# Get some (big) data[¶](https://knowledge.dataiku.com/latest/courses/dss-and-aws/dss-and-redshift/get-big-data.html#get-some-big-data "Permalink to this headline")

In this example, we are going to use Github data. Specifically, we’ll use data from the GitHub Archive, which is a project to record the public GitHub timeline, archive it, and make it easily accessible for further analysis. These data are very interesting since they provide very detailed and granular events, letting us looking for interesting insights from open source software projects.

We have access here to 6 months of Github activity history (from January to June 2015), stored on our server as JSON files split by hour (one hour == one JSON file).

As usual, the first step is to create a DSS Dataset on top of these files:

Please note that the selected format is “One record per line”, since we do not want to flatten the JSON file yet due to its very rich nature.

Also, we define a partitioning scheme, allowing us to efficiently work on daily subsets of data:

You can now save the input dataset (named “github” here):

Note that you see here the raw JSON content, with one event per line, stored in a fairly large 30Gb dataset (compressed) with 90 millions records for this 6 months period. Also, the column has automatically be named “line”.
