Zepl
Search…
Zepl in 4 Easy Steps
Getting started running on Zepl is fast and easy. This Test Flight guide will walk you through a complete example starting with joining an existing organization and ending with running your first sample notebook connected to data in S3. The sample notebook takes the common Titanic disaster dataset and demonstrates how to run python code, create visualizations, and connect to to a Zepl data source. We will also call out additional Zepl notebook features around versioning, code snippets, and sharing.

If you have any existing Jupyter (.ipynb) or Apache Zeppelin (.json, .zpln) notebooks from previous projects, these can be easily imported from your local file system or from a public URL, such as Github or S3.

  1. 1.
    From the Zepl home page, Select the "+ Add New" button > Import Notebook
  2. 2.
    Notebook title: This will be filled in by the imported notebook but can always be changed later
  3. 3.
    Space: Select "My Notebooks"
  4. 4.
    Type: Select "Fetch From URL"
  5. 5.
    Link to your notebook:https://bit.ly/getting-started-on-zepl

Zepl has one click integrations with the most popular data warehouses. Zepl data sources enable you to securely connect to the data warehouse of your choice without ever exposing your personal credentials and allows you to reuse these connections in any notebook. View all of our data source connections below:
Most data science projects also start with local data files. You can easily work with and share local data files by uploading those to your notebook. Check out our File Upload documentation here:
In this step, we will provide you with access to a Zepl hosted S3 bucket with pre-loaded sample data.

  1. 1.
    Inside the notebook from Step 2, click the Data Source icon
    on the right side of the screen
  2. 2.
    Select "Add new" > Amazon S3
  3. 3.
    Complete the Data Source form
    1. 1.
      Name: Titanic_Data
    2. 2.
      Description: "Getting started on Zepl"
    3. 3.
      Bucket Name: s3-datasource-tutorial
    4. 4.
      ​Credential Type: Select "Private"
    5. 5.
      Access Key ID: AKIAYC6CTAF4DUQGHGWH
    6. 6.
      Secret Access Key: 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
    7. 7.
      Session Token: Leave this blank
  4. 4.
    Select Add - *Refresh the screen if required
Data Sources are Reusable: Each user is able to add their own credentials to a data source, making it easy to maintain a library of secure data connections that everyone on the team can use. The data access control is always handled by the rules created by the data provider (i.e. Snowflake, S3, etc.).

Now that we have created and attached our S3 data source to the notebook, we will use Zepl's universal function to create the connection: z.getDatasource("<Data Source Name>").
  1. 1.
    Scroll down to the paragraph labeled "Connect to your S3 Bucket"
  2. 2.
    The code required to connect to our S3 bucket is already added for you: bucket = z.getDatasource("Titanic_Data") .
  3. 3.
    The value returned is a boto3 Bucket object (boto3.resources.factory.s3.Bucket), which has many functions for downloading and uploading files to S3. Please see our Amazon S3 reference below for more information.

Select the "Run" button in the top left corner to execute every paragraph or run each paragraph individually by select the small run icon in the right corner of each paragraph.

  1. 1.
    Select the "+ Add New" button > Invite New Members
  2. 2.
    First Name
  3. 3.
    Last Name
  4. 4.
    Email: This will be the user name and email address where that user will receive their invite with temporary account credentials
  5. 5.
    Security Policies: See our security policy documentation​

  1. 1.
    Navigate back to your notebook labeled Getting Started: ML on Titanic
  2. 2.
    In the notebook, Select Share
  3. 3.
    Enter the email of the new user you invited > Select Add
  4. 4.
    Set up Notebook Access Control: Documentation Here​
  5. 5.
    Select Save Changes

Notebook versions are created automatically when paragraphs are executed, code is modified, or results change. Versions can also be named for easier identification and to act as a tagging system. Naming a version can also be used to create saved versions manually. And, of course, it's possible to rollback the notebook to any of the previous versions.

Notebooks can be scheduled to run on a consistent time schedule. Select from the prepopulated options (1h, 3h, 6h, etc.) or enter a cron schedule for full customization.

Zepl provides quick and easy navigation within your notebook by listing the titles of your paragraphs, language used in the paragraph, and any markdown tags. This can be accessed within your notebook by selecting the list icon on the upper right hand menu bar.

Use comments to leave feedback or ask questions on specific paragraphs. Leaving a comment will automatically notify all users with whom the notebook is shared.

Files such as .csv, .pkl, or other model files can be easily uploaded to a notebook. This can be done by selecting the folder icon on the right hand menu bar. Note that uploaded files cannot exceed 100mb per notebook.

​
Copy link
On this page
Step 1: Import Notebook
Import from URL
Step 2: Connect to your Data
Create and attach an S3 Data Source
Use Python to connect to the S3 Data Source
Step 3: Run the Notebook!
Put it all together
Step 4: Invite your Team
Invite members
Share notebook with new user
Notebook Tips and Tricks