce.

S4 is a general-purpose, distributed, scalable, fault-tolerant, pluggable platform that allows programmers to easily develop applications for processing continuous unbounded streams of data.

Data analytics cluster computing framework originally developed in the AMPLab at UC Berkeley. Spark fits into the Hadoop open-source community, building on top of the Hadoop Distributed File System (HDFS). However, Spark provides an easier to use alternative to Hadoop MapReduce and offers performance up to 10 times faster than previous generation systems like Hadoop MapReduce for certain applications.

framework for stream processing, part of Spark

Storm is a complex event processor and distributed computation framework written predominantly in the Clojure programming language. Is a distributed real-time computation system for processing fast, large streams of data. Storm is an architecture based on master-workers paradigma. So a Storm cluster mainly consists of a master and worker nodes, with coordination done by Zookeeper.

Tez is a proposal to develop a generic application which can be used to process complex data-processing task DAGs and runs natively on Apache Hadoop YARN.

Twill is an abstraction over Apache Hadoop® YARN that reduces the complexity of developing distributed applications, allowing developers to focus more on their business logic. Twill uses a simple thread-based model that Java programmers will find familiar. YARN can be viewed as a compute fabric of a cluster, which means YARN applications like Twill will run on any Hadoop 2 cluster.

Spins a web of microservices around unsuspecting sysadmins

Python users high-level access to efficient computation on inconveniently large data

data processing and querying library

High Performance, Custom Data Warehouse on Top of MapReduce

Application framework for Java developers to simply develop robust Data Analytics and Data Management applications on Apache Hadoop.

Library for develop MapReduce programs using the LISP like language Clojure. Parkour aims to provide deep Clojure integration for Hadoop. Programs using Parkour are normal Clojure programs, using standard Clojure functions instead of new framework abstractions. Programs using Parkour are also full Hadoop programs, with complete access to absolutely everything possible in raw Java Hadoop MapReduce.

A new MapReduce paradigm. A new API for MR jobs, in higher level than Java.

real-time engine is designed to enable distributed, asynchronous, real time in-memory big-data computations in as unblocked a way as possible, with minimal overhead and impact on performance.

scalable high-performance platform for the R language

a Business Rules Management System (BRMS) solution

REST based interface for PIG execution

a highly scalable, memory-efficient, in-memory computing, SQL-standard, minimal latency, real-time streaming-capable Big Data processing engine for historical data

“The next version of Map-Reduce“ from Facebook, based in own fork of Hadoop. The current Hadoop implementation of the MapReduce technique uses a single job tracker, which causes scaling issues for very large data sets. The Apache Hadoop developers have been creating their own next-generation MapReduce, called YARN, which Facebook engineers looked at but discounted because of the highly-customised nature of the company’s deployment of Hadoop and HDFS. Corona, like YARN, spawns multiple job trackers (one for each job, in Corona’s case).

Map Reduce framework

distributed in-memory datastore

a lightweight real-time big data streaming engine

geographic data processing engine for high performance applications

a Python library, which allows you to build newsfeed and notification systems using Cassandra and/or Redis

Big Data Spatial Analytics for the Hadoop Framework

create data pipelines to help themæingest, transform and analyze data

Easy, Efficient Data-Parallel Pipelines. Base of Google Dataflow

map reduce framework

fault tolerant stream processing framework

fast, scalable engine of GraphLab Create, a Python library

In-Memory Data Grid

data parsing transformation environment optimized for Hadoop

advanced analytic platform that allows user-developed applications to quickly ingest, analyze and correlate information as it arrives from thousands of real-time sources

JAQL is a functional, declarative programming language designed especially for working with large volumes of structured, semi-structured and unstructured data. As its name implies, a primary use of JAQL is to handle data stored as JSON documents, but JAQL can work on various types of data. For example, it can support XML, comma-separated values (CSV) data and flat files. A “SQL within JAQL” capability lets programmers work with structured SQL data while employing a JSON data model that’s less restrictive than its Structured Query Language counterparts.

is a set of libraries, tools, examples, and documentation focused on making it easier to build systems on top of the Hadoop ecosystem.

Java serialization and cloning: fast, efficient, automatic

a fast and efficient batch computation engine for complex analysis and reporting of massive datasets on Hadoop

Pig workflow visualization tool

Realtime analytical data store.

an event processing engine that helps uncover real-time insights from devices, sensors, infrastructure, applications and data

a straightforward approach to building distributed high-scale computing applications

a framework that provides a straightforward approach to building distributed high-scale computing applications

a high-performance in-memory incremental analytics engine

Bulk Data Pipeline out of Cassandra. implements a reader for the SSTable format and provides a map/reduce program to create a compacted snapshot of the data contained in a column family

Pig Visualization framework

Event Stream Processing System

PigPen is map-reduce for Clojure whiche compiles to Apache Pig. Clojure is dialect of the Lisp programming language created by Rich Hickey, so is a functional general-purpose language, and runs on the Java Virtual Machine, Common Language Runtime, and JavaScript engines. In PigPen there are no special user defined functions (UDFs). Define Clojure functions, anonymously or named, and use them like you would in any Clojure program. This tool is open sourced by Netflix, Inc. the American provider of on-demand Internet streaming media.

language-agnostic as well as storage-agnostic web interface for storing data into persistent storage systems

a collection of tools for analysis in Pig and Hive

Netflix’s In-Memory Data Propagation Framework

Dataflow oriented toolkit for parallel and distributed computational pipelines

MapReduce framework developed by Nokia

is a realization of the lambda architecture built on Apache Spark and Apache Kafka, but with specialization for real-time large scale machine learning

lets you store and analyze your data using containers.

streamparse lets you run Python code against real-time streams of data. It also integrates Python smoothly with Apache Storm.

PigPen is map-reduce for Clojure, or distributed Clojure. It compiles to Apache Pig, but you don’t need to know much about Pig to use it

asynchronous job execution system

Data stream network

Pydoop is a Python MapReduce and HDFS API for Hadoop, built upon the C++ Pipes and the C libhdfs APIs, that allows to write full-fledged MapReduce applications with HDFS access. Pydoop has several advantages over Hadoop’s built-in solutions for Python programming, i.e., Hadoop Streaming and Jython: being a CPython package, it allows you to access all standard library and third party modules, some of which may not be available.

fast, scalable in-memory data grid for Hadoop

Simple and scalable scripting for large sequencing data set(ex: bioinfomation) in Hadoop

Pig on Apache Spark

Stanford Network Analysis Platform is a general purpose, high performance system for analysis and manipulation of large networks

allows users to execute dataflow pipelines with Spark

SpatialHadoop is a MapReduce extension to Apache Hadoop designed specially to work with spatial data.

unified configuration model and easy to use APIs for using HDFS, MapReduce, Pig, and Hive

stream processing platform

provides an unified way to access to multiple datastores

the union of a real-time messaging bus with a complex event processing engine using Spark Streaming

the union of a real-time messaging bus with a complex event processing engine using Spark Streaming

Stratosphere is a general purpose cluster computing framework. It is compatible to the Hadoop ecosystem: Stratosphere can access data stored in HDFS and runs with Hadoop’s new cluster manager YARN. The common input formats of Hadoop are supported as well. Stratosphere does not use Hadoop’s MapReduce implementation: it is a completely new system that brings its own runtime. The new runtime allows to define more advanced operations that include more transformations than just map and reduce. Additionally, Stratosphere allows to express analysis jobs using advanced data flow graphs, which are able to resemble common data analysis task more naturally.

usefull for counting activities of event streams over different time windows and finding the most active one

Enabling Queries on Compressed Data

cloud based analyzer for machine-generated data.

data-access layer that can orchestrate multiple modes of analysis across multiple databases plus Hadoop

in-memory data grid

a distributed framework built on Apache HadoopTM and Apache HBaseTM for real-time, high-throughput, low-latency data processing and analytics applications

Scientific computing for LuaJIT

a high-level abstraction for doing realtime computing on top of Storm

Java ETL

a flexible sharding framework for creating eventually-consistent distributed datastores

a realtime, distributed, fault-tolerant stream processing engine from Twitter

Scala library for Map Reduce jobs, built on Cascading

a system that aims to mitigate the tradeoffs between batch processing and stream processing by combining them into a hybrid system. In the case of Twitter, Hadoop handles batch processing, Storm handles stream processing, and the hybrid system is called Summingbird.

