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Updated README, project metadata, and consistent file headers
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# People who have agreed to one of the CLAs and can contribute patches. | ||
# The AUTHORS file lists the copyright holders; this file | ||
# lists people. | ||
# The AUTHORS file lists the copyright holders; this file lists individuals. | ||
# | ||
# https://developers.google.com/open-source/cla/individual | ||
# https://developers.google.com/open-source/cla/corporate | ||
# | ||
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Dinesh Kulkarni <[email protected]> | ||
Drew Bryant <[email protected]> | ||
Graham Wheeler <[email protected]> | ||
Nikhil Kothari <[email protected]> | ||
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Google Cloud DataLab Team | ||
- Nikhil Kothari <[email protected]> | ||
- Drew Bryant <[email protected]> | ||
- Dinesh Kulkarni <[email protected]> | ||
- Graham Wheeler <[email protected]> | ||
- Bradley Jiang <[email protected]> |
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# Google Cloud DataLab | ||
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Google Cloud DataLab brings interactive tools for big data scenarios on Google | ||
Cloud Platform. | ||
[Google Cloud DataLab](https://cloud.google.com/datalab) provides a productive, interactive, and | ||
integrated tool to explore, visualize, analyze and transform data, bringing together the power of | ||
python, SQL, and the [Google Cloud Platform](https://cloud.google.com) with services such as | ||
[BigQuery](https://cloud.google.com/bigquery) and [Storage](https://cloud.google.com/storage) | ||
to extract insights and harness the value of your data. | ||
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DataLab enables developers and data scientists to easily and efficiently | ||
explore, transform, analyze and visualize their data and develop, test and | ||
deploy data processing pipelines to run on the cloud. It caters to scenarios | ||
ranging from ad-hoc and exploratory data analysis to development. | ||
DataLab builds on the interactive notebooks, and the foundation of [Jupyter](http://jupyter.org) | ||
(formerly IPython) to enable developers, data scientists and data analysts to easily work with | ||
their data in exploratory scenarios and extends that metaphor to developing and deploying | ||
data processing pipelines. | ||
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In its current form, DataLab enables using services such as Google BigQuery, | ||
and using the combination of SQL, python (and libraries such as pandas and | ||
matplotlib). | ||
DataLab deeply integrates into Google Cloud Platform. It provides a secure environment for all the | ||
members of your cloud project to effortlessly access all data and resources available to the | ||
project, and manage and share notebooks within the project using the associated git repository. | ||
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You can see an example of the notebooks by browsing through the | ||
[samples and documentation](https://github.com/GoogleCloudPlatform/datalab/tree/master/content/datalab/notebooks), | ||
which are themselves written in the form of notebooks. | ||
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## Getting Started | ||
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DataLab is packaged as a docker container, and contains the DataLab experience, along with | ||
Jupyter/IPython, and a variety of python libraries such as numpy, pandas, scikit-learn and | ||
matplotlib, in a ready-to-use form. | ||
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The simplest way to start using DataLab is on Google Cloud Platform. Head over to the | ||
[Google Cloud DataLab](https://datalab.cloud.google.com) site to deploy your own instance. | ||
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You can also run the docker container locally, as described in the | ||
[wiki](https://github.com/googlecloudplatform/datalab/wiki/Getting-Started). | ||
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## Contacting Us | ||
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Please submit questions on using DataLab at | ||
[StackOverflow](http://stackoverflow.com/questions/tagged/google-cloud-datalab) using the tag | ||
`google-cloud-datalab`. | ||
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For any product issues, you can either submit issues here, or you can submit feedback using the | ||
feedback link within the product. | ||
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## Developing DataLab | ||
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### Contributing | ||
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Contributions are welcome! Please see our [roadmap](https://github.com/GoogleCloudPlatform/datalab/wiki/Roadmap) | ||
page. Please check the page on [contributing](https://github.com/GoogleCloudPlatform/datalab/wiki/Contributing) | ||
for more details. | ||
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You can always contribute even without code submissions by submitting issues and suggestions to | ||
help improve DataLab and building and sharing samples and being a member of the community. | ||
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### Building and Running | ||
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The [wiki](https://github.com/googlecloudplatform/datalab/wiki/Development-Environment) describes | ||
the process of setting up a local development environment, as well as the steps to build and run, | ||
and the developer workflow. | ||
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### Navigating the Repository | ||
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This is a quick description of the repository structure to help understand and | ||
discover the relevant pieces. | ||
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All source code corresponding to product functionality that is built exists | ||
within `/sources`. The following is a list of the individual components: | ||
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* `/sources/lib` - set of python libraries used to implement APIs to access Google | ||
Cloud Platform services, and implement the DataLab interactive experience. | ||
- api: Google Cloud Platform APIs (currently: BigQuery and Cloud Storage). | ||
- datalab: interactive notebook experience to plug into Jupyter and IPython. | ||
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* `/sources/web` - the DataLab web server. This is implemented in node.js and | ||
serves the DataLab front-end experience - both content and APIs, as well as backend | ||
infrastructure such as notebook source control. | ||
Some of the requests are proxied to the Jupyter notebook server, which manages notebooks and | ||
associated kernel sessions. | ||
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* `/sources/tools` - miscellaneous other supporting tools. | ||
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Source code builds into the /build directory, and the generated build outputs are | ||
consumed when building the DataLab docker container. | ||
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The build outputs are packaged in the form of a docker container. | ||
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* `/containers/datalab` - the only container for now. This is the container that is used as the | ||
DataLab AppEngine module. | ||
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This directory contains the definitions for the docker containers associated with the | ||
Google Cloud DataLab product. |
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This directory contains imports (such as TypeScript declarations), used for building DataLab | ||
component sources. |
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