[MUSIC PLAYING]
SPEAKER: Thanks for taking a look at Google
for your data analytics needs.
This quick video will demonstrate what organizations
can achieve when they tap into Google Data Analytics'
limitless capabilities across all data, workloads, and users.
Lisa is a data analyst at Cymbal Direct--
a fashion retailer that manages its own logistics
across multiple regional distribution centers.
The company recently experienced a spike
in fleet management costs due to an increase
in gas consumption and various other inefficiencies.
To get to the bottom of this, Lisa
knows that she will need to quickly and securely combine
internal data and external data, analyze data
across multiple clouds, and most importantly, work with data
without experiencing limits across size, scale, and format.
Finally, Lisa will need to tap into machine learning
to model future trends and present her analysis in a way
that business users can understand and act on quickly.
What's great about Google's Data Analytics
is that it provides a fully interoperable and limitless
data ecosystem that allows Lisa to innovate and solve problems
faster.
For instance, Google's Data Analytics
comes with free, embedded, and real-time business intelligence
capabilities that let Lisa quickly get
an overview of her business.
She notices an unusual spike in recent delivery times
and distances, which is resulting in an increase in gas
usage across the fleet.
Let's dig into this a bit more.
BigQuery is the serverless analytics powerhouse
at the core of the limitless data analytics
and is built with elasticity, flexibility, security,
and reliability in mind.
Lisa can analyze data of any size or type
without a lot of operational overhead.
And she doesn't have to worry about running out
of capacity because the analytics platform scales
to meet her needs.
Lisa can query across clouds, and within integrated data
sharing, she can easily and securely
access data from other teams and organizations
without having to make copies.
This is BigQuery UI.
On the left, you can see the data sets Lisa has access to.
She can start working with them immediately.
She doesn't have to set up a virtual warehouse, pick a size,
or do any of the administrative management of the warehouse.
That's because the Google Data Analytics
scales horizontally and vertically
to the needs of the organization and is fully managed
with free data ingestion and on-demand consumption-based
pricing.
Lisa only pays for the queries she runs.
Speaking of scale, whatever your data set size,
Google Data Analytics can handle it.
Lisa first, starts her investigation
with a data set that contains historical order data.
She wants to make sure there are no significant changes
in orders, even though, there have been some outliers
recently in delivery times.
This table contains over 1 petabyte of data,
a trillion rows of transactions.
It's a real world data set scrubbed in partition by day
and clustered by customer for performance.
Joining tables at scale is no problem for BigQuery.
Let's look at this query--
average daily sales joined against the product table.
A trillion rows, about 10 seconds.
No manual cluster resizing.
No waiting for a warehouse to be provisioned.
The order data looks good so Lisa
is going to dig further into delivery data
to see what's going on.
In her dashboard, Lisa saw a spike
in delivery times and mileage.
The query that backs the dashboard
compares delivery stats over the last hour
to the most recent five-day window.
Delivery data is ingested in real time
through built-in streaming support.
Lisa can process real-time data without having
to set up additional products, tools, or infrastructure.
That's not something offered with other cloud data
warehouses.
Google's limitless Data Analytics
is not limited to structured data.
Lisa can query against unstructured and
semi-structured data across the analytics platform
directly from within BigQuery.
For example, some of the product delivery data
is in JSON stored in Google Cloud Storage.
Within BigQuery, Lisa can explore the data directly.
She can even query against it, seeing results in tabular
or JSON format, as well.
Lisa's theory is that the delivery time spike
may be due to inventory issues at certain distribution
centers.
So next, she's going to look at incoming wholesale orders.
Even though, that data is managed by another team,
it's fast and easy for Lisa to get access to what she needs.
Integrated data sharing functionality
means Lisa can get access to internal data,
public data sets, commercial data
sets from Google Partners, and Google exclusive data sets,
such as Search Trends, all in one place.
