Connect Data from Sizmek to Looker
You can get your Sizmek 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 Sizmek and then analyze it in Looker. Our Sizmek to Looker connector allows you to access your Sizmek 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 Sizmek with Looker to turn your Sizmek data into actionable insights.
Sizmek is an ad management platform for advertisers and agencies focused on digital advertising campaigns. The platform engages audiences across any screen on a global scale.
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.
Sizmek API 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 Sizmek API enables clients and partners to integrate with the Sizmek advertising platform and data in their own independently built applications. The API provides programmatic access and control to advertising accounts so partners can create campaigns, manage and traffic ads, and retrieve analytics and reporting data.
Read more about how to set up your Sizmek API account here: https://developers.sizmek.com/hc/en-us/articles/360029611991-Introduction-to-the-Sizmek-API
Sizmek REST Reporting API enables developers to use and integrate the Report Builder Analytics services in their applications. As requests for large reports can take a while to generate, the Sizmek API uses an asynchronous technique for requesting reports. With this technique, you send an initial request that specifies the data you want in the report.
Once the report has been generated it will be delivered to you by selected delivery option (email, FTP, or URL).
There are two types of reports:
- Ad hoc: Defined on a specific date range of data, the report is executed when you save it. After executing the report, the execute response shows the metadata for the report that includes the Execution ID. You use the Execution ID to ping the report to establish if it has failed or completed so that you can either report the reason for the failure or retrieve the completed report by URL, email, or FTP.
- Scheduled: Defined on a specific date range of data, the report is activated when you save it. When creating this type of report, you define a schedule specifying when you want the report run, its frequency and the period of time that you want the the report to run.
On completion of each scheduled report it is delivered by email or to an FTP.Use the auto-generate tool in the Sizmek Advertising Suite to simulate a JSON report request, and then copy the report request into the code application, and create your report request. The procedure for using the auto-generate tool for both an ad hoc and scheduled report is the same except that for a scheduled report, you must create the schedule when you want the report to run.
Read more about this here: https://developers.sizmek.com/hc/en-us/articles/206688003-Report-Service-API-Reporting-API-Documentation
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, Sizmek’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 Sizmek 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 Sizmek 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.