TimeSeries AggregatoR by Twitter

file storage service for Amazon Elastic Compute Cloud (Amazon EC2) instances

secure, durable, highly-scalable object storage

The Hadoop Distributed File System (HDFS) offers a way to store large files across multiple machines. Hadoop and HDFS was derived from Google File System (GFS) paper. Prior to Hadoop 2.0.0, the NameNode was a single point of failure (SPOF) in an HDFS cluster. With Zookeeper the HDFS High Availability feature addresses this problem by providing the option of running two redundant NameNodes in the same cluster in an Active/Passive configuration with a hot standby.

completes Hadoop’s storage layer to enable fast analytics on fast data

formerly FhGFS, parallel distributed file system

Ceph is a free software storage platform designed to present object, block, and file storage from a single distributed computer cluster. Ceph’s main goals are to be completely distributed without a single point of failure, scalable to the exabyte level, and freely-available. The data is replicated, making it fault tolerant. The problem right now is Ceph currently requires Hadoop 1.1.X stable series.

distributed filesystem

object storage system

durable and highly available object storage

a highly available, affordable solution for backup, archiving and disaster recovery.

distributed filesystem (GFS2)

distributed filesystem

scalable, highly available storage

GridGain is open source project licensed under Apache 2.0. One of the main pieces of this platform is the In-Memory Apache Hadoop Accelerator which aims to accelerate HDFS and Map/Reduce by bringing both, data and computations into memory. This work is done with the GGFS - Hadoop compliant in-memory file system. For I/O intensive jobs GridGain GGFS offers performance close to 100x faster than standard HDFS. Paraphrasing Dmitriy Setrakyan from GridGain Systems talking about GGFS regarding Tachyon: GGFS allows read-through and write-through to/from underlying HDFS or any other Hadoop compliant file system with zero code change. Essentially GGFS entirely removes ETL step from integration.GGFS has ability to pick and choose what folders stay in memory, what folders stay on disc, and what folders get synchronized with underlying (HD)FS either synchronously or asynchronously. GridGain is working on adding native MapReduce component which will provide native complete Hadoop integration without changes in API, like Spark currently forces you to do. Essentially GridGain MR+GGFS will allow to bring Hadoop completely or partially in-memory in Plug-n-Play fashion without any API changes.

HDFS-DU is an interactive visualization of the Hadoop distributed file system.

The Lustre filesystem is a high-performance distributed filesystem intended for larger network and high-availability environments. Traditionally, Lustre is configured to manage remote data storage disk devices within a Storage Area Network (SAN), which is two or more remotely attached disk devices communicating via a Small Computer System Interface (SCSI) protocol. This includes Fibre Channel, Fibre Channel over Ethernet (FCoE), Serial Attached SCSI (SAS) and even iSCSI.

Distributed filesystem from MapR

a hyper scale repository for big data analytic workloads

library that provides an additional layer of consistency checking on top of Amazon’s S3 index through use of a consistent, secondary index

(QFS) is an open-source distributed file system software package for large-scale MapReduce or other batch-processing workloads. It was designed as an alternative to Apache Hadoop’s HDFS, intended to deliver better performance and cost-efficiency for large-scale processing clusters. It is written in C++ and has fixed-footprint memory management. QFS uses Reed-Solomon error correction as method for assuring reliable access to data.

GlusterFS is a scale-out network-attached storage file system. GlusterFS was developed originally by Gluster, Inc., then by Red Hat, Inc., after their purchase of Gluster in 2011. In June 2012, Red Hat Storage Server was announced as a commercially-supported integration of GlusterFS with Red Hat Enterprise Linux. Gluster File System, known now as Red Hat Storage Server.

Tachyon is an memory distributed file system. By storing the file-system contents in the main memory of all cluster nodes, the system achieves higher throughput than traditional disk-based storage systems like HDFS.

column-oriented analytic database

Distributed key/value store is a robust, scalable, high performance data storage and retrieval system. Apache Accumulo is based on Google’s BigTable design and is built on top of Apache Hadoop, Zookeeper, and Thrift. Accumulo is software created by the NSA with security features.

Distributed Non-SQL DBMS, it’s a BDDB. MR can retrieve data from Cassandra. This BDDB can run without HDFS, or on-top of HDFS (DataStax fork of Cassandra). HBase and its required supporting systems are derived from what is known of the original Google BigTable and Google File System designs (as known from the Google File System paper Google published in 2003, and the BigTable paper published in 2006). Cassandra on the other hand is a recent open source fork of a standalone database system initially coded by Facebook, which while implementing the BigTable data model, uses a system inspired by Amazon’s Dynamo for storing data (in fact much of the initial development work on Cassandra was performed by two Dynamo engineers recruited to Facebook from Amazon).

Google BigTable Inspired. Non-relational distributed database. Ramdom, real-time r/w operations in column-oriented very large tables (BDDB: Big Data Data Base). It’s the backing system for MR jobs outputs. It’s the Hadoop database. It’s for backing Hadoop MapReduce jobs with Apache HBase tables

Evolution of HBase made by Facebook

column-oriented distributed datastore

is a fully managed, schemaless database for storing non-relational data built on top of Google’s BigTable infrastructure

Database system inspired by publications on the design of Google’s BigTable. The project is based on experience of engineers who were solving large-scale data-intensive tasks for many years. Hypertable runs on top of a distributed file system such as the Apache Hadoop DFS, GlusterFS, or the Kosmos File System (KFS). It is written almost entirely in C++. Sposored by Baidu the Chinese search engine.

is accessed through a MySQL interface and use massive parallel processing to parallelize queries

fast, scalable, and enterprise-ready in-Hadoop database architected to manage big data

Co-Process for backup/recovery, Token Management, and Centralized Configuration management for Cassandra

improved version of HBase

a massively scalable datastore and transactional layer that can be placed on top of any key-value store to give it ACID properties

NoSQL databases on top of Apache Accumulo

Cassandra index functionality has been extended to provide near real time search such as ElasticSearch or Solr, including full text search capabilities and multivariable, geospatial and bitemporal search

Transactions for HBase

real-time, multi-tenant distributed database for Twitter scale

commercial object-oriented database management systems

a highly available and flexible non-relational data store that offloads the work of database administration

The scalable blockchain database.

a database software for high-speed storage and large-scale processing of XML and JSON data on clusters of commodity hardware

is an open source massively scalable data store. It requires zero administration.