It's a frictionless experience for analyzing data
across Google's open ecosystem.
The purchasing team has set up a private exchange
enabling anyone in the company to access
the data via a read-only linked data set.
This means, Lisa can access what she
needs without making copies or moving data around.
The purchasing team has published metadata
about the data set so Lisa knows, it's the right one,
and she can add it to her project to start querying it.
Now, Lisa returns to the SQL Workspace.
Her shared data set has already been added to our project
and is ready to use right away.
This data set has wholesale order data,
but she needs to know a little more
about one of these columns, and the source for that
is actually on AWS.
An external data source is set up here,
which lets Lisa use the analytics platform's
integrated cross-cloud analytics to query data in Amazon S3
all within the same SQL workspace.
Lisa can look up all distribution centers quickly.
She doesn't have to copy any data over
in order to query this data set in S3,
and she only pays egress on the results of the query,
not the full data set.
This means, full interoperability
across clouds with lower data egress costs
than with other data warehouses.
The single pane of glass to manage and analyze data
is great for organizations that use
AWS or Azure for the application stack,
but centralize their analytics on Google Cloud.
For example, application log data on AWS
can easily be queried from within Google's Data Analytics,
and cross-cloud data transfer makes
it simple to copy over results sets for anomaly detection
or other log analytics.
Lisa and her team can also directly ingest
data from Teradata or Redshift or even event-level data
from Google's Ads products.
Google's Data Analytics also integrates easily
with ecosystem data integration partners,
like Fivetran and Informatica.
Security is easy to manage across the Google Data
Analytics.
With fine-grained access controls at the table, row,
and column level and dynamic data masking,
Lisa only sees data necessary for her job.
For example, Lisa doesn't have the required access
to view all user information.
Cymbal Direct enforces data governance
through a centralized data catalog
and can ensure data security and privacy controls.
These policy tags are set in the main data catalog
and enforced in BigQuery, but Lisa can still
access aggregate information about the number of customers
in each city.
All of Google Data Analytics Advanced Security features
are offered at no additional charge for all customers
across the analytics platform.
Lisa now has all of her data sources identified
and looks at the inventory status by distribution center,
and it's clear there's a stock issue.
She contacts the purchasing team with her findings,
and they make adjustments to their ordering schedule,
but Lisa also doesn't want the increase in delivery times
to affect customer retention.
Using Google Data Analytics' integrated machine learning,
Lisa can generate predictive analytics
using familiar SQL syntax.
Lisa creates a churn prediction model
that predicts the set of customers
who are retention risks, which the customer
service and marketing teams can use
for outreach and targeted offers.
All of these insights are available
from predefined machine learning models
without having to copy data sets over to a separate exploration
tool.
It's all right here on the fly, integrated into the analytics
platform and accessible to any analyst who can write SQL.
If Lisa wants to share this model with her data science
team directly, she can do so easily using Vertex AI--
Google's native API Workbench, including integration
into Vertex MLOps framework.
She can also use any of Google's Ecosystem Partner Solutions
from C3 AI to DataIQ to DataRobot.
That way, Lisa's data science colleagues
will be able to use the notebooks they're
most familiar with.
They can even use PySpark directly from within the SQL
Workspace.
Google Cloud is committed to meeting users where they are
and making it easy for teams to get access
to the data they need when they need it using the tools most
familiar to them.
Speaking of tools people are most familiar with,
do your users like spreadsheets?
Well, remember the query Lisa ran earlier?
It's results set can, of course, be visualized
in Google's built-in business intelligence tool or partner
tools, like Tableau or Power BI, but it can also
be explored directly in Google Sheets in just a few clicks.
This is something Lisa's finance colleagues are bound to like.
No other data analytics can provide such a set
of limitless capabilities for all data, all workloads,
and all users.
We hope you have enjoyed this quick tour.
To get started today without a credit card,
get access to the Google BigQuery Sandbox
by clicking the link below.