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From abu...@apache.org
Subject kudu git commit: [blog] Data Pipelines Simplified with Kudu
Date Tue, 11 Sep 2018 15:54:25 GMT
Repository: kudu
Updated Branches:
  refs/heads/gh-pages 1488e1788 -> 9f7058cc7

[blog] Data Pipelines Simplified with Kudu

Change-Id: I222d2462da86c3aad3fa9afd71f686faaa9aa025
Reviewed-on: http://gerrit.cloudera.org:8080/11417
Reviewed-by: Attila Bukor <abukor@apache.org>
Tested-by: Attila Bukor <abukor@apache.org>

Project: http://git-wip-us.apache.org/repos/asf/kudu/repo
Commit: http://git-wip-us.apache.org/repos/asf/kudu/commit/9f7058cc
Tree: http://git-wip-us.apache.org/repos/asf/kudu/tree/9f7058cc
Diff: http://git-wip-us.apache.org/repos/asf/kudu/diff/9f7058cc

Branch: refs/heads/gh-pages
Commit: 9f7058cc77b2c710589194ed53e2e7119cbc9cc9
Parents: 1488e17
Author: Jordan Birdsell <jtbirdsell@apache.org>
Authored: Mon Sep 10 21:27:34 2018 -0400
Committer: Attila Bukor <abukor@apache.org>
Committed: Tue Sep 11 15:44:35 2018 +0000

 ...2018-09-11-simplified-pipelines-with-kudu.md | 44 ++++++++++++++++++++
 1 file changed, 44 insertions(+)

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+layout: post
+title: Simplified Data Pipelines with Kudu
+author: Mac Noland
+I’ve been working with Hadoop now for over seven years and fortunately, or unfortunately,
have run
+across a lot of structured data use cases.  What we, at [phData](https://phdata.io/), have
found is
+that end users are typically comfortable with tabular data and prefer to access their data
in a
+structured manner using tables.
+When working on new structured data projects, the first question we always get from non-Hadoop
+followers is, _“how do I update or delete a record?”_  The second question we get is,
_“when adding
+records, why don’t they show up in Impala right away?”_  For those of us who have worked
with HDFS
+and Impala on HDFS for years, these are simple questions to answer, but hard ones to explain.
+The pre-Kudu years were filled with 100’s (or 1000’s) of self-join views (or materialization
+and compaction jobs, along with scheduled jobs to refresh Impala cache periodically so new
+show up.  And while doable, for 10,000’s of tables, this basically became a distraction
from solving
+real business problems.
+With the introduction of Kudu, mixing record level updates, deletes, and inserts, while supporting
+large scans, are now something we can sustainably manage at scale.  HBase is very good at
+level updates, deletes and inserts, but doesn’t scale well for analytic use cases that
often do full
+table scans. Moreover, for streaming use cases, changes are available in near real-time.
 End users,
+accustomed to having to _”wait”_ for their data, can now consume the data as it arrives
in their
+A common data ingest pattern where Kudu becomes necessary is change data capture (CDC). 
That is,
+capturing the inserts, updates, hard deletes, and streaming them into Kudu where they can
be applied
+immediately.  Pre-Kudu this pipeline was very tedious to implement.  Now with tools like
+[StreamSets](https://streamsets.com/), you can get up and running in a few hours.
+A second common workflow is near real-time analytics.  We’ve streamed data off mining trucks,
+oil wells, manufacturing lines, and needed to make that data available to end users immediately.
+longer do we need to batch up writes, flush to HDFS and then refresh cache in Impala.  As
+before, with Kudu, the data are available as soon as it lands.  This has been a significant

+enhancement for end users, who previously had to _”wait”_ for data.
+In summary, Kudu has made a tremendous impact in removing the operational distractions of
merging in
+changes, and refreshing the cache of downstream consumers.  This now allows data engineers
+and users to focus on solving business problems, rather than being bothered by the tediousness
+the backend.

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