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Back To The Future of Real-Time Applications
Real-time systems have a long history. A new economic tipping point means it's now time to go back to the future...
For the last few decades, operations and analytics have been firmly separated in enterprise architectures, with different systems for the different needs.
This idea is far from new -- for example, here's the text from a Univac computer ad in 1956:
"...there's only one commercially available platform capable of real time performance... It's the ideal system for...simulation and on-line data reduction.
It solves complex problems from purely sensed data at speeds that are compatible with real-time control.
Because of its ability to reduce large volumes of data at tremendous speeds, the...system easily handles even the most difficult problems.
Furthermore, it offers many other outstanding characteristics, including: superb operating efficiency, large capacity, great versatility, the ability to interface with a wide variety of different types of data, and far greater reliability than any other computer of its type...."
Just like some of today's "internet of things" systems, the Univac scientific machine was designed to process information in-memory, directly from sensors (it could also access information from magnetic tape or punch cards).
Note how perfectly the ad ticks all the modern buzzword boxes, promising the 3Vs of big data (volume, variety, and velocity), along with powerful analytics and data compression.
Unfortunately for business in the 1950s, rising amounts of data and cheaper disk storage quickly upended the economics of computing -- and we have had to deal with the complexity and latency of separate operational and analytic systems ever since.
We're now seeing a new tipping point. The falling price of memory and the increased costs of complexity mean that in-memory platforms like SAP HANA are once again the simplest and cheapest way of running businesses.
The result is that although moving to HTAP entails "upheaval in the established architectures" according to Gartner, we're actually getting back to the fundamentals of what we've always wanted.
Release
2019.2
of
Tableau
represents
an
overhaul
of
the
company's
underlying
mapping
technology.
Location
and
geospatial
data
is
increasingly
being
combined
with
analytics
for
...
A
combination
of
Microsoft
and
Google
technology,
cobbled
together
with
human
expert
assistance,
delivers
an
NLP
solution
for
BI.
Can
it
compete
with
the
likes
of
Tableau,
Microsoft
...
Expected
to
be
included
as
part
of
the
upcoming
amendment
to
the
country's
data
protection
law,
the
new
guidelines
state
businesses
must
take
no
more
than
30
days
to
investigate
a
suspected
...
DataStax
announces
its
Constellation
product,
to
include
Cassandra
as
a
Service
and
DataStax
Insights,
will
release
in
Q4.
Elastic
deployment;
consumption-based
pricing;
and
...
The
organisation
is
taking
what
it
has
learned
in
the
motorsport
industry
from
capturing
data
off
fast-moving
vehicles
and
applying
it
to
the
automotive,
public
transport,
and
health
...
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