Transform Raw Data with Parse.ly
The more you know about your audience, the better the content (and experience) you can create for them.
Audience insights platform Parse.ly just announced the availability of their Data Pipeline, a live stream of user- and even-level data that is structured for sophisticated analysis and real-time data engineering projects.
The real-time infrastructure builds upon Parse.ly’s dashboard and API offerings, which already collect and process billions of user events per month.
The Data Pipeline provides full access to historical raw data, as well as real-time data via streaming endpoints. The solution will likely prove appealing to developers looking to build in-house ETL (extract, transform, load) proccesses. The data formats integrate cleanly with open source data analysis stacks, like R, Python, Pandas, Hadoop, and Spark. The raw events are also ideal for imports into cloud SQL engines like Amazon Redshift or Google BigQuery.
Customers are able to use first-party data about their audience to gain deeper insights into valuable user segments, campaign tracking, device segmentation, and more. An organization’s existing Business Intelligence tools, such as Looker or Tableau, can easily plug in to this data to visualize and share custom analyses of every user action and event.
“Digital publishers and other web companies have long felt that point vendor solutions are holding their data hostage,” said Andrew Montalenti, co-founder and CTO at Parse.ly. “Parse.ly’s Data Pipeline unlocks this data, making it easy to build an in-house practice around analytics. This lets our customers focus on meaningful analysis, rather than the arduous task of building and managing data infrastructure.”