Facebook’s Paxos-like NoSQL database

document oriented datastore over Hadoop

Distributed object store

horizontally scalable document-oriented NoSQL data store

Schema-agnostic Enterprise NoSQL database technology

fully-managed, highly-scalable, NoSQL document database service

a unique hybrid cloud storage solution that lowers costs and improves data protection

Document-oriented database system. It is part of the NoSQL family of database systems. Instead of storing data in tables as is done in a “classical” relational database, MongoDB stores structured data as JSON-like documents

A transactional, open-source Document Database

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

a modern document store which provides advanced scalability and elasticity features without sacrificing consistency

High-Performance MongoDB Distribution

Tokutek claims to improve MongoDB performance 20x

NoSQL flash-optimized, in-memory. Open source and “Server code in ‘C’ (not Java or Erlang) precisely tuned to avoid context switching and memory copies.

distributed key/value store, implementation of Dynamo

Fast Key-Value Storage Engine Based on Hierarchical B+-Tree Trie

Edis is a protocol-compatible Server replacement for Redis, written in Erlang. Edis’s goal is to be a drop-in replacement for Redis when persistence is more important than holding the dataset in-memory. Edis (currently) uses Google’s leveldb as a backend. Future plans call for a multi-master clustering model. Near term goals are to act as a read-slave for existing Redis servers.

Distributed database specialized in exporting data from Hadoop

An open-source, functional database with support for Complex Event Processing. It provides a persistence engine for applications using event-sourcing, or for storing time-series data. Event Store is written in C#, C++ for the server which runs on Mono or the .NET CLR, on Linux or Windows. Applications using Event Store can be written in JavaScript.

an in-memory, column-oriented, relational database management system

next generation key-value store

a distributed key-value datastore

is a simple persistent data store with very low latency and high throughput. It is designed for easy integration with read-write-intensive applications with little effort in tuning configuration, performance and JVM garbage collection.

Distributed data store that is designed as a key-value store used by LinkedIn for high-scalability storage.

a distributed key-value storage system designed for persistent

thin Dynamo-based replication for cached data

distributed key-value database by Oracle Corporation

A fast, high availability, fully Redis compatible store

storage system that provides large-scale low-latency storage by keeping all data in DRAM all the time and aggregating the main memories of thousands of servers

Distributed database fully compatible with redis protocol

Redis is an open-source, networked, in-memory, key-value data store with optional durability. It is written in ANSI C. In its outer layer, the Redis data model is a dictionary which maps keys to values. One of the main differences between Redis and other structured storage systems is that Redis supports not only strings, but also abstract data types. Sponsored by Pivotal and VMWare. It’s BSD licensed.

distributed implementation of Redis

system designed to help managing Redis instances

a decentralized datastore.

a distributed transactional key-value store

library to work with asynchronous key value stores, by Twitter

an efficient NoSQL database and a Lua application server.

key-value store that’s replicated and sharded and provides atomic multirow writes

hosted, distributed and geographically replicated key-valueÊcloud storage platform

Apache Giraph is an iterative graph processing system built for high scalability. For example, it is currently used at Facebook to analyze the social graph formed by users and their connections. Giraph originated as the open-source counterpart to Pregel, the graph processing architecture developed at Google

implementation of Pregel, part of Spark

An open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient sql-like query language or JavaScript extensions.

Doradus is a REST service that extends a Cassandra NoSQL database with a graph-based data model, advanced indexing and search features, and a REST API

TAO is the distributed data store that is widely used at facebook to store and serve the social graph. The entire architecture is highly read optimized, supports a graph data model and works across multiple geographical regions.

Hadoop-based graph analytics engine for analyzing graphs represented across a multi-machine compute cluster

open-source graph database.

graph processing framework

a core C++ GraphLab API and a collection of high-performance machine learning and data mining toolkits built on top of the GraphLab API. In addition, we are actively developing new interfaces to allow users to leverage the GraphLab API from other languages and technologies.

A Resilient Distributed Graph System on Spark

graph traversal Language.

general purpose, open-source data storage mechanism based on a powerful knowledge management formalism known as directed hypergraphs

distributed graph database

RDF-centric Map/Reduce framework

library which provides tools to construct large-scale graphs on top of Apache Hadoop

Massively Parallel Graph processing on GPUs

extends a Neo4j graph database to run scheduled big data graph compute algorithms at scale with HDFS and Apache Spark.

cypher compatibile, high-performance in-memory transactional and real-time analytics graph database

a distributed, in-memory, large graph processing engine, underpinned by a strongly-typed RAM store and a general computation engine

An open-source graph database writting entirely in Java. It is an embedded, disk-based, fully transactional Java persistence engine that stores data structured in graphs rather than in tables.

It is an Open Source NoSQL DBMS with the features of both Document and Graph DBMSs. Written in Java, it is incredibly fast: it can store up to 150,000 records per second on common hardware.

framework for large scale graph processing

Pinterest’s Graph Storage Service

scalable high-performance graph database

graph database: search, query, reasoning, and constraints in a lightweight, pure Java system

distributed graph database, built over Cassandra

distribuited graph database

commercially supported, open-source SQL relational database management system

BayesDB, a Bayesian database table, lets users query the probable implications of their tabular data as easily as an SQL database lets them query the data itself. Using the built-in Bayesian Query Language (BQL), users with no statistics training can solve basic data science problems, such as detecting predictive relationships between variables, inferring missing values, simulating probable observations, and identifying statistically similar database entries.

Scalable, Geo-Replicated, Transactional Datastore

distributed database designed to enable scalable, flexible and intelligent applications.

distributed database, inspired by F1, aquired Akiban server

distributed SQL database built on Spanner

globally distributed semi-relational database

is an experimental main-memory, parallel database management system that is optimized for on-line transaction processing (OLTP) applications. It is a highly distributed, row-store-based relational database that runs on a cluster on shared-nothing, main memory executor nodes.

HandlerSocket is a NoSQL plugin for MySQL/MariaDB (the storage engine of MySQL). It works as a daemon inside the mysqld process, accepting TCP connections, and executing requests from clients. HandlerSocket does not support SQL queries. Instead, it supports simple CRUD operations on tables. HandlerSocket can be much faster than mysqld/libmysql in some cases because it has lower CPU, disk, and network overhead.

object-relational database management system

infinity scalable RDBMS

in memory SQL database witho optimized columnar storage on flash

SQL/ACID compliant distributed database

object-relational database management system

in-memory, relational database management system with persistence and recoverability

Low-latency, in-memory, distributed SQL data store. Provides SQL interface to in-memory table data, persistable in HDFS.

is an in-memory, column-oriented, relational database management system

Track your customer data to Amazon Redshift

Open-source, distributed, realtime, semi-structured database. Some Features: Full-text search, Fast realtime updates, Structured and faceted search, BQL: SQL-like query language, Fast key-value lookup, High performance under concurrent heavy update and query volumes, Hadoop integration

Sky is an open source database used for flexible, high performance analysis of behavioral data. For certain kinds of data such as clickstream data and log data, it can be several orders of magnitude faster than traditional approaches such as SQL databases or Hadoop.

