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We don’t create and operate data
centers just to proclaim we can
store lots of data. We own the
process of acquiring, formatting,
merging, safekeeping, and
analyzing the ow of information –
and enabling decisions based on it.
Choosing a solid-state drive (SSD)
as part of the data center
mix hinges on a new idea: high-
value data.
When viewing data solely as a
long-term commodity for storage,
enterprise hard disk drives (HDDs)
form a viable solution. They
continue to deliver value where the
important metrics are density and
cost per gigabyte. HDDs also oer
a high mean-time-between-failure
(MTBF) and longevity of storage.
Once the mechanics of platter
rotation and actuator arm speed
are set, an interface chosen and
capacity determined, there isn’t
much else left to chance.
For massive volumes of data in
infrequently accessed parts of the
“data lake,” HDDs are well suited.
Predictability and reliability of
mature HDD technology translates
to reduced risk. However, large
volumes of long life data are only
part of the equation.
The rise of personal, mobile, social
and cloud computing means
real-time response has become a
key determining factor. Customer
satisfaction relies on handling
incoming data and requests
quickly, accurately, and securely.
In analytics operating on data
streams, small performance
dierences can aect outcomes
signicantly.
Faster response brings tiered
IT architecture. Data no longer
resides solely at Tier 3, the storage
layer; instead, transactional data
spreads across the enterprise.
High-value data generally is taken
in from Tier 1 presentation and
aggregation layers, and created in
Tier 2 application layers. It may be
transient, lasting only while a user
session is in progress, a sensor is
active, or a particular condition
arises.
SSDs deliver blistering transactional
performance, shaming HDDs
in benchmarks measuring I/O
operations per second (IOPS).
Performance is not the only
consideration for processing high-
value data. IDC denes what they
call “target rich” data, which they
estimate will grow to about 11%
of the data lake within ve years,
against ve criteria:
1
Easy to access: is it connected, or
locked in particular subsystems?
Real-time: is it available for
decisions in a timely manner?
Footprint: does it aect a lot
of people, inside or outside the
organization?
Transformative: could it, analyzed
and actioned, change things?
Synergistic: is there more than
one of these dimensions at work?
Instead of nebulous depths of “big
data,” we now have a manageable
region of interest. High-value data
usually means fast access, but it
also means reliable access over any
conditions including trac variety,
congestion, integrity through
temporary loss of power and long-
term endurance. These parameters
suggest the necessity for a specic
SSD class – the data center SSD –
for operations in and around high-
value data.
Choosing the right SSD for an
application calls for several
pieces of insight. First, there is
understanding the dierence
between client and data center
models. Next is seeing how
the real-world use case shapes
up compared to synthetic
benchmarks. Finally, recognizing
the characteristics of ash memory
and other technology inside SSDs
reveals the attributes needed to
handle high-value data eectively.
INTRODUCTION: WHAT THE DATA LAKE REALLY LOOKS LIKE
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