While kamu focuses primarily on the problem of data management, you often may want to do some basic data exploration before exporting data for further use in your data science projects. Just like gitk in git we decided to provide a few simple exploration tools for you to assess the state of data without leaving the comfort of one tool.

Tail Command

To quickly view a sample of last events in a dataset:

$ kamu tail ca.bccdc.covid19.case-details

Lineage Command

To display the lineage of a certain dataset in a browser:

$ kamu inspect lineage ca.covid19.daily-cases -b
kamu sql

SQL Console

kamu provides a simple way to run ad-hoc queries and explore data using SQL language.

kamu sql

Following comand will drop you into the SQL shell:

$ kamu sql

SQL console by default uses the Apache Spark engine.

All datasets in your workspace should be available to you as tables:

kamu> show tables;

You can use describe to inspect the dataset’s schema:

kamu> describe `us.cityofnewyork.data.zipcode-boundaries`;
The extra back ticks needed to treat the dataset ID containing dots as a table name.

For brevity you can create aliases:

kamu> create temp view zipcodes as (select * from `us.cityofnewyork.data.zipcode-boundaries`);

And of course you can run queries against any dataset:

0: kamu> select po_name, sum(population) from zipcodes group by po_name;

Use Ctrl+D to exit the SQL shell.

SQL is a widely supported language, so kamu can be used in conjuction with many other tools that support it, such as Tableau and Power BI. Use following command to expose kamu data through the JDBC server:

$ kamu sql server

The kamu sql is a very powerful command that you can use both interactively or for scripting. We encourage you to explore more of its options through kamu sql --help.

Jupyter Notebooks

kamu also connects the power of Apache Spark with the Jupyter Notebook server. You can get started by running:

$ kamu notebook
You can use -e ENV_VAR option to pass additional environment variable into the notebook server. This can be very useful for different access and security tokens needed by different visualization APIs.

Executing this should open your default browser with a Jupyter running in it.

From here create a PySpark notebook. We start all notebooks by loading kamu extension:

%load_ext kamu

After this the import_dataset command becomes available and we can load the dataset and alias it by doing:

%import_dataset us.cityofnewyork.data.zipcode-boundaries --alias zipcodes
kamu notebook 001

This will take a few seconds as in the background it creates Apache Spark session, and it is Spark that loads the dataset into what it calls a “dataframe”.

You can then start using the zipcodes dataframe in the exact same way you would in an interactive spark-shell.

There few very important things to understand here:

  • Spark and Jupyter are running in separate processes
  • The commands you execute in the notebook are executed “remotely” and the results are transferred back
  • This means that it doesn’t really matter if your data is located on your machine or somewhere else - the notebook will work the same

The dataframe is automatically exposed in the SQL engine too, and you can run SQL queries using %%sql annotation:

kamu notebook 002

Thanks to the sparkmagic library you also get some simple instant visualizations for results of your queries.

kamu notebook 003

After you are done joining, filtering, and shaping the data you can choose to get it out of the Spark into the Jupyter notebook kernel using %%sql -o alias command

kamu notebook 004

Now that you have the data in Jupyter - you can use any of your favorite tools and libraries to further process it or visualize it.