Welcome to Amazon FinSpace
Amazon FinSpace is a data management and analytics service
For the financial services industry
FinSpace reduces the time it takes to discover
Transform, and manage access to data
What used to take months now takes minutes
By providing your analysts with self service access to their data
They will have more time to spend on analysis.
Let's dive in to how Amazon FinSpace works.
First we need to navigate to our unique application URL
And then log in to our FinSpace application
Note that the application doesn't require access to the AWS console, but instead
Maintains its own user database and permissions
This makes it easy to provide access to coworkers
Contractors, or your customers.
Authentication can be configured to allow FinSpace to provide login credentials.
Or, you can use single sign on with own identity provider via SAML.
Amazon FinSpace start with data.
Data is ingested from various data sources, either via an API
Or direct upload, and becomes a dataset in Amazon FinSpace
Each dataset is represented by one or more views in to the data.
A data view determines how the dataset is accessed and presented
We'll talk about views in more detail later in the video.
The data catalog helps you find the data you want.
Once you've found the right data
You can then open a view of the data in a Jupyter notebook for further analysis and manipulation.
On the FinSpace application home page
You can browse and search for data.
See recent data views you've created
And find helpful tutorials and other resources.
This environment is currently populated with capital markets sample data
Which is included for you to try out the service. Let's check it out.
Your datasets are sorted by attributes you select
Allowing you to browse for data that matches what you're looking for.
Let's dive in to the data tagged as an asset class.
Then we can keep drilling down
Find our equity offerings, and finally, common stocks.
You can see that there are several datasets available that match the criteria
However, rather then browsing
You can also use free form queries to search for the data you need.
For example, let's look for five minute bars.
Once you have the results you're looking for, you can further refine the list.
This takes us to the data summary. This is where you can see information about the data
As well as configure how it's accessed and processed.
For now, let's just take a look at where the data is analyzed once it's found.
So, we select "Analyze in Notebook"
A new browser tab open with a read only Jupyter notebook
That is pre-configured to help start your analysis.
We'll copy the notebook so that we can start using it.
There's code to connect the FinSpace cluster where the node will retrieve data.
As well as loading data specific the view we're interested int.
The data can then be added in to an Apache Spark dataframe
Which you can query with the Spark API or SQL commands.
From here you can run further analysis and manipulation.
Of course the real power of FinSpace is the self service ability to
Find, access, and process your own data.
Let's take a look at how to do that.
You would typically add or update data in the application
With an automated process using the AWS SDK.
However
You an also add data ad hoc by uploading standard CSV files.
Which will automatically be parsed in to a data field.
Via its schema.
You can also add other types of files like
PDF or text that can be cataloged alongside your parsable data.
Let's add a CSV file that contains history of the treasury spread.
Once we've selected the file, if needed
We can edit the schema of the incoming data.
Now that the data has been uploaded
A view of the data is automatically created.
Let's edit the dataset description to be more concise.
Next, we're going to set up the attributes
That tag the data for browsing in different categories.
Before we add the attributes
Let's get a better understanding of what attribute sets are and how they work.
The sample data comes with a preconfigured attribute set.
Let's take a look at it.
You can think of an attribute set as a schema of how data can be tagged.
The attribute set describes what attributes can be applied to the data
Not the values of the attributes themselves.
You can see that it contains several attribute types, like asset class,
Source, event type, etcetera.
Taking a quick look at the configuration
You can see that we can add or remove attributes,
Set them to be required or optional,
And, if the attribute will be used during search
At the bottom
You can see that you can select different attribute types that can be added.
The simplest is the data attribute.
Data can be a number, string, or Boolean
And can be set to any value that fits the type.
Categories allow you to define a hierarchy
Which you can use the drill down and find very specifically tagged views.
You saw this in action earlier
When we browsed to find views that include common stocks.
Controlled vocabularies allow you to create
A specific list of values that can be assigned to an attribute.
For example, a list of US states.
Now that we have a better understanding of attributes, let's assign some to our data view.
We'll add the sample data attribute set.
Once that's added, we can start assigning values to the attributes.
You may have noticed that we can create different views in to the same dataset.
New views allow you to determine how the view in to the data is updated,
Accessed, partitioned, and sorted.
Views can be static and disallow future changes
So they are a snapshot of a specific time.
This can provide a historical view that can be useful for backtesting.
Otherwise, views will automatically be updated as data is appended or corrected.
You can determine if a view is meant to be used in a notebook directly,
Or if it will be available through an API to any consumer.
You can partition out specific columns of the data.
That doesn't make much sense here, but for data with many columns, you can choose the partition
Which can improve performance for large datasets, depending on how you query the data.
You can also select if the data should be presented
In a sorted order or not.
As mentioned, you can append or replace data in a dataset.
Any view that is configured to auto update
Will return the up to date data when it's modified.
The update history allows you to
Quickly see when an how the data has been modified.
As you'll see later, this can also be included in a report
Which is useful for compliance and auditing.
The schema tab is useful as a reference
To see how the data will be structured when making queries.
The final tab, permissions, gives you fine grain control on who
Can access the dataset, as well as how they can access it.
Permissions groups allows you to assign different sets of people with different access levels.
You can see it's easy to add users.
Group permissions allow you to control what the group can do with the data.
This configuration is quite permissive, however that can be easily configured.
You can also see what data sets this group has access to
Which can help make decisions about what groups can access what.
We've already touched on some configuration of categories
But let's take a more complete look at what's configurable in the FinSpace application.
Here we can create and edit a controlled vocabulary
Let's make it "days of the week".
We create a new vocabulary
Name it, then add our data as fields.
Now, let's add the days of the week attribute to our sample data attribute set.
In permission settings, we can add or remove users,
And modify their permission groups.
It's easy to create a new user.
Let's invite Helen Wu as a regular user.
We can now e-mail Helen with the URL to our application
Along with temporary credentials.
We can immediately assign her to a permission group as well.
You can also add new groups, or edit existing group permissions.
Note that you have fine grained control over what data,
Features, and configuration a group is able to access.
This include allowing or limiting access to application operations,
Such as the setting of permissions or attribute creation.
API credentials are used to allow external software
access to your FinSpace API.
The audit report is a valuable tool for keeping track
Of activity in your FinSpace application.
Every change and access activity is logged and can be reported.
You can select any time period,
See the activity of specific users or specific datasets.
Once a report is complete
You can see the first twenty entries in the interface and download the report as a CSV file for further analysis.
Amazon FinSpace supports your organizations compliance
Requirements by allowing you specify and enforce your data access controls and maintaining audit logs.
Last, but not least,
Let's see how we create a FinSpace from the AWS console.
Simple create a new environment.
Select a key to encrypt data stored by the application.
And select an authentication method, either an e-mail and password
Provided by the application, or singe sign on with your own identity provider.
Once the environment is ready
Add at least one superuser to administrate the FinSpace.
Superusers are users with elevated administrative privileges
Using the FinSpace application, a superuser can create and manage both
User and superusers, and access all data stored in the application.
Once the superuser has been added
Just click the unique domain link to get started.
For more information and to get started with amazon FinSpace
Please visit our web site.