Sometimes used as shorthand for the Anaconda Distribution, Anaconda, Inc. is the company behind Anaconda Distribution, conda, conda-build and Anaconda Enterprise.
A cloud package repository hosting service at https://www.anaconda.org. With a free account, you can publish packages you create to be used publicly.
Open source repository of hundreds of popular data science packages, along with the conda package and virtual environment manager for Windows, Linux, and MacOS. Conda makes it quick and easy to install, run, and upgrade complex data science and machine learning environments like scikit-learn, TensorFlow, and SciPy.
A software platform for developing, governing, and automating data science and AI pipelines from laptop to production. Enterprise enables collaboration between teams of thousands of data scientists running large-scale model deployments on high-performance production clusters.
A desktop Graphical User Interface (GUI) included in Anaconda Distribution that allows you to easily use and manage IDEs, conda packages, environments, channels, and notebooks without the need to use the Command Line Interface (CLI).
An encapsulation of your data science assets to make them easily
portable. Projects may include files, environment variables, runnable
commands, services, packages, channels, environment specifications,
scripts, and notebooks. Each project also includes an
anaconda-project.yml configuration file to automate setup, so you
can easily run and share it with others. You can create and configure
projects from the Enterprise web interface or command line interface.
A public repository of over 100,000 professionally built software packages maintained by Anaconda, Inc. Anyone can download packages from the Anaconda Repository. A subset of them are included in the Anaconda Distribution installer, and it can be mirrored—completely or partially—for use with Anaconda Enterprise.
A location in the repository where Anaconda Enterprise looks for packages. Enterprise Administrators and users can define channels, determine which packages are available in a channel, and restrict access to specific users or groups.
To make a set of local changes permanent by copying them to the remote server. Anaconda Enterprise checks to see if your work will conflict with any commits that your colleagues have made on the same project, so the files will not be overwritten unless you so choose to do so.
An open source package and environment manager that makes it quick and easy to install, run, and upgrade complex data science and machine learning environments like scikit-learn, TensorFlow, and SciPy. Thousands of Python and R packages can be installed with conda on Windows, MacOS X, Linux and IBM Power.
A tool used to build conda packages from recipes.
A superset of Python virtual environments, conda environments make it easy to create projects with different versions of Python and avoid issues related to dependencies and version requirements. A conda environment maintains its own files, directories, and paths so that you can work with specific versions of libraries and/or Python itself without affecting other Python projects.
A binary tarball file containing system-level libraries, Python and R modules, executable programs, or other components. Conda tracks dependencies between specific packages and platforms, making it simple to create operating system-specific environments using different combinations of packages.
Instructions used to tell conda-build how to build a package.
A deployed Anaconda project containing a Notebook, web app, dashboard or machine learning model (exposed via an API). When you deploy a project, Anaconda Enterprise builds a container with all the required dependencies and runtime components—the libraries on which the project depends in order to run—and launches it with the security and access permissions defined by the user. This allows you to easily run and share the application with others.
Interactive data application
Visualizations with sliders, drop-downs and other widgets that allow users to interact with them. Interactive data applications can drive new computations, update plots and connect to other programmatic functionality.
Interactive development environment (IDE)
A suite of software tools that combines everything a developer needs to write and test software. It typically includes a code editor, a compiler or interpreter, and a debugger that the developer accesses through a single Graphical User Interface (GUI). An IDE may be installed locally, or it may be included as part of one or more existing and compatible applications accessed through a web browser.
A popular open source IDE for building interactive Notebooks by the Jupyter Foundation.
An open source system for hosting multiple Jupyter Notebooks in a centralized location.
Jupyter Foundation’s successor IDE to Jupyter, with flexible building blocks for interactive and collaborative computing. For Jupyter Notebook users, the interface for JupyterLab is familiar and still contains the notebook, file browser, text editor, terminal, and outputs.
The default browser-based IDE available in Anaconda Enterprise. It combines the notebook, file browser, text editor, terminal and outputs.
JupyterLab and Jupyter Notebooks are web-based IDE applications that allow you to create and share documents that contain live code in R or Python, equations, visualizations, and explanatory text.
Software files and information about the software—such as its name, description, and specific version—bundled into a file that can be installed and managed by a package manager. Packages can be encapsulated into Anaconda projects for easy portability.
Contains all the base files and components to support a particular programming environment. For example, a Python Spark project template contains everything you need to write Python code that connects to Spark clusters. When creating a new project, you can select a template that contains a set of packages and their dependencies.
Any storage location from which software or software assets may be retrieved and installed on a local computer.
A common way to operationalize a machine learning model is through a REST API. A REST API is a web server endpoint, or callable URL, which provides results based on a query. REST APIs allow developers to create applications that incorporate machine learning and prediction, without having to write models themselves.
An open project, running in an editor or IDE.
A distributed SQL database and project of the Apache Foundation. While Spark has historically been tightly associated with Apache Hadoop and run on Hadoop clusters, recently the Spark project has sought to separate itself from Hadoop by releasing support for Spark on Kubernetes. The core data structure in Spark is the RDD (Resilient Distributed Dataset)—a collection of data types, distributed in redundant fashion across many systems. To improve performance, RDDs are cached in memory by default, but can also be written to disk for persistence. Spark Ignite is a project to offer Spark RDDs that can be shared in-memory across applications.