SymmetricDS is open source software for both file and database synchronization with support for multi-master replication, filtered synchronization, and transformation across the network in a heterogeneous environment. It supports multiple subscribers with one direction or bi-directional, asynchronous data replication. It uses web and database technologies to replicate data as a scheduled or near real-time operation. The software was designed to scale for a large number of nodes, work across low-bandwidth connections, and withstand periods of network outage. It works with most operating systems, file systems, and databases, including Oracle, MySQL, MariaDB, PostgreSQL, MS SQL Server (including Azure), IBM DB2, H2, HSQLDB, Derby, Firebird, Interbase, Informix, Greenplum, SQLite (including Android), Sybase ASE, and Sybase ASA (SQL Anywhere) databases.

complete relational database management system

in-memory NewSQL database

data warehouse service, based on PostgreSQL

Powering Columnar In-Memory Analytics

column oriented DBMS

framework for interactive analysis, implementation of Dremel

framework for interactive analysis, implementation of Dremel

column store database

columnar storage format for Hadoop.

purpose-built, dedicated analytic data warehouse

The grid-based, column-oriented, Vertica Analytics Platform is designed to manage large, fast-growing volumes of data and provide very fast query performance when used for data warehouses and other query-intensive applications. The product claims to drastically improve query performance over traditional relational database systems, provide high-availability, and petabyte scalability on commodity enterprise servers.

fast and efficient time series storage based on Apache Lucene and Apache Solr

uses MongoDB to store time series data

simple daemon for easy stats aggregation

InfluxDB is an open source distributed time series database with no external dependencies. It’s useful for recording metrics, events, and performing analytics. It has a built-in HTTP API so you don’t have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out. It aims to answer queries in real-time. That means every data point is indexed as it comes in and is immediately available in queries that should return in

Time series data storage in Redis, Mongo, SQL and Cassandra

similar to OpenTSDB but allows for Cassandra

OpenTSDB is a distributed, scalable Time Series Database (TSDB) written on top of HBase. OpenTSDB was written to address a common need: store, index and serve metrics collected from computer systems (network gear, operating systems, applications) at a large scale, and make this data easily accessible and graphable.

an open-source service monitoring system and time series database

system for collecting timestamped events and deriving metrics

Cloud-based sensor analytics

high performance interactive SQL access to all Hadoop data

ADABAS was NoSQL from a time when there was no SQL

Touted as SQL database with object structured storage

Shark is a large-scale data warehouse system for Spark designed to be compatible with Apache Hive. It can execute Hive QL queries up to 100 times faster than Hive without any modification to the existing data or queries. Shark supports Hive’s query language, metastore, serialization formats, and user-defined functions, providing seamless integration with existing Hive deployments and a familiar, more powerful option for new ones. Shark is built on top of Spark

Drill is the open source version of Google’s Dremel system which is available as an infrastructure service called Google BigQuery. In recent years open source systems have emerged to address the need for scalable batch processing (Apache Hadoop) and stream processing (Storm, Apache S4). Apache Hadoop, originally inspired by Google’s internal MapReduce system, is used by thousands of organizations processing large-scale datasets. Apache Hadoop is designed to achieve very high throughput, but is not designed to achieve the sub-second latency needed for interactive data analysis and exploration. Drill, inspired by Google’s internal Dremel system, is intended to address this need

HCatalog’s table abstraction presents users with a relational view of data in the Hadoop Distributed File System (HDFS) and ensures that users need not worry about where or in what format their data is stored. Right now HCatalog is part of Hive. Only old versions are separated for download.

Data Warehouse infrastructure developed by Facebook. Data summarization, query, and analysis. It’s provides SQL-like language (not SQL92 compliant): HiveQL.

framework that allows efficient translation of queries involving heterogeneous and federated data

Apache Phoenix is a SQL skin over HBase delivered as a client-embedded JDBC driver targeting low latency queries over HBase data. Apache Phoenix takes your SQL query, compiles it into a series of HBase scans, and orchestrates the running of those scans to produce regular JDBC result sets. The table metadata is stored in an HBase table and versioned, such that snapshot queries over prior versions will automatically use the correct schema. Direct use of the HBase API, along with coprocessors and custom filters, results in performance on the order of milliseconds for small queries, or seconds for tens of millions of rows.

massively parallel, approximate query engine

a fully enabled GPGPU database which allows for offloading of database operations to General Processing on Graphics Processor Units.

The Apache-licensed Impala project brings scalable parallel database technology to Hadoop, enabling users to issue low-latency SQL queries to data stored in HDFS and Apache HBase without requiring data movement or transformation. It’s a Google Dremel clone (Big Query google).

Open source project enabling fast and simple Big Data application development on Apache Hadoop. project that delivers ANSI-standard SQL technology to easily build new and integrate existing applications onto Hadoop

Splout allows serving an arbitrarily big dataset with high QPS rates and at the same time provides full SQL query syntax.

Distributed Analytics Engine from eBay Inc. that provides SQL interface and multi-dimensional analysis (OLAP) on Hadoop supporting extremely large datasets

Facebook has open sourced Presto, a SQL engine it says is on average 10 times faster than Hive for running queries across large data sets stored in Hadoop and elsewhere.

a native implementation of SQL for the Apache Hadoop open-source project

Refer to lock-free architecture for SQL Server 2014

index-based SQL engine for Hadoop

data compute engine

SQL-like data warehouse system for Hadoop

database for storing petabyte-scale volumes of structured and semi-structured data

Catalyst is a Query Optimization Framework for Spark and Shark

Manipulating Structured Data Using Spark

a full-featured SQL-on-Hadoop RDBMS with ACID transactions

interactive query for Hive

Tajo is a distributed data warehouse system on Hadoop that provides low-latency and scalable ad-hoc queries and ETL on large-data sets stored on HDFS and other data sources.

enterprise-class SQL-on-HBase solution targeting big data transactional or operational workloads

IDE for R.

Real-time processing of streaming data at massive scale

a petabyte-scale data transport solution that uses secure appliances to transfer large amounts of data into and out of AWS

scalable techniques for data cleaning and statistical inference on dirty data

a distributed logging service called BookKeeper and a distributed publish/subscribe system built on top of BookKeeper called Hedwig

Large scale log aggregator, and analytics.

Un-structured data agregator to HDFS.

Apache Samza is a distributed stream processing framework. It uses Apache Kafka for messaging, and Apache Hadoop YARN to provide fault tolerance, processor isolation, security, and resource management. Developed by http://www.linkedin.com/in/jaykreps Linkedin.

System for bulk data transfer between HDFS and structured datastores as RDBMS. Like Flume but from HDFS to RDBMS.

Unstructured Information Management applications are software systems that analyze large volumes of unstructured information in order to discover knowledge that is relevant to an end user

framework that help ETL to Solr, HBase and HDFS.

Log agregator in real-time. It’s a Apache Thrift Service.

tool to collect events and logs

geographically distributed system for joining multiple continuously flowing streams of data in real-time with high scalability and low latency

open source stream processing software system.

This project is a framework for connecting disparate data sources with the Apache Hadoop system, making them interoperable. HIHO connects Hadoop with multiple RDBMS and file systems, so that data can be loaded to Hadoop and unloaded from Hadoop

Kafka to HDFS pipeline. It is a mapreduce job that does distributed data loads out of Kafka

stream of change capture events for a database

a framework for Solving Big Data Ingestion Problem

utility package for compressing sorted integer arrays

bridge from OLTP to OLAP for use it on Hadoop

log aggregator and dashboard

a tool for managing events and logs.

a Inter Process Communication (remote procedure calls) library with built in software load balancers. The primary usage model involves REST calls with various serialization scheme support

Suro has its roots in Apache Chukwa, which was initially adopted by Netflix. Is a log agregattor like Storm, Samza.

is a service implementing Kafka log persistance

Automatic structure for your text-formatted data

extensive log processing and reporting features

Apache Flume with steroids

standards-based messaging middleware

distributed tracing system that helps us gather timing data for all the disparate services at Twitter

streaming data collection for real-time Big Data analytics

open source messaging and Integration Patterns server

fast, reliable, scalable, fully managed queue service

Distributed publish-subscribe system for processing large amounts of streaming data. Kafka is a Message Queue developed by LinkedIn that persists messages to disk in a very performant manner. Because messages are persisted, it has the interesting ability for clients to rewind a stream and consume the messages again. Another upside of the disk persistence is that bulk importing the data into HDFS for offline analysis can be done very quickly and efficiently. Storm, developed by BackType (which was acquired by Twitter a year ago), is more about transforming a stream of messages into new streams.

