(gentle music)
- My name is Lisa Spory.
I'm the Vice President of
National Security and Immigration programs at Sevatec.
Sevatec is a leading national security firms,
specializing in agile, DevSecOps, data sciences,
cyber engineering, and cloud services.
And we support a number of mission critical applications,
across the Federal Government to include Homeland Security,
Department of Defense, Department of Transportation,
Department of State and other Federal Civilian Agencies.
So we are a firm that specializes in data analytics.
So we're always looking for new tools
and capabilities that we can bring to our customers
to help empower them with their data
and bring them into more modern tool sets.
(gentle music)
So we develop and maintain
a large Traditional Data Warehouse environment
for a Federal customer.
We broker data to about 30 different systems.
We ingest data from about 30 different systems,
and we support a variety of different types of users
to include traditional canned in ad hoc reporting users,
downstream systems and data science users.
What we found in a Traditional Data Warehousing environment
is that what we lacked was a unified platform
for people to be able to perform data science experiments.
As a result of that,
we had users coming through our traditional BI tools
and performing large data extractions,
taking that data back down to their desktop
and using a variety of different tools such as our studio,
to try to perform the data science experiments
that they were trying to do
and just in time Data Warehousing.
Their desktops didn't really have the computing power
they needed.
Meanwhile, our system was being heavily taxed
by very IO and types of queries.
The result of that was that it was impacting our ability
to meet RSLS on some of our interfaces
and really meet the demands of
the rest of our user community.
So we were really looking for a way that we could
enable the data science population
without taxing the system
and that's where we needed to look to new technologies.
- [Rakesh] Databricks is a very, very well
suited experimental tool,
for any of the data's operations
we are dealing with right now.
It allows us to actually experiment with the data,
it allows us to
incrementally change the organization culture.
It allows us to roll out new technology.
And if you look at the data scientist challenges
we've been having,
it is actually a unifying challenge we had.
We were looking for a unifying solution to put all of the
technology data and the democratization of data together
and Databricks gave us all those solutions.
When we started rolling out Databricks,
we are finding thrilling solutions,
we were finding thrilling results over there.
They were folks who were looking for data
and they were taking hours to get the data out
for their science experiment.
Now it's actually getting in minutes.
There were folks who are looking for statistical operation,
but there were sampling the data.
Now they don't have to sample the data.
These are the simple results
which we didn't even expect when we started with,
but Databricks definitely changed
the whole ball game for us.
We are right now using Delta to change the entire operation
so that we can increase the frequency of the data
it can be refreshing the link.
We are introducing machine learning.
We are introducing new R and Python packages to constantly
enable the data scientists community
to do the things they never actually thought of doing
in this kind of environment.
Before even Databricks,
everybody was getting their own EMR,
everybody was engineering their own solution.
With Databricks they don't have to,
did they get the secure,
easy access to the data
without worrying about engineering behind it.
Data has been held hostage through bureaucracy,
through security, through multiple different ways.
And Databricks allows us to liberate that data
without compromising security.
(gentle music)