- [Narrator] The Lakehouse is an open,
simple platform to store and manage all of your data
and support all of your analytics and AI use cases.
SQL Analytics allows data analysts and SQL users
to query all of the data in the Lakehouse
by offering scalable,
cost-effective clusters optimized for SQL workloads.
When combined with Delta Lake,
the open source storage layer for data lakes,
you get the reliability of ACID transactions
and query results with lightening-fast performance,
even with multiple concurrent users.
On top of that,
SQL Analytics also makes it easier to quickly visualize
and share insights with a new
and easy-to-use SQL query editor,
rich dashboards, and automatic alerts.
Or you can connect SQL Analytics endpoints
to a broad set of BI tools,
including Tableau and Power BI,
for reliable data and fast query performance.
SQL Analytics allows you to build a Lakehouse architecture
that provides data warehousing performance
at data lake economics for better price and performance
than traditional cloud data warehouses.
Let's look at an example.
Imagine that you and I are data analysts
at a large coffee company
and we want to determine which countries
to source beans from this week to get the best value.
To find some relevant data,
let's look through the data lake using the data browser.
Once we choose the coffee database,
we can see the tables it contains
and the schema of each one,
including the names and data types of each column.
This looks like the data we need.
Let's write a query to find the average rating
of coffee beans in each country,
using the interactive query editor.
SQL Analytics' live autocomplete offered suggestions
to help us find what we're searching for quickly,
and pressing the tab button accepts a suggestion.
A chart could probably help us understand
the information more easily, so let's visualize this data.
As you can see, we have lots of options,
funnels, Sankey charts, Sunbursts, and more,
but let's choose a map since our data is grouped by country.
Looks good.
We're going to want to keep an eye on this query,
so let's give it a name,
add some searchable tags,
and schedule it to run every 24 hours.
Perfect.
The rest of our data team should see this right away.
SQL Analytics makes it easy to add this chart to a dashboard
and share it directly with key stakeholders.
We can also use the access controls
to tailor permissions on a group-wide basis.
So now that we have some good ratings data,
we should also bring in some price data
to help our stakeholders understand which beans offer
the highest quality at the lowest price.
So right now,
it looks like Brazilian coffee offers us the best value,
but if that changes,
I'd like to know about it without having
to check the dashboard every day.
So with SQL Analytics,
we can set up value-based alerts
so that we'll get an email if the value per dollar dips
below the threshold that we define.
Now, just to switch gears a bit,
let's imagine that we're administrators
and let's walk through some of the controls we can use
to manage our Lakehouse.
We can manage SQL endpoints from the endpoints tab.
SQL endpoints are optimized for low-latency SQL workloads
and offer fast query performance under high concurrency.
They're easy to set up, too.
We can select the size of cluster we want,
using simple T-shirt sizing
and we can add multi-cluster load-balancing
if we know we're going to have a lot
of analysts querying the data at once,
so that the query performance doesn't suffer,
even under heavy load.
We can also manage the query history.
The query history allows us to view all the recent queries
that ran on this endpoint.
We can sort by query, and point, user, and time.
Once we've selected a query, we can view details,
including phases of execution and query performance.
This information is useful for debugging
and performance optimization,
as well as for audit and governance purposes.
So the SQL Analytics UI works great
for exploratory data analysis,
quick dashboarding,
and setting up alerts for you and your team.
But if you already use an existing BI application,
like Tableau, Looker, or Power BI,
you can still use SQL Analytics on the backend
as the scalable query engine that provides clean,
reliable data and fast query performance,
to power your applications.
SQL Analytics offers data analysts a fresh new way
to work and play with data and Databricks.
Try SQL Analytics on Databricks today.