messaging tools that speak AMQP and support many languages and platforms

an open-source, high-performance, lightweight cloud native messaging system

ActiveMQ’s next generation of messaging

a highly scalable publish-subscribe event ingestor

simple, fast work queue

realtime distributed message processing at scale

Distributed Task Queue

library for building scalable and high performance distributed applications

simple, lightweight message queue

a totally ordered queue of messaging updates with separate pointers into the queue indicating the last update sent to your Messenger app and the traditional storage tier

Job Server

reliable, many-to-many, asynchronous messaging hosted on Google’s infrastructure

reliable, many-to-many, asynchronous messaging hosted on Google’s infrastructure

open source project to build a multi-protocol, embeddable, very high performance, clustered, asynchronous messaging system

easy-to-use highly available message queuing service

distributed message queue system

queuing and notification service made by and for OpenStack, but not only for it

Robust messaging for applications

message queue which uses HTTP as transport, JSON to format a minimalist protocol and is organized as REST resources

simple Python library for queueing jobs and processing them in the background with workers

Simple, efficient background processing for Ruby

The Intelligent Transport Layer

Akka is an open-source toolkit and runtime simplifying the construction of concurrent applications on the Java platform.

Apache Avro is a framework for modeling, serializing and making Remote Procedure Calls (RPC). Avro data is described by a schema, and one interesting feature is that the schema is stored in the same file as the data it describes, so files are self-describing. Avro does not require code generation. This framework can compete with other similar tools like: Apache Thrift, Google Protocol Buffers, ZeroC ICE, and so on.

Curator is a set of Java libraries that make using Apache ZooKeeper much easier.

Apache Karaf is an OSGi runtime that runs on top of any OSGi framework and provides you a set of services, a powerful provisioning concept, an extensible shell & more.

A cross-language RPC framework for service creations. It’s the service base for Facebook technologies (the original Thrift contributor). Thrift provides a framework for developing and accessing remote services. It allows developers to create services that can be consumed by any application that is written in a language that there are Thrift bindings for. Thrift manages serialization of data to and from a service, as well as the protocol that describes a method invocation, response, etc. Instead of writing all the RPC code – you can just get straight to your service logic. Thrift uses TCP and so a given service is bound to a particular port.

It’s a coordination service that gives you the tools you need to write correct distributed applications. ZooKeeper was developed at Yahoo! Research. Several Hadoop projects are already using ZooKeeper to coordinate the cluster and provide highly-available distributed services. Perhaps most famous of those are Apache HBase, Storm, Kafka. ZooKeeper is an application library with two principal implementations of the APIs—Java and C—and a service component implemented in Java that runs on an ensemble of dedicated servers. Zookeeper is for building distributed systems, simplifies the development process, making it more agile and enabling more robust implementations. Back in 2006, Google published a paper on “Chubby”, a distributed lock service which gained wide adoption within their data centers. Zookeeper, not surprisingly, is a close clone of Chubby designed to fulfill many of the same roles for HDFS and other Hadoop infrastructure.

a lock service for loosely-coupled distributed systems

Norbert is a library that provides easy cluster management and workload distribution. With Norbert, you can quickly distribute a simple client/server architecture to create a highly scalable architecture capable of handling heavy traffic. Implemented in Scala, Norbert wraps ZooKeeper, Netty and uses Protocol Buffers for transport to make it easy to build a cluster aware application. A Java API is provided and pluggable load balancing strategies are supported with round robin and consistent hash strategies provided out of the box.

high performance and widely portable implementation of the Message Passing Interface (MPI) standard

message passing framework

decentralized solution for service discovery and orchestration

a Python package for building complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization, handling failures, command line integration, and much more.

Spring XD (Xtreme Data) is a evolution of Spring Java application development framework to help Big Data Applications by Pivotal. SpringSource was the company created by the founders of the Spring Framework. SpringSource was purchased by VMware where it was maintained for some time as a separate division within VMware. Later VMware, and its parent company EMC Corporation, formally created a joint venture called Pivotal. Spring XD is more than development framework library, is a distributed, and extensible system for data ingestion, real time analytics, batch processing, and data export. It could be considered as alternative to Apache Flume/Sqoop/Oozie in some scenarios. Spring XD is part of Pivotal Spring for Apache Hadoop (SHDP). SHDP, integrated with Spring, Spring Batch and Spring Data are part of the Spring IO Platform as foundational libraries. Building on top of, and extending this foundation, the Spring IO platform provides Spring XD as big data runtime. Spring for Apache Hadoop (SHDP) aims to help simplify the development of Hadoop based applications by providing a consistent configuration and API across a wide range of Hadoop ecosystem projects such as Pig, Hive, and Cascading in addition to providing extensions to Spring Batch for orchestrating Hadoop based workflows.

Elephant Bird is a project that provides utilities (libraries) for working with LZOP-compressed data. It also provides a container format that supports working with Protocol Buffers, Thrift in MapReduce, Writables, Pig LoadFuncs, Hive SerDe, HBase miscellanea. This open source library is massively used in Twitter.

Finagle is an asynchronous network stack for the JVM that you can use to build asynchronous Remote Procedure Call (RPC) clients and servers in Java, Scala, or any JVM-hosted language.

AirFlow is a system to programmatically author, schedule and monitor data pipelines

is a service scheduler that runs on top of Apache Mesos

Apache™ Falcon is a data management framework for simplifying data lifecycle management and processing pipelines on Apache Hadoop®. It enables users to configure, manage and orchestrate data motion, pipeline processing, disaster recovery, and data retention workflows. Instead of hard-coding complex data lifecycle capabilities, Hadoop applications can now rely on the well-tested Apache Falcon framework for these functions. Falcon’s simplification of data management is quite useful to anyone building apps on Hadoop. Data Management on Hadoop encompasses data motion, process orchestration, lifecycle management, data discovery, etc. among other concerns that are beyond ETL. Falcon is a new data processing and management platform for Hadoop that solves this problem and creates additional opportunities by building on existing components within the Hadoop ecosystem (ex. Apache Oozie, Apache Hadoop DistCp etc.) without reinventing the wheel.

Workflow scheduler system for MR jobs using DAGs (Direct Acyclical Graphs). Oozie Coordinator can trigger jobs by time (frequency) and data availabilit

distributed and fault-tolerant scheduler

Hadoop workflow management. A batch job scheduler can be seen as a combination of the cron and make Unix utilities combined with a friendly UI.

customizable platform for creating workflow managers

Sparrow is a high throughput, low latency, and fault-tolerant distributed cluster scheduler. Sparrow is designed for applications that require resource allocations frequently for very short jobs, such as analytics frameworks. Sparrow schedules from a distributed set of schedulers that maintain no shared state. Instead, to schedule a job, a scheduler obtains intantaneous load information by sending probes to a subset of worker machines. The scheduler places the job’s tasks on the least loaded of the probed workers. This technique allows Sparrow to schedule in milliseconds, two orders of magnitude faster than existing approaches. Sparrow also handles failures: if a scheduler fails, a client simply directs scheduling requests to an alternate scheduler

visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology

a general framework for parallelizing stochastic learning algorithms on multi-node clusters

a data management system for facilitating the next steps in real-world, large-scale analytics pipelines

Machine learning library and math library, on top of MapReduce.

tool for topological data analysis

Neural networks in JavaScript.

a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Cente

The Oryx open source project provides simple, real-time large-scale machine learning / predictive analytics infrastructure. It implements a few classes of algorithm commonly used in business applications: collaborative filtering / recommendation, classification / regression, and clustering.

