Hi, everyone. I'm Sam Jewel. I work at Grafana and this is about Aggrafana. We're trying to combine
observability data with business data, trying to blend them together. We want to answer
questions like how much lost revenue was there due to downtime, what's the latency impact
on black Friday sales, how customers impacted by that recent incident. You might have
run a service, maybe, and seen some error logs, and you can imagine, you can see that customers
are affected. You can see some ideas for customers, but you don't know, are those customers
paying you millions or are they just on the free tier and completely inactive? You don't
know how important this is, whether to focus energy on this, and you need to blend in that
revenue data to find out. That should be easy, right? What you want to do, in theory, you just
want to join on a customer's table, and you want to select their names, their revenue, and
so surely their solutions to do this. You'll look around, you'll find their solutions, you know,
some logs back in, can speak SQL, some metrics back in, can speak SQL, and their solutions
where you can put everything into a single SQL database, it all speaks SQL, and so you should be
able to write a single query and join everything together and get your answer, right? And so
your analytics, business analysts, and your engineers, your SREs, should be able to use this
and you're away, fine, great. In reality, it's not that simple. For a few reasons, so your
analysts probably are not writing SQL. They might be using Metabase, Superset, Tableau,
Looker, they're creating queries using this abstraction layer, allows them to query with
dimensions and measures, and the tool generates the SQL. And that is a problem for us in two ways.
The first is that the things that have the most value, the things they're producing that
have the most value are their reports, their dashboards, their queries, and those no longer live
particularly close to the SQL database, they live within that tool, within Looker, within Metabase,
and that's a problem because now your engineers, your analytics, your SRE, can't access
those reports and those dashboards, and they can't access the queries that hold the meaning
and the value around like how a monthly active user or annual recurring revenue has even been
defined. So the other problem is, like tools like Metabase and Looker and Tableau don't even
play nicely with log data and metrics data, it's just such a different shape that you can't
make it kind of speak these dimensions and measures. So these guys are upset too because they
can't access the log data in the metric data that they're trying to join in. And exactly
the same thing is true from the other side. You'll see that SREs are not writing SQL either.
They are using a UI UX in Data Dog in Grafana and Kibana, and those tools in turn are not
speaking SQL either, often. Elastic can speak SQL, but when Kibana speaks to Elastic,
for log data, it's using this query DSL because log data and metric data is a different shape,
and there's a different access pattern, it's a different domain. And their queries and their
reports and their dashboards live within Data Dog, within Kibana, within Grafana, where the
analysts can't reach it. So we've got these divided worlds, they can't reach each other's
queries and results, you know. So at Grafana we've fixed this. We've taken a new approach.
We are connecting to Kib and I'll jump forward. And so it means you're no longer having to write
SQL, you can write these dimensions and measure queries and you can click to you're just clicking
on data to filter the whole dashboard and you keep clicking and filtering. This is what you get by
using a semantic layer. And suddenly we're all working side-by-side in the same single pane of glass.
And learn the whole, you've got your logs, query your analytics query and you join them together
with a SQL engine that we've built into Grafana. And that simple SQL query at the very end just
joins the data together and you can see your customers. And there you go. We've released that
this week. For first time and we'd love for you to try it out and I'd love to hear your feedback.
So thank you very much.