h2o = fast statistical, machine learning & math runtime for bigdata
Switch branches/tags
Nothing to show
Pull request Compare This branch is 15850 commits behind h2oai:master.
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Failed to load latest commit information.



H2O makes hadoop do math! H2O scales statistics, machine learning and math over BigData. H2O is extensible and users can build blocks using simple math legos in the core. H2O keeps familiar interfaces like R, Excel & JSON so that big data enthusiasts & & experts can explore, munge, model and score datasets using a range of simple to advanced algorithms. Data collection is easy. Decision making is hard. H2O makes it fast and easy to derive insights from your data through faster and better predictive modeling. H2O has a vision of online scoring and modeling in a single platform.

Product Vision for first cut:

H2O product H2O, the Analytics Engine will scale Classification and Regression.

  • RandomForest, Generalized Linear Modeling (GLM), logistic regression, k-Means, available over R / REST/ JSON-API
  • Basic Linear Algebra as building blocks for custom algorithms
  • High predictive power of the models
  • High speed and scale for modeling and scoring over BigData

Data Sources:

  • We read and write from/to HDFS, S3, NoSQL, SQL
  • We ingest data in CSV format from local and distributed filesystems (nfs)
  • A JDBC driver for SQL and DataAdapters for NoSQL datasources is in the roadmap. (v2)

Console provides Adhoc Data Analytics at scale via R-like Parser on BigData

  • Able to pass and evaluate R-like expressions, slicing and filters make this the most powerful web calculator on BigData


Primary users are Data analysts looking to wield a powerful tool for Data Modeling in the Real-Time. Microsoft Excel, R, SAS wielding Data Analysts and Statisticians. Hadoop users with data in HDFS will have a first class citizen for doing Math in Hadoop ecosystem. Java and Math engineers can extend core functionality by using and extending legos in a simple java that reads like math. See package hex. Extensibility can also come from writing R expressions that capture your domain.


We use the best execution framework for the algorithm at hand. For first cut parallel algorithms: Map Reduce over distributed fork/join framework brings fine grain parallelism to distributed algorithms. Our algorithms are cache oblivious and fit into the heterogeneous datacenter and laptops to bring best erformance. Distributed Arraylets & Data Partitioning to preserve locality. Move code, not data, not people.


One of our first powerful extension will be a small tool belt of stats and math legos for Fraud Detection. Dealing with Unbalanced Datasets is a key focus for this. Users will use JSON/REST-api via H2O.R through connects the Analytics Engine into R-IDE/RStudio.


We will breathe & sustain a vibrant community with the focus of taking software engineering approach to data science and empower everyone interested in data to be able to hack data using math and algorithms. join us on google groups: h2ostream https://groups.google.com/forum/?!forum/h2ostream


SriSatish Ambati
Cliff Click
Jan Vitek
Petr Maj
Tomas Nykodym
Kevin Normoyle
Matt Fowles
Cyprien Noel
Lauren Brems

Michal Malohlava


Scientific Advisory Council

Stephen Boyd
Rob Tibshirani
Trevor Hastie

Systems, Data, FileSystems and Hadoop

Doug Lea
Chris Pouliot
Dhruba Borthakur
Charles Zedlewski
Niall Dalton


Jishnu Bhattacharjee, Nexus Venture Partners
Anand Babu Periasamy
Anand Rajaraman