Machine Learning for Cascading on Apache Hadoop through an API, and standards based PMML

Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser.

GPU-accelerated library of primitives for deep neural networks

Flexible and Extensible Machine Learning in Ruby.

OpenCL library to train deep convolutional neural networks

text classification with machine learning

Conjecture is a framework for building machine learning models in Hadoop using the Scalding DSL. The goal of this project is to enable the development of statistical models as viable components in a wide range of product settings. Applications include classification and categorization, recommender systems, ranking, filtering, and regression (predicting real-valued numbers). Conjecture has been designed with a primary emphasis on flexibility and can handle a wide variety of inputs. Integration with Hadoop and scalding enable seamless handling of extremely large data volumes, and integration with established ETL processes. Predicted labels can either be consumed directly by the web stack using the dataset loader, or models can be deployed and consumed by live web code. Currently, binary classification (assigning one of two possible labels to input data points) is the most mature component of the Conjecture package.

a deep learning-based text understanding engine that can understand with near-human accuracy the textual content of several thousands posts per second, spanning more than 20 languages

provides innovative functionality, like automatic generation of UI experiences from pipeline definitions and automatic parallelization of Python code using futures

Deep Learning CUDA Extensions from Facebook AI Research

software framework that can utilize computing clusters with thousands of machines to train large models

System for Large Scale Machine Learning at Google

an Open Source Software Library for Machine Intelligence

statistical, machine learning and math runtime for Hadoop

cognitive computing system

Simplifying robust end-to-end machine learning on Apache Spark

contains a collection of library/tools for advanced feature engineering to derive features on top of other features, or convert a light weighted model into a feature

ADMM based large scale logistic regression

is built on the machine learning capabilities already available in several Microsoft products including Xbox and Bing and using predefined templates and workflows

Computational Network Toolkit

distributed machine learning libraries for the BDAS stack

Fast multilayer perceptron neural network library for iOS and Mac OS X.

a highly configurable deep learning framework

Numenta Platform for Intelligent Computing: a brain-inspired machine intelligence platform, and biologically accurate neural network based on cortical learning algorithms.

a toolkit for developing and comparing reinforcement learning algorithms

machine learning server buit on Hadoop, Mahout and Cascading

scikit-learn: machine learning in Python.

an open source predictive analytics platform based upon Spark, Kafka and Hadoop

a Spark implementation of some common machine learning (ML) functionality

combine H2OÕs Machine Learning capabilities with the power of the Spark platform

Distributed Neural Networks for Spark

Python package for deep learning that can utilize NVIDIA’s CUDA toolkit to run on the GPU

Large-scale analysis of neural data

Machine learning and natural language processing with Apache Pig

a system for serving machine learning predictions

global platform that enables developers to plug into and create an intelligent, conversational interface to anything

learning system sponsored by Microsoft and Yahoo!

Weka (Waikato Environment for Knowledge Analysis) is a popular suite of machine learning software written in Java, developed at the University of Waikato, New Zealand. Weka is free software available under the GNU General Public License.

Natural Language for the Internet of Things

computational knowledge engine

platform for deploying, managing, and scaling predictive models in production applications

There are two main JAR files in Apache Hadoop for benchmarking. This JAR are micro-benchmarks for testing particular parts of the infrastructure, for instance TestDFSIO analyzes the disk system, TeraSort evaluates MapReduce tasks, WordCount measures cluster performance, etc. Micro-Benchmarks are packaged in the tests and exmaples JAR files, and you can get a list of them, with descriptions, by invoking the JAR file with no arguments. With regards Apache Hadoop 2.2.0 stable version we have available the following JAR files for test, examples and benchmarking. The Hadoop micro-benchmarks, are bundled in this JAR files: hadoop-mapreduce-examples-2.2.0.jar, hadoop-mapreduce-client-jobclient-2.2.0-tests.jar.

The SWIM benchmark (Statistical Workload Injector for MapReduce), is a benchmark representing a real-world big data workload developed by University of California at Berkley in close cooperation with Facebook. This test provides rigorous measurements of the performance of MapReduce systems comprised of real industry workloads.

Big Bench Workload Development

some benchmarking queries for Apache Hive

Testbench for experimenting with Apache Hive at any data scale.

HiBench is a Hadoop benchmark suite.

Mesos task load simulator framework for (cluster and Mesos) performance analysis

performance focused Big Data tool

Benchmark suite which represents a broad range of MapReduce applications exhibiting application characteristics with high/low computation and high/low shuffle volumes. There are a total of 13 benchmarks, out of which Tera-Sort, Word-Count, and Grep are from Hadoop distribution. The rest of the benchmarks were developed in-house and are currently not part of the Hadoop distribution. The three benchmarks from Hadoop distribution are also slightly modified to take number of reduce tasks as input from the user and generate final time completion statistics of jobs.

Hadoop cluster benchmarking from Yahoo engineer team.

System that provides a single point of secure access for Apache Hadoop clusters. The goal is to simplify Hadoop security for both users (i.e. who access the cluster data and execute jobs) and operators (i.e. who control access and manage the cluster). The Gateway runs as a server (or cluster of servers) that serve one or more Hadoop clusters.

framework to enable, monitor and manage comprehensive data security across the Hadoop platform (formerly called Apache Argus)

Sentry is the next step in enterprise-grade big data security and delivers fine-grained authorization to data stored in Apache Hadoop™. An independent security module that integrates with open source SQL query engines Apache Hive and Cloudera Impala, Sentry delivers advanced authorization controls to enable multi-user applications and cross-functional processes for enterprise data sets. Sentry was a Cloudera development.

Open Source Big Data Security Analytics

data protection framework

A big data cluster management tool that creates and manages clusters of different technologies.

Intuitive, easy-to-use Hadoop management web UI backed by its RESTful APIs. Apache Ambari was donated by Hortonworks team to the ASF. It’s a powerful and nice interface for Hadoop and other typical applications from the Hadoop ecosystem. Apache Ambari is under a heavy development, and it will incorporate new features in a near future. For example Ambari is able to deploy a complete Hadoop system from scratch, however is not possible use this GUI in a Hadoop system that is already running. The ability to provisioning the operating system could be a good addition, however probably is not in the roadmap..

Bigtop was originally developed and released as an open source packaging infrastructure by Cloudera. BigTop is used for some vendors to build their own distributions based on Apache Hadoop (CDH, Pivotal HD, Intel’s distribution), however Apache Bigtop does many more tasks, like continuous integration testing (with Jenkins, maven, …) and is useful for packaging (RPM and DEB), deployment with Puppet, and so on. Apache Bigtop could be considered as a community effort with a main focus: put all bits of the Hadoop ecosystem as a whole, rather than individual projects.

Apache Helix is a generic cluster management framework used for the automatic management of partitioned, replicated and distributed resources hosted on a cluster of nodes. Originally developed by Linkedin, now is in an incubator project at Apache. Helix is developed on top of Zookeeper for coordination tasks. .

Mesos is a cluster manager that provides resource sharing and isolation across cluster applications. Like HTCondor, SGE or Troque can do it. However Mesos is hadoop centred design

Slider is a YARN application to deploy existing distributed applications on YARN, monitor them and make them larger or smaller as desired -even while the cluster is running.

Apache Whirr is a set of libraries for running cloud services. It allows you to use simple commands to boot clusters of distributed systems for testing and experimentation. Apache Whirr makes booting clusters easy.

