- [Narrator] Thank you for joining us today.
Today we will be talking about the Alation Data Catalog
and delivering Data Governance on the Delta Lake.
My name is Raja Perumal and I work in our partners practice
to make sure that we are delivering
customer value using technology partners.
We'll be splitting this discussion up into three main parts.
First, we'll be talking about the Alation Data Catalog.
What makes us unique
and why we are key to driving a data culture.
Then we will be talking about
Alations approach to Data Governance.
And finally we will wrap up by talking about
the unique capabilities of Alation and Databricks
and what we can deliver to customers.
If all goes right I should be in the chat right now,
able to answer your questions live.
So feel free to ask questions
and I will do my best to answer them.
Alation is founded on the principle
of creating a data culture for our customers.
Our customers are telling us that
increasingly they need to make data-driven decisions.
And it's not simple enough to have a data science team
or a data analyst team.
A data culture needs to be throughout the business
in order to drive effective decision making
that affects the bottom line.
In these days this driver is even more important.
The pandemic is accelerating the need
to make informed data-driven decisions.
So Alations' mission to empower a curious and rational world
is more crucial than ever today.
To do this we obviously are working
to instill a data culture within our customers
and we believe the key to doing that is three key pillars.
First is data search discovering.
When our customers have a question
they need to answer with data.
We allow them to find the best place
to answer that question.
Second is data literacy.
When our customers find that data
that they need to answer the question.
we provide them the tools to know how to use that data well.
And finally a topic that we'll be talking at length
in a few minutes is data governance.
We provide the tools for organizations
to give authority to their data
without hindering the business value
to get from data as an asset.
The Explosion of data and necessary changes in the workforce
due to COVID and other factors,
are making it difficult for our customers to realize
the return on value from their data as an asset.
And an evolving set of laws,
whether it's state level like CCPA or regional like GDPR,
there are implications for how our customers use that data
and what they can do with that data.
So we need to give tools to ensure that our customers
are driving value from their data.
And to do that we have created a data catalog
that answers four key questions.
How do you find information?
Can that information be used?
Should it be used and how should be used?
And we answer these four questions,
with five key insights in our data catalog.
First, is we provide data intelligence.
We provide intelligence on the data
using our automated and intelligent algorithms.
We ensure that right out of the box,
we will understand usage patterns and understand metadata
to guide or to drive our customers
to make intelligent decisions.
We provide a platform for our customers
to build a community around data.
Our customers can collaborate around,
how to use data and how to trust data.
We provide a accelerated onboarding experience
through guided navigation.
So when a customer comes to the catalog,
regardless of how many data sets they have,
they understand which is the right data set for them.
We provide active data governance.
We don't divorce governance from or policies from the data.
We put policies right with the data.
So customers know how to use the data.
And finally, we do this across the enterprise.
We provide broad, deep connectivity.
It's not just about connecting to a lot of data sources.
It's about connecting to key data sources
like databit very deeply.
So we understand how people are using the data
and what they're doing with that data.
There are many types of data catalogs out there
and Alation really focuses on building a data catalog
as a platform.
That means that we're not just focused
on a defensive data governance strategy,
which means like you're preventing people
from getting access to the data,
or we're not a add-on to a tool
that focuses on driving adoption simply of that one tool.
We are a platform that allows,
the community of data users within an enterprise
to both get access to the right data
and make sure they understand when not to use that data.
To build the platform.
We connect to a series of data sources,
key to which is Databricks
and your cloud data Lake infrastructure.
But also your on-prem data basis.
So you can understand what is happening
across your analytical environment.
And with this information,
we build a platform that provides a active metadata catalog
with a behavior analysis engine that tells you
what your users are doing with your data
across your platform.
And once you satisfy that use case,
you can layer on solutions like data search and discovery,
data privacy, cloud data migrations
and crucially data governance.
which we will now talk about.
Data governance now is crucial more than ever
because the rate of data that is being created is exploding
due to a variety of factors.
This is causing folks to be worried about
data breaches in their environment.
And then it is causing concerns among the C-suite
to make sure that they're responding to those changes.
Traditional approaches to data governance
however have been failing.
These traditional approaches typically again
focus on the defense,
focus on preventing people from getting access to data.
They tend to be done in isolation
and not with engagement of business stakeholders.
And these approaches are often created
separate from the actual consumption of data.
Altogether this creates a solution
that does not focus on people, does not focus on business
and will give the people who need to use data
incentives to get around these approaches
instead of actually delivering to true compliance.
So with Alation we take a different focus.
We look at each of these pillars
as core to delivering active data governance.
