[Music]
[Applause]
I'm Eric Wasserman I'm senior architect
at jam city we make mobile games in
mobile games we take in data that's
extremely large coming in hundreds of
thousands of records per second and so
some of our most interesting uses of
data are really around learning how art
players engage with our games and how to
make our games better prior to using
data bricks we were using a traditional
data warehouse type architecture which
had a number of difficulties for us
number one it didn't scale well it's
very difficult to add capacity the other
problem was just that's 24 hours sort of
ETL cycle that we would have to endure
made it so that we were kind of running
the business by looking in the rearview
mirror instead of you know looking at
that was happening right now the data
science part of our business before data
bricks was it was a lot of individual
tests so they had to be run over periods
of time trying to avoid overlapping each
other it was very difficult to to to
automate the testing process so it was
it was much slower cycle you know sort
of 24 hours was at was a minimum beat
for something to change we wanted to put
our investment of our time into building
our analytics and our machine learning
models and not into building spark
clusters and managing those so data
bricks was very attractive from the
perspective that it afforded us the
ability to to do that what we didn't
really appreciate was exactly how much
more we would get out of using data
bricks as far as data science goes it's
really enabled us to do a lot more
actual model building and deployment of
models and experiments because not so
much of the time is spent
with the plumbing parts right it's it's
really been a liberating experience as
far as using Delta goes that has been a
data engineering relief essentially
there's a bunch of hard problems that
you don't really appreciate until you
face them and we had faced those
problems ourselves using spark and and
we're struggling to build systems
ourselves to to handle things like like
evolving schemas replaying of data that
that had errors in it optimizing the
representation of data in park' format
in order to get good query performance
and Delta basically does almost all of
that automatically so data bricks is the
heart of our data platform event type
data is coming in through our
proprietary collector system and then
fed into a Kafka pub/sub system and from
then it's all spark downstream of that
we load the data using structured
streaming ETL jobs store it in Delta
tables and then enrich those Delta
tables with down stream streaming jobs
and batch jobs that actually construct a
data warehouse within Delta itself so we
we have both both the raw data and the
enriched data in our data Lake and all
of that's available to all of our our
downstream uses the returns for moving
today to Brick's have been largely
centered around how many more people can
use the data it's definitely changed the
nature of how we approach virtually all
the aspects of our business to make them
more data-driven and once you start
using data to make decisions you start
making incremental gains you know ten
fifteen percent here and there across
the board is is not at all unusual the
latency on the data from when the data
is produced to
when we can make use of the information
that comes from that data has gone from
24 hours to a couple of minutes and even
less in some cases and that has a
monumental effect on decision making
because we've gone from a cycle of
asking a question going through a
bottleneck of gatekeepers who will try
to answer that question through a
process that may take days to being able
to answer the the question within
minutes
with data bricks were able to put all of
our data and the power to get value from
it into the hands of all of our
employees and that's a game changer for
us
[Music]