Apache Hadoop YARN is a sub-project of Hadoop at the Apache Software Foundation introduced in Hadoop 2.0 that separates the resource management and processing components. YARN was born of a need to enable a broader array of interaction patterns for data stored in HDFS beyond MapReduce. The YARN-based architecture of Hadoop 2.0 provides a more general processing platform that is not constrained to MapReduce.

brooklyn is a library that simplifies application deployment and management. For deployment, it is designed to tie in with other tools, giving single-click deploy and adding the concepts of manageable clusters and fabrics: Many common software entities available out-of-the-box. Integrates with Apache Whirr – and thereby Chef and Puppet – to deploy well-known services such as Hadoop and elasticsearch (or use POBS, plain-old-bash-scripts) Use PaaS’s such as OpenShift, alongside self-built clusters, for maximum flexibility

Buildoop is an open source project licensed under Apache License 2.0, based on Apache BigTop idea. Buildoop is a collaboration project that provides templates and tools to help you create custom Linux-based systems based on Hadoop ecosystem. The project is built from scrach using Groovy language, and is not based on a mixture of tools like BigTop does (Makefile, Gradle, Groovy, Maven), probably is easier to programming than BigTop, and the desing is focused in the basic ideas behind the buildroot Yocto Project. The project is in early stages of development right now.

a comprehensive data management platform with the flexibility and power to evolve with your business

Web application for interacting with Apache Hadoop.

collect operational metadata from your virtualized infrastructure, then correlate and analyze it to expose operational hazards and waste that pose a threat to your datacenter performance, efficiency and uptime

Mesos containerizer hooks for Docker

tool for provisioning, managing and monitoring Apache Hadoop

Visualizing Cascading Workflows at Etsy

the load balancer will concentrate workload to a server until it has at least a medium-level workload

multi datacenters replication system

scalable distributed monitoring system for high-performance computing systems such as clusters and Grids

Genie provides REST-ful APIs to run Hadoop, Hive and Pig jobs, and to manage multiple Hadoop resources and perform job submissions across them.

job scheduling and monitoring system

job scheduling and monitoring system

Hannibal is tool to help monitor and maintain HBase-Clusters that are configured for manual splitting.

HOYA is defined as “running HBase On YARN”. The Hoya tool is a Java tool, and is currently CLI driven. It takes in a cluster specification – in terms of the number of regionservers, the location of HBASE_HOME, the ZooKeeper quorum hosts, the configuration that the new HBase cluster instance should use and so on.

Jumbune is an open-source product built for analyzing Hadoop cluster and MapReduce jobs.

Marathon is a Mesos framework for long-running services. Given that you have Mesos running as the kernel for your datacenter, Marathon is the init or upstart daemon.

scripts/recipes/configs to spin up VPC-based infrastructure in AWS from scratch and deploy labs to it

a mesos framework designed for scaling YARN clusters on Mesos. Myriad can expand or shrink one or more YARN clusters in response to events as per configured rules and policies.

a suite of tools for keeping your cloud operating in top form

AWS Service registry for resilient mid-tier load balancing and failover

a latency and fault tolerance library designed to isolate points of access to remote systems, services and 3rd party libraries, stop cascading failure and enable resilience in complex distributed systems where failure is inevitable

tracing data center problems to root cause, predict capacity issues, identify emerging failures and highlight latent threats

install, manage and monitor all the technology stack related to the Stratio Platform

Infrastructure management for engineers

a tool for data center automation

a highly scalable, high performance container management service that supports Docker containers

cluster management tool from CoreOS

an open platform for developers and sysadmins to build, ship, and run distributed applications

native clustering for Docker

fast, isolated development environments using Docker

Run Docker containers on Google Cloud Platform, powered by Kubernetes

a Distributed, Highly Available, Datacenter-Aware Scheduler

open source implementation of container cluster management

Chaos testing tool for Docker

an alternative to the Docker runtime, designed for server environments with the most rigorous security and production requirements

Next-generation web analytics processing with Scala, Spark, and Parquet

Build Real-time Big Data Applications on Apache HBase.

Highly extensible and scalable open source web crawler software project. A search engine based on Lucene: A Web crawler is an Internet bot that systematically browses the World Wide Web, typically for the purpose of Web indexing. Web crawlers can copy all the pages they visit for later processing by a search engine that indexes the downloaded pages so that users can search them much more quickly.

OODT was originally developed at NASA Jet Propulsion Laboratory to support capturing, processing and sharing of data for NASA’s scientific archives

Toolkit detects and extracts metadata and structured text content from various documents using existing parser libraries.

Run, scale, share, and deploy models Ñ without any infrastructure.

BIRT is an open source Eclipse-based reporting system that integrates with your Java/Java EE application to produce compelling reports.

open source event analytics platform.

HIPI is a library for Hadoop’s MapReduce framework that provides an API for performing image processing tasks in a distributed computing environment.

Splunk analytics for Hadoop

The MADlib project leverages the data-processing capabilities of an RDBMS to analyze data. The aim of this project is the integration of statistical data analysis into databases. The MADlib project is self-described as the Big Data Machine Learning in SQL for Data Scientists. The MADlib software project began the following year as a collaboration between researchers at UC Berkeley and engineers and data scientists at EMC/Greenplum (now Pivotal)

PivotalR is a package that enables users of R, the most popular open source statistical programming language and environment to interact with the Pivotal (Greenplum) Database as well as Pivotal HD / HAWQ and the open-source database PostgreSQL for Big Data analytics. R is a programming language and data analysis software: you do data analysis in R by writing scripts and functions in the R programming language. R is a complete, interactive, object-oriented language: designed by statisticians, for statisticians. The language provides objects, operators and functions that make the process of exploring, modeling, and visualizing data a natural one.

auto-scaling Hadoop cluster, built-in data connectors.

Cloud Platform for Data Science and Big Data Analytics

enterprise-strength web and event analytics, powered by Hadoop, Kinesis, Redshift and Postgres.

R frontend for Spark

analyzer for machine-generated date

Talend is an open source software vendor that provides data integration, data management, enterprise application integration and big data software and solutions.

Hosted Search API that delivers instant and relevant results from the first keystroke

a search engine capable of querying massive amounts of structured data at incredible speeds

Search engine library

Search platform for Apache Lucene

Search and analytics engine based on Apache Lucene

Elasticsearch real-time search and analytics natively integrated with Hadoop. Supports Map/Reduce, Cascading, Apache Hive and Apache Pig.

Freemium robust web application for exploring, filtering, analyzing, searching and exporting massive datasets scraped from across the Web

social graph search platform

continuous indexing system

continuous indexing system

large search index

Haeinsa is linearly scalable multi-row, multi-table transaction library for HBase. Use Haeinsa if you need strong ACID semantics on your HBase cluster. Is based on Google Perlcoator concept.

implementation of Percolator, part of HBase

Secondary Index for HBase

distributed high performance massive data engine for enterprise/vertical search

quickly and easily search for any content stored in HBase

is a Faceted Search implementation written purely in Java, an extension to Apache Lucene.

Cleo is a flexible software library for enabling rapid development of partial, out-of-order and real-time typeahead search. It is suitable for data sets of varying sizes and types. Cleo has been used extensively to power LinkedIn typeahead search covering professional network connections, companies, groups, questions, skills and other site features.

search architecture at LinkedIn

Zoie is a realtime search/indexing system written in Java.

