Connect Data from MediaMath to Looker
You can get your MediaMath data into Looker to make more informed business decisions. This simple and quick guide gives you easy instructions on how you can extract your data from MediaMath and then analyze it in Looker. Our MediaMath to Looker connector allows you to access your MediaMath data so you can easily manage various media objects, view metadata, and comments, and get reliable insights and metrics, such as reach, impressions, follower-like ratio, and likes. Integrate your MediaMath with Looker to turn your MediaMath data into actionable insights.
MediaMath is a leading digital marketing technology company. It was founded in 2007 in New York. MediaMath was the first company that officially introduced the first DSP solution in the market. MediaMath is great as it combines media, audience, and intelligence in one efficient platform in order to help marketers execute effectively at scale. MediaMath’s T1 helps activate data, optimizes interactions, and automates execution across all addressable media. This helps deliver transparency, superior performance, and control to all marketers as well as better and more personalized experiences for consumers.
Looker is a popular analytics tool that helps bring data and business teams together by making it quick and easy for everyone to explore and comprehend the data which drives business. Looker Analytics can integrate with any SQL database and data warehouse, like Greenplum and Amazon Redshift.
From agile startups to large corporations, savvy companies can easily leverage Looker's amazing analysis capabilities to monitor the overall health of their businesses and also make more data-driven and sound decisions. Looker is great as it lets companies access, describe, control, explore, and visualize all their data across any web browser, on any device. Looker can help companies of all sizes use data to drive their business activities and decisions in the right direction.
Before loading your data into Looker, you will have to prep it first. If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them.
MediaMath's documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes. Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. In these cases you'll likely have to create additional tables to capture the unpredictable cardinality in each record.
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The great thing about MediaMath is that it provides an API called MediaMath API. You can retrieve data programmatically through this API. It is available to all users who use the platform. By leveraging Improvado, you can easily and quickly integrate, connect, and see all your MediaMath data flow into Looker.
Looker will connect to your company's data warehouse or database in order to perform its analyses. This is where the data you would like to analyze is stored and maintained.
Some of the popular and commonly used data warehouses are Google BigQuery, Amazon Redshift, and Snowflake. Looker's documentation is helpful in this regard as it offers instructions on how you can configure and connect your database or data warehouse.
Note that in a majority of cases, it simply involves creating and copying your access credentials, such as a password, username, and server information. Then you can easily move your data into your data warehouse from your various data sources for Looker to use. As soon as you connect your data warehouse to Looker, you may build constructs called explores, and each is a comprehensive SQL view that contains a certain set of data for more analysis. An example can be "customers" or "orders."
Keeping data up to date
If you've made it this far, congrats! You probably have a written a program or script to extract your data and move it into Looker.
Now it's time to think about how you will keep this data up-to-date by loading updated or new data. Of course, you can just replicate all your data every time you have updated your records, but that would be extremely manual and time-consuming.
Luckily there is a better way. The key is building your script so that it can sense incremental updates made to the data.
Thankfully, MediaMath’s API results include fields so that you can identify those records which are new since the last update you made (or since the most recent record you have copied). Once you have taken new data into consideration, you can easily set your script either as a continuous loop or cron job to pull down new data as soon as it appears.
And keep in mind that, as with most codes, once you write it, you will need to maintain it. In case MediaMath changes its API, or if the API sends a field containing a data type that your code does not recognize, you might have to change the script.
The Easiest And Fastest Way To Do It
If all this sounds a bit overwhelming, don't worry -- there is an easier way to get this done!
Thankfully, products like Improvado were developed to move data from MediaMath to Looker automatically. Using Improvado, you can easily combine the most crucial and relevant data from your ad campaign into a dashboard. You can then connect this data to Looker.