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https://issues.apache.org/jira/browse/BEAM-10983?focusedWorklogId=502760&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-502760
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ASF GitHub Bot logged work on BEAM-10983:
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Author: ASF GitHub Bot
Created on: 20/Oct/20 16:13
Start Date: 20/Oct/20 16:13
Worklog Time Spent: 10m
Work Description: bradmiro commented on pull request #12963:
URL: https://github.com/apache/beam/pull/12963#issuecomment-712964924
Generally, Spark RDDs are used for unstructured data, whereas Spark
Dataframes are used for structured data as it can be supplied a schema. The
transformations in this case tend to be more efficient. Does Beam have any sort
of similar split, or would you want to use PCollections in both cases?
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Issue Time Tracking
-------------------
Worklog Id: (was: 502760)
Time Spent: 1h 50m (was: 1h 40m)
> Have a getting started for Spark users
> --------------------------------------
>
> Key: BEAM-10983
> URL: https://issues.apache.org/jira/browse/BEAM-10983
> Project: Beam
> Issue Type: New Feature
> Components: website
> Reporter: David Cavazos
> Assignee: David Cavazos
> Priority: P2
> Time Spent: 1h 50m
> Remaining Estimate: 0h
>
> Have a friendlier getting started experience for users who already know Spark.
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