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Foundry's data integration capabilities
span across data connection and engineering
to create a unified data asset, the ontology,
which you've heard of several times in this video.
You can use the ontology to build key workflows
and insights on top of it.
What we've seen in this series so far
is some of the core functionality
that Foundry has to offer.
But it's certainly not the exhaustive set.
Foundry also provides an easy interface
to configure other things that you might need along the way.
We've seen Foundry's data lineage tools several times
in this video series.
While it serves as a compass to help
us navigate our pipelines in a graphical interface,
it does a lot more than that.
Namely, it answers questions about provenance
at a glance, how a data set came to be at the state
that it's in.
It shows the relationship between data sets
and their ancestors, which were used to compute them.
On the fly, data lineage shows a preview of the data set,
its build history, so you can get a sense
of how fresh the data is.
We can also zoom in on the exact code
that transforms the data sets, as we saw earlier.
Where other similar tools end, data lineage
can also help you track downstream applications
of your data assets so you can get a bird's eye view of where
your data is being used.
These views are totally dynamic, as Foundry natively
keeps a record of all these relations.
In addition to building our pipelines,
we often need to orchestrate our pipeline builds.
Foundry lets us configure schedules
on our pipelines to ensure that they're
running at the right cadence.
These schedules can be configured using either time
or event based conditions.
For instance, we see here that this data set
is configured to run once an hour
at nine minutes past the hour.
Now we've set up the schedules, but what
happens if something goes wrong with the build?
To really make sure that our pipeline is robust,
we need to add some monitoring capabilities to ensure we're
aware if anything goes wrong.
Foundry lets us configure data health checks that run
every time a data set builds.
With data health checks, we can configure alerts
for build failures, build duration, data quality,
and many more things.
Data lineage can also reveal interesting metadata
at a glance about the data sets in our view.
We can color code data sets by things
like data freshness, project, type of transform,
and many more dimensions.
This becomes extremely useful when
we're trying to understand some fundamental properties
about our pipelines.
Perhaps one of the most interesting ones
is permissions.
If I color code data sets based on my access level to them,
you can see from the legend that the blue data sets are
all the ones I have access to.
This is, again, a dynamic view which
reflects the dynamic enforcement of security
throughout the platform.
Security is baked right into every resource in Foundry
as immutable metadata.
What we see here is that Jason doesn't have access
to most of this data.
This is a consequence of the fact
that permissions flow with data in Foundry,
meaning that when I lock down the source
data with certain policies, those policies propagate
all the way down to the objects and also into applications
and any third party access.
We can implement role based security policies here,
which sync with an organization's existing Active
Directory.
You can also layer on classification based
and purpose based access control as well.
With a plethora of scaffolding around the core data
integration tooling, Foundry helps
you secure the quality and integrity of your data
pipelines.
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To recap, we've explored the Foundry data integration
journey and have seen how we can go
from connecting our various data source systems
all the way to creating our ontological objects.
We've taken our raw data sets, cleaned, transformed,
and combined them to build semantic assets that
represent the digital twin of our organization.
Along the way, we've leveraged Foundry's build orchestration
system to ensure that this data is updating
at a regular cadence, set up health checks
to validate data quality, and applied access policies that
will ensure that only the relevant people have access
to this data.
Now with a running data pipeline backing ontology,
we can build any number of new applications
using the same data foundation with minimal effort.
Foundry abstracts the complexity of managing your data
infrastructure to a couple of clicks
and brings an unrivaled solution to do the most with the least
amount of resources.
Congratulations.
You've made it to the end of the video series.
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