tayagulf.blogg.se - Redshift tutorial

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Suppose, we had this SQL query: DROP TABLE IF EXISTS atomic.pageview_geo_summary ĬREATE TABLE atomic.pageview_geo_summary ASĭATE_TRUNC('day', derived_tstamp) AS report_date, Let’s imagine that we want to collect page views from a website, group visitors by country, count how many times particular page was viewed from each country, and then store the aggregated results for further analysis or visualization. Let’s get started with this tutorial, by setting out the event data modeling that we want to migrate to Spark. This figure compares the dataflows for the Redshift- and Spark-based approaches: This tutorial presents one possible solution, using Apache Spark, Dataflow Runner and a Snowplow Analytics SDK to perform event data modeling. An alternative approach with Apache Spark

Loading richly nested enriched event data into Redshift requires our shredding process, which is an costly operation in EMR, and leads to complex SQL JOINs in RedshiftĢ.

Snowflake), limiting options to tune the event data modeling performance

Redshift does not support elastically scaling compute independently of storage (unlike e.g.

Loading all enriched events into Redshift and then running event data modeling processes can put a significant load on the Redshift database.

Running data modeling processes in Amazon Redshift also comes with some challenges, particularly at very large event volumes:

SQL is impossible to parameterise – you quickly end up with a user-specific fork of any given data model.

SQL is difficult to modularise – leading to “copy-paste-itis”.

SQL is difficult to unit test - leading to plenty of “debugging in production”.

SQL is great for prototyping event data models, but it can be challenging to then put these models into production: Challenges of SQL event data modeling with RedshiftĮvent data modeling in Redshift with SQL has worked well for many Snowplow users, particularly at small to medium event volumes, but there have been certain challenges, some around the use of SQL in general, and some around the use of Redshift itself: 1.1 Challenges of SQL A good example of this is Snowplow’s own web data model.

#REDSHIFT TUTORIAL SERIES#

The Snowplow pipeline ingests enriched events into Redshift, and then a series of SQL scripts, perhaps orchestrated by SQL Runner, has performed the aggregations, storing the results in new tables. Typically Snowpow users have relied on Amazon Redshift for their event data modeling.

A key data processing stage in a Snowplow pipeline is event data modeling:Įvent data modeling is the process of using business logic to aggregate over event-level data to produce ‘modeled’ data that is simpler for querying.