The first of which is it is about the people using the data.
It's about guiding them to use the right data.
It's about guiding them to make sure
they're using data effectively.
Supporting that is Operationalized Policy.
We want the data policy that is determined by our customers
to actually be in action,
actually be in use and be measurable.
We want this approach to be collaborative,
stewards and governance officers need to work with users
users need to work with stewards.
We want some level of automation,
so this is not a manual effort throughout the catalog.
And finally we need to react to changes
across the business as they grow.
We'll talk more about the product and the process,
the actual how this works in the mechanics in a few moments.
But around the active data governance,
this is our approach to our lightweight approach
to understand how to work with the business
to make sure that we are getting started
with an active data governance process.
Now, let's talk about Databricks.
Our approach to active data governance
incorporates a variety of personas across the business
to ensure that we're driving adoption
of our analytical investments in the Delta Lake.
This approach to governance
ensures that users can use data easily
because it's well curated and well governed
and also can use the data safely
knowing that they have policy concerns
already taken into account.
Here are some of the few capabilities
that we take into consideration when we talk about
when we talk about governance
that Alation helps scale and automate.
Shifting gears let's talk about
how this applies specifically to Alation and Databricks.
Working with our joint customers,
we just determined three key challenges
about using a machine learning platform like Databricks
on the cloud that illusion an outlook.
First, is moving to the cloud environment,
ensuring that you're getting complex projects off the ground
can require a lot of time,
a lot of resources
that quick moving data-driven organizations just don't have.
Second, once you are on to the Databricks platform
or when you're on a modern machine learning stack,
empowering your data scientists to find the right datasets
can be time-consuming.
And there data scientists need to be able to collaborate
with experts that really understand the data.
So they're not redoing analysis that's already been done.
Alation helps with each one of these capabilities.
First Alation will identify the key data sets
to move to a cloud environment.
So instead of starting with the big bang project,
which opens up for risk,
it opens up for issues with time.
Alation will help our customers identify
which data assets to move,
who are the user groups associated to those data assets
and communicate to those data users within our data catalog
to move those data assets in a timely fashion.
All the while we take advantage of database capability
to manage ETL insecurity
and safely move that data to the cloud.
Overall together we ensure
that we de-risk the move to the cloud
by focusing on key assets and user groups.
And we can accelerate the time
that it takes to do those processes.
Alations core Search and discovery capabilities
allow data scientists to find
the right sets of data to use for their analysis,
regardless of where it exists
across their enterprise analytical environment.
This allows the data scientist to take advantage
and create data pipelines across their environment
to make sure that data scientists again,
can run efficient experiments using the right data
regardless of where it is.
Finally, using the Alation data catalog,
data scientists and data analysts can get involved with,
get understand the context that has already happened
within the data sources.
Collaborate with other users of the data,
whether they're on the data science team or not.
So they can understand what analysis has already been done
and what they can reuse.
Once that work is done they can track these experiments,
publish models and continue to use Databricks
to perform their experiments,
focusing on the use cases
that has core value to the business.
This collaboration allows Databricks and relation customers
to ensure that they are making better models.
And these business insights
are shared across the environment.
With the release of,
or the announcement of Databricks SQL Analytics Service.
These core tenants apply to
more and more parts of the business.
Previously Alation and Databricks work together
to empower data scientists to find
the best data for their data science experiments.
But with Alation and SQL analytics service.
Now data analysts can take advantage
of the easy SQL endpoints
that are spun up with SQL analytics service
to query those same data sets in Delta lake
through the Alation SQL editor,
to ensure that our analysts can take advantage
of the same capabilities that the data scientists can.
Because Databricks makes it easy to speed up
SQL analytic endpoints.
Alation can use our database JDBC driver
to ensure that we are using the best data.
And using it in a very high performance way
when we're connecting with the SQL analytics server.
Thanks to the built-in concurrences,
we don't have to worry about database management.
We don't need someone to have a ton of knowledge and spark
to get started with the Alation SQL compose editor,
they can get started using this dataset very quickly.
This gives an idea to tie together
all of the different pieces of what we've talked about
up until this point.
For a full demo we can get into
each and every piece of this.
But what I would like to highlight is that within Alation,
we're going to tell you,
who are your top users in your Databricks environment?
Who should you go ask when you have a question
about this data and usage about this data?
How popular are the data sets in those columns?
Who are the stewards?
Who has the authority and the responsibility
to maintain those datasets?
If you would like to know more there's a lot to show
and I encourage you to reach out to me so I can talk to you.
Thank you very much.