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From: Benjamin Kim <bbuild11@gmail.com>
Date: Wed, 30 Aug 2017 14:57:24 +0000
Message-ID: <CAK=LtcSyXithmwHZ9_RBRAKM=D=tGgrCAgEs-xoUc8aR0UJjfg@mail.gmail.com>
Subject: DMP/CDP Profile Store
To: "user@kudu.apache.org" <user@kudu.apache.org>
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archived-at: Wed, 30 Aug 2017 14:57:40 -0000

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I was wondering has anyone worked on a DMP/CDP for storing user and
customer profiles in Kudu. Each user will have their base ID's aka identity
graph along with statistics based on their attributes along with tables for
these attributes grouped by category.

Please let me know what you think of my thoughts.

I was thinking of creating a base profile table to store the ID's and
statistics along with unchanging or rarely changing attributes, such as
name, that do not need to be tracked. Next, I would create tables to
categorize groups of attributes, such as user information, behaviors,
geolocation, devices, etc. These attribute tables would have columns for
each attribute and would track changes by only inserting data via a time
stamp column to know when it was entered. Essentially, I would follow the
type 2 slowly changing dimension operandi for data warehouses. For
attributes that expire, we will partition by a time range so that we can
drop off expired data. For attributes where we only need to latest one, we
would add an active column to easily flag and query them after inactivating
older versions.

Any comments or advice would be truly appreciated.

Cheers,
Ben

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<div dir=3D"auto">I was wondering has anyone worked on a DMP/CDP for storin=
g user and customer profiles in Kudu. Each user will have their base ID&#39=
;s aka identity graph along with statistics based on their attributes along=
 with tables for these attributes grouped by category.</div><div dir=3D"aut=
o"><br></div><div dir=3D"auto">Please let me know what you think of my thou=
ghts.</div><div dir=3D"auto"><br></div><div dir=3D"auto">I was thinking of =
creating a base profile table to store the ID&#39;s and statistics along wi=
th unchanging or rarely changing attributes, such as name, that do not need=
 to be tracked. Next, I would create tables to categorize groups of attribu=
tes, such as user information, behaviors, geolocation, devices, etc. These =
attribute tables would have columns for each attribute and would track chan=
ges by only inserting data via a time stamp column to know when it was ente=
red. Essentially, I would follow the type 2 slowly changing dimension opera=
ndi for data warehouses. For attributes that expire, we will partition by a=
 time range so that we can drop off expired data. For attributes where we o=
nly need to latest one, we would add an active column to easily flag and qu=
ery them after inactivating older versions.</div><div dir=3D"auto"><br></di=
v><div dir=3D"auto">Any comments or advice would be truly appreciated.</div=
><div dir=3D"auto"><br></div><div dir=3D"auto">Cheers,</div><div dir=3D"aut=
o">Ben</div>

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