In both cases we will need to create a schema in order to read in the data which are formatted in Snowplow’s Enriched Event format, which is covered in the schema setup step below, which has its requirements outlined in the prerequisites step.įinally, you can follow either one or both of the Athena or Redshift Spectrum steps to achieve that goal. a new version of the website being release, and how that affects performance). That context contains performance information about page loading and the hypothetical analyst would like to obtain that data as a table, perhaps in order to correlate performance with something else (e.g. Specifically, we will the context with schema iglu:org.w3/PerformanceTiming/jsonschema/1-0-0. In order to develop these examples we will use a hypothetical scenario, in which we want to analyze Snowplow enriched events by extracting a certain context from the contexts field. If that is what you are trying to do you may want to also want to look into triggers for the Glue script, which will enable you to keep the parquet copy up to date. Parquet is an efficient, columnar, Hadoop file format which is ideally suited to that use case. The optional parquet step is there in case you are planning to use the archive often, in which case performance would be important, so a way to create a copy of the archive in parquet is shown. In the case of the resource constrained Redshift Cluster, the data owner may elect to run the query on Athena. In all those scenarios one or both of the following examples may be useful.įor instance to get back deleted data from S3, one may use the Redshift Spectrum example to query the archive and even insert the query result into a new table. Yet another motivation, may be to run a very large query on all history without affecting a resource constrained Redshift cluster.Another possibility is that the user would like to get a special subset of the data onto S3 and make it available for a specific use.if the data owner is dropping all but the last month of data to save space). One common reason is when the data that is needed is no longer on Redshift because the data owner only keeps the most recent data in Redshift (e.g.Why analyze Snowplow enriched events in S3?Īnalyzing the Snowplow data on S3 may be useful in a number of scenarios, for instance: Use AWS Redshift Spectrum to access the dataġ.Optionally format shift to Parquet using Glue.Creating the source table in Glue Data Catalog.Why analyze Snowplow enriched events in S3?.This guide consists of the following sections: The objective is to open new possibilities in using Snowplow event data via AWS Glue, and how to use the schemas created in AWS Athena and/or AWS Redshift Spectrum. This is a guide to interacting with Snowplow enriched events in Amazon S3 with AWS Glue.
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