Contents x
    Visualization Integrations
    • 25 May 2021
    • 2 Minutes to read
    • Contributors
    • Print
    • Dark
      Light
    • PDF
    Contents

    Visualization Integrations

    • Updated on 25 May 2021
    • 2 Minutes to read
    • Contributors
    • Print
    • Dark
      Light
    • PDF
    Article Summary

    With Tableau, Power BI, Google Analytics, etc., there are many options for where to put your data. Below, we review a few of the possible ways you can integrate your data into visualizations.

    Uploadoing files

    The most straight-forward method for incorporating your data with visualization software is to load the file directly into the visualization software. This provides no live connection and requires the most effort with regards to updating the data, but can be a direct and easy way to take your data and create visualizations of it to help you make better decisions.

    S3 -> Athena -> ODBC

    If you prefer to have your data stored on Amazon's S3 and have datasets and fields that are consistent, this could be a great option for you to view your data. It does require that you have the ODBC drivers installed on the machine you will be building the visualizations on. Below are the pros/cons of using this method for your visualizations:

    ProsCons
    - Great for data already stored in Amazon S3.
    - Great for data that has consistently the same fields.
    - Through Athena, you can write SQL queries on your data and create new tables.    		-Does not work with live data (the data has to be rerun with the visualization software in order to update.
    -Does not play nice with Tableau.
    -Tedious to set up.
    -Requires data consistently have the same fields and be the same formats.

    In order to fully grasp how to set up for this method of publication, we recommend you visit their public sites and follow their instructions

    The Mozenda API

    A popular method for accessing live data is through the API. You can learn more about how to use the API here. Furthermore, you can use Mozenda's Postman repository to use various method to access the data. In our August 2020 webinar, we went over using a python script to access Mozenda data. You can find that webinar here and the script used in said webinar here.

    Python Script

    If you choose to use the script from this webinar, we recommend using it as a starting point to create your own code. We do not offer support for this code specifically, so we only recommend it for users familiar with Python. Alternatively, you can access the API using other coding languages and get those calls from Mozenda's Postman folder.

    ProsCons
    - Great for smaller datasets that you don't store in any particular database or data you keep on Mozenda.
    - Great for data that potentially changes over time.
    - You can apply basic data transformations in Python.    		- Does not work with live data (the data has to be rerun with the visualization software in order to update.
    - Doesn't store history, only what's kept on Mozenda.
    - Does not play nice with Tableau.

    Alternative methods with the API include pulling the data regularly through directly into your database or datastore of choice. Then you can connect that database or datastore to the visualization software. This allows you to actually keep history and is the best method of usage with regards to connecting Mozenda data to visualizations

    NOTE

    The REST API is available to our Enterprise and Professional-level customers.

    Other

    We are always happy to help you achieve your data goals. Feel free to reach out to us if you need help identifying the best method for you to connect with your data.

    Was this article helpful?
    What's Next