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Enrich Open Measures Poal with Datastreamer Location Inference Enrichment
Top companies trust Datastreamer to integrate, enrich, join, and apply their web data needs.
About Open Measures Poal
Collect and extract comments, video metadata and user profiles on Poal.
About Datastreamer Location Inference Enrichment
Location Inference Enrichment work to infer the location of the author of a piece of text content, by assessment and predicting on a number of parameters in the data. Datastreamer Location Inference Enrichment model cover 65+ country and 8+ languages.
Quickly enrich Open Measures Poal with Datastreamer Location Inference Enrichment with a Datstreamer Pipeline.
Step 1
Start your Pipeline with Open Measures Poal
Web data serves as the foundational input for any data pipeline. Pipelines can be powered by diverse data sources, including datasets from our partner ecosystem, proprietary internal systems, or any externally accessible web data.
Step 2
Add Datastreamer Location Inference Enrichment to enrich
Want to do more with your web data? Datastreamer offers hundreds of ready-to-use operations—filter, join, enrich, search, and more—to help you process and transform data at speed.
Step 3
That's it! You have just connected Open Measures Poal and Datastreamer Location Inference Enrichment
With Datastreamer, web data integration is effortless. Add new capabilities to your Pipelines dynamically and solve the operational issues that used to complicate your process.