Sphinx lets you either batch index and search data stored in an SQL database, NoSQL storage, or just files quickly and easily — or index and search data on the fly, working with Sphinx pretty much as with a database server.

a MySQL-compatible, relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases

MySQL databases in Amazon’s cloud

Real-time Computing Engine Designed for Big Data

Drizzle is a re-designed version of the MySQL v6.0 codebase and is designed around a central concept of having a microkernel architecture. Features such as the query cache and authentication system are now plugins to the database, which follow the general theme of “pluggable storage engines” that were introduced in MySQL 5.1. It supports PAM, LDAP, and HTTP AUTH for authentication via plugins it ships. Via its plugin system it currently supports logging to files, syslog, and remote services such as RabbitMQ and Gearman. Drizzle is an ACID-compliant relational database that supports transactions via an MVCC design

a synchronous multi-master cluster for MySQL, Percona and MariaDB

MySQL databases in Google’s cloud

an open source framework for horizontally partitioning MySQL systems

enhanced, drop-in replacement for MySQL

MySQL implementation using NDB Cluster storage engine providing shared-nothing clustering and auto-sharding

enhanced, drop-in replacement for MySQL

High Performance Proxy for MySQL

a distributed SQL database inspired by the design of Google F1

TokuDB is a storage engine for MySQL and MariaDB that is specifically designed for high performance on write-intensive workloads. It achieves this via Fractal Tree indexing. TokuDB is a scalable, ACID and MVCC compliant storage engine. TokuDB is one of the technologies that enable Big Data in MySQL.

is a collaboration among engineers from several companies that face similar challenges in running MySQL at scale, and seek greater performance from a database technology tailored for their needs.

provides servers and tools which facilitate scaling of MySQL databases for large scale web services

hybrid of MapReduce and DBMS

high-performance data warehouse appliances

Scalable Open Source PostgreSQL-based Database Cluster

Open Source Recommendation Engine Built Entirely Inside PostgreSQL

open source MPP database system solely targeted at data warehousing and data mart applications

multi-peta-byte database / MPP derived by PostgreSQL

proxy to memcached servers

key/value cache for flash storage

a memcached protocol router for scaling memcached deployments

fork of Memcache

A fast, light-weight proxy for memcached and redis

key/value cache for flash storage

fork of Memcache

ACID-compliant DBMS developed by Pervasive Software, optimized for embedding in applications

a software library that provides a high-performance embedded database for key/value data

in-memory database combines exceptional performance, reliability and developer efficiency in a proven real-time embedded database engine

a cross-platform database engine

a powerful API to store and sync data in realtime

transactional key-value database

HanoiDB implements an indexed, key/value storage engine. The primary index is a log-structured merge tree (LSM-BTree) implemented using ‘doubling sizes’ persistent ordered sets of key/value pairs, similar is some regards to LevelDB. HanoiDB includes a visualizer which when used to watch a living database resembles the ‘Towers of Hanoi’ puzzle game, which inspired the name of this database.

a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values.

ultra-fast, ultra-compact key-value embedded data store developed by Symas

RocksDB is an embeddable persistent key-value store for fast storage. RocksDB can also be the foundation for a client-server database but our current focus is on embedded workloads.

a library of routines for managing a database

a in-process software library which implements a self-contained, serverless, zero-configuration, transactional NoSQL database engine

Java In-Memory OLAP cube stored in columns, with clearly decoupled pre/post processing

business intelligence and data science platform

Business Intelligence for Big Data

platform for streaming analytics and intelligent automated action

data analytics platform

business intelligence platform in the cloud

lean business intelligence platform to visualize and explore your data.

self-service business intelligence tool in the cloud

powerful business intelligence suite.

Palo Suite combines all core applications — OLAP Server, Palo Web, Palo ETL Server and Palo for Excel — into one comprehensive and customisable Business Intelligence platform. The platform is completely based on Open Source products representing a high-end Business Intelligence solution which is available entirely free of any license fees.

used for audit analytics, revenue assurance, fraud management, and customer experience management

provides RUM data processing, visualization, monitoring, and analyses data daily, hourly, or on a near real-time basis

GPU in-memory database, big data analysis and visualization platform

business intelligence software and platform.

software platforms for business intelligence, mobile intelligence, and network applications.

business intelligence platform.

business intelligence and analytics platform.

SpagoBI is an Open Source Business Intelligence suite, belonging to the free/open source SpagoWorld initiative, founded and supported by Engineering Group. It offers a large range of analytical functions, a highly functional semantic layer often absent in other open source platforms and projects, and a respectable set of advanced data visualization features including geospatial analytics

business intelligence platform

an Interactive Web interpreter to Apache Crossdata, Stratio Ingestion, Stratio Decision,Markdown, Apache Spark, Apache Spark-SQL and command Shell

business intelligence platform.

Big Data Analytics

Environment for Deep Analysis of Large Complex Data

open source data analysis environment on top of Hadoop.

Big Data Analytics

a web-based notebook that enables interactive data analytics

data analytics application for Hadoop combines self-service data integration, analytics and visualization

Python big data analysis framework for high performance at Hadoop-scale, with first-class integration with Impala

a distributed system that supports columnar indexes with the ability to add new types of indexes

a fully managed big data and advanced analytics suite that enables you to transform your data into intelligent action.

scalable Analytics-as-a-Service platform based on relational algebra

plugs directly into your databases and lets you run, save, and share analyses over billions of data rows in seconds

Pinterest’s data analytics engine

web application framework for R

real time monitoring

standalone tool to catalog all of your enterprise metadata

fully integrated Hadoop data management platform

self-service data discovery, curation, and governance

an API for distributed data computation. Scale with your data.

highly scalable analytic data warehousing system

data processing, warehousing and analytics

Data Warehousing and Analysis Needs, all in the Cloud

businesses access to an elastic petabyte-scale, data warehouse-as-a-service offering that can scale according to their needs

Microsoft’s internal BigData analysis platform

graph visualization library using web workers and jQuery.

D3-based reusable chart library

open-source or freemium hosting for geospatial databases with powerful front-end editing capabilities and a robust API

open source HTML5 Charts visualizations.

another open source HTML5 Charts visualization

avaScript library for exploring large multivariate datasets in the browser. Works well with dc.js and d3.js

JavaScript library for time series visualization.

open source software platform for visualizing complex networks and integrating these with any type of attribute data

javaScript library for manipulating documents.

Dimensional charting built to work natively with crossfilter rendered using d3.js. Excellent for connecting charts/additional metadata to hover events in D3

dynamic HTML5 visualization.

allows you to write SQL queries that return charts instead of tables.

open source real-time dashboard builder for IOT and other web mashups.

An award-winning open-source platform for visualizing and manipulating large graphs and network connections. It’s like Photoshop, but for graphs. Available for Windows and Mac OS X.

simple charting API.

open source, feature rich metrics dashboard and graph editor for Graphite, InfluxDB & OpenTSDB

running on GPUs and turns static designs into interactive tools using client/cloud GPU infrastructure and GPU-accelerated languages like Superconductor

scalable Realtime Graphing.

simple and flexible charting API.

provides a rich architecture for interactive computing

toolkit for visualizing the networks in your data

visualize logs and time-stamped data

plotting with Python.

visually explore data sets to find stories and extract insights

chart components for d3.js.

Progressive SVG bar, line and pie charts.

Easy-to-use web service that allows for rapid creation of complex charts, from heatmaps to histograms. Upload data to create and style charts with Plotly’s online spreadsheet. Fork others’ plots.

simple but powerful library for building data applications in pure Javascript and HTML.

open-source platform to query and visualize data.

JavaScript library dedicated to graph drawing.

aÊD3Êplugin for visualizing time series. Use Cubism to construct better realtime dashboards, pulling data fromÊGraphite,ÊCubeÊand other sources

dashboarding tool

a visualization grammar.

Platform for Internet of things

Making products smart

Rapid development and connection of intelligent systems