The Blaze ecosystem is a set of libraries that help users store, describe, query and process data. It is composed of the following core projects:

  • Blaze: An interface to query data on different storage systems
  • Dask: Parallel computing through task scheduling and blocked algorithms
  • Datashape: A data description language
  • DyND: A C++ library for dynamic, multidimensional arrays
  • Libndtypes: A C/C++ library for a low-level version of Datashape
  • Ndtypes-python: Python bindings for libndtypes
  • Odo: Data migration between different storage systems

Recent blog posts

Analyzing 1.7 Billion Reddit Comments with Blaze and Impala
by Daniel Rodriguez and Kristopher Overholt

Blaze is a Python library and interface to query data on different storage systems. Blaze works by translating a subset of modified NumPy and Pandas-like syntax to databases and other computing systems. Blaze gives Python users a familiar interface to query data living in other data storage systems such as SQL databases, NoSQL data stores, Spark, Hive, Impala, and raw data files such as CSV, JSON, and HDF5. Hive

Analyzing Reddit Comments with Dask and Castra
by Jim Crist

The scientific Python ecosystem is great for doing data analysis. Packages like NumPy and Pandas provide an excellent interface to doing complicated computations on datasets. With only a few lines of code one can load some data into a Pandas DataFrame, run some analysis, and generate a plot of the results. However, this workflow starts to falter when working with data that's larger than the RAM on your computer. At this point people often move their workflow from a Python based one into some other larger system like Spark or Hadoop. These are great at what they do, but for small problems are a bit overkill

Talks and Tutorials