(upbeat music)
- So, name's Stephen Galsworthy.
I'm the head of data science at Quby,
we're the provider of a connected home platform
and the creators of a smart thermostat
and energy display called Toon.
So, some of the use cases we have for Databricks is,
we have a connected home platform
that's collecting a lot of data
from residential households.
What we're able to do is,
we use the Databricks platform to enable us
to create data driven services on top of that data.
So, a big challenge we face
from the data engineering perspective
was just the amount of time that it took
to manage our own infrastructure.
We could see from just working with our
on site Hadoop Cluster
that as soon as a new package came along
we'd have to be having a data engineer
spending a day or two making sure that
that was installed correctly
or the dependencies were managed well.
Moving to Databricks has allowed us
to take away some of that concentration
on infrastructure management,
and that means our data engineers can focus on
where they're really adding even more
specific value to the business.
From a data science perspective,
at that time before using Databricks
we were using a number of different tools,
our data scientist came from backgrounds
in MATLAB, Python...
So, what we use Databricks for at that stage
was where the (indistinct),
the collaboration allowed us to develop code
in a very systematic way,
and what we're also able to do
is very quickly switch between languages.
So, for data science some things work really well in Python,
you want to be able to use some of the latest developments
in terms of flow and caress,
sometimes you want to be switching more towards scalar
for production code.
So, one example of how Databricks
has provided us with a lot of value,
is we have a use case where we're trying to detect
anomalies in heating systems of homes.
Databricks Delta in combination with Spark Streaming
allows us to offer alerts to users
with a very limited latency,
so they are able to react to problems within the home
before it affects their comfort levels.
The real business impact of Databricks,
a couple of things.
One, it allows us to manage our whole data science stack
with a very slick team,
so we don't have to spend our time
really concentrating on the infrastructure,
we can put our emphasis into developing
the machine and the algorithms,
that's where we can really truly extract information
from the data we're collecting.
On real cost saving,
approach is just being able to collect data
and analyze it in a systematic way really helps.
One example is,
Databricks allows us to access spot instances very easily,
that allows us to change the timing
of when we're using clusters.
Auto scaling of clusters on top of that
allows us to process large volumes of data
in a way that can be very economic for us as a company.
So, the Databricks Unified Analytics Platform
has allowed us as a team of
composing of data engineers, data scientists,
but also the rest of the business
to allow us to create valuable services
from the data we're collecting.
So in a summary,
the Databricks Analytics Platform has allowed us to
transition from a hardware based business model
towards enabling both us and our partners
to become more service orientated
and ready for the future.
(upbeat music)