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>> Welcome, everyone, to
this CUBE Conversation featuring Imply.
I'm your host, Lisa Martin.
Today, we are excited to be joined by FJ Yang,
the co-founder and CEO of Imply.
FJ, thanks so much for joining us today.
>> Lisa, thank you so much for having me.
>> Tell me a little bit about yourself and about Imply.
>> Yeah, absolutely.
So, I started Imply a couple years ago
and before start the company, I was a technologist.
So, I was a software engineer
and software developer primarily specializing
in distributed systems.
And one of the projects I worked on,
ultimately became kind of the centerpiece behind Imply.
Imply, as a company is a database company.
What we do is we provide developers
a powerful tool in order to help them build various types
of data analytic applications.
We're also an open source company,
where the company develops
a popular open source project called Apache Druid.
>> Got it, so database as a service
for modern analytics applications.
You're also one of the original authors of Apache Druid.
Talk to me,
gimme a timeline, Druid's 10-year history or so.
What's the big picture?
What's been the market evolution that you've seen?
>> Yeah, absolutely.
So, I moved out to Silicon Valley basically to try
and work at a startup, 'cause I was enamored with startups
and I thought they were the coolest thing ever.
So, at one point, I basically joined the smallest startup
I could find.
It was a startup called Metamarkets,
which actually doesn't exist anymore,
it was ultimately acquired by Snapchat a couple years ago.
But, I was one of the first employees there.
And what we were trying to do at the time,
was we were trying to build an analytics application,
a user-facing application where people could slice
and dice various types of data.
At the time, the data sets we were working
with were like online advertising,
digital advertising data sets
which were very large and complex.
And, we really struggled to find a database
that could basically power the kind of interactive
and user experience that we know we want to provide
our end customers.
So, what ended up happening was we decided
to build our own database
and we were a three or five-person shop
when we decided to build our own database,
and that was Druid.
And over time, we saw many other types
of companies actually struggle with a similar set
of problems, albeit with very different types
of use cases and very different types of data sets.
And, the Druid community kind of grew and evolved from that.
And in my work in engaging with the community,
what I saw was a market opportunity and a market gap
and that's where Imply formed.
>> Let's double click on that.
You talked about why you built Druid,
the problem you were looking to solve.
But, talk to me about the role that Imply has.
>> Right.
So, Imply is a commercial company.
What we do is we build kind
of an end-to-end enterprise product around Druid
as the core engine.
Imply provides deployment,
it deploys management, it provides security,
and it also provides visualization
and monitoring pieces around Druid as a core engine.
What we aim to do at Imply is really enable developers
to build various types of data applications
with only the click of a few buttons
and interacting with a simple set of APIs.
So, the goal is, if you're a developer,
you don't have to think about managing
the database yourself,
you don't have to think about the operational complexity
at the database,
but instead, what you do is just work with APIs
and build your application.
>> So, then what gives Druid its superpower?
What makes Druid Druid?
>> Yeah, so, Druid, the easiest way to think
about it, is it's a really fast calculator
and it's a very fast calculator for a whole lot of data.
So, when you have a whole lot of data
and you want to crunch numbers very, very quickly, Druid
is very good at doing that.
And, people always ask me this question,
which is, what makes Druid special?
And I always struggle with it,
because it's never just one thing,
it's actually layers, upon layers,
upon layers of engineering.
You start with fundamentals
of how you maximally optimize the resources
of any hardware.
So, how do you maximize storage?
How do you maximize compute?
And then, there's a lot of optimizations
around how do you store the data?
How do you access that data in a very fast way once
it's stored in order to run computations very quickly?
So, unfortunately, there's no silver bullet
about Druid, but maybe I can summarize in this way.
Druid, it's like a search system, and a data warehouse,
and a time series database all mixed together.
And, that architecture enables it to be very, very quickly.
And unfortunately, if you don't know what some
of the components I'm talking about are,
it's hard to describe where the secret sauce is (chuckling).
>> Sometimes you want to keep that secret sauce secret.
Talk to me about the overall data space,
as we see these days, every company is a data company
or if it's not, it needs to be to be successful.
Where does Druid fit in the overall data space?
Give us that picture of where it fits.
>> Yeah, absolutely.
So, it's pretty interesting
that you see now in the public markets
as well as the private markets, some of the hottest unicorns
out there are actually data companies.
And, I think what people are are understanding now
for the first time, is just how vast and complex
the data space is and also how large the market is as well.
So for sure, there's many different components and pieces
in the data space,
and they oftentimes come together to form what's known
as a data stack.
So, data stack is basically kind of an architecture
that has various systems and each of these systems
are designed to do a certain set of things very, very well.
For example, a company that recently went public
is a company called Confluent,
which mostly catered towards data transport,
so getting data from one place to another.
They're built around
an open source engine called Apache Kafka.
Databricks is another mega unicorn
that's going to go public pretty soon.
And they're built around
an open source project called Spark,
which is mainly used for data processing.
Where we sit is on the data query side.
So, what that means is we're a system
in which people can store data
and then access that data very, very quickly.
And there's other systems
that do that, but where our bread and butter
is, is we're building some sort of application,
where you have end users that are clicking buttons
in order to get access to data,
we're a platform that enables the best end user experience.
We return queries very, very quickly with a consistent SLA,
we immediately visualize data
as soon as it's made available, and then we
can support many, many, many concurrent end users
to access the system at the same time.
>> So, real time.
One of the things I think
that we learned during the pandemic, one
of the many things is that access to real time data,
it's no longer a nice to have, it is table stakes
for, as I said, every company, these days is a data company.
So with how you describe it,
how should people think of Druid versus a data warehouse?
>> Yeah.
So, that's a great question.
And obviously, data warehouses have been around
since the 70s.
In the B2B space, they're among the largest players
that kind of exist in enterprise software.
So, it's only natural that when you come up
with sort of a new analytics database, that people compare
it with what they already know, which is data warehouse.
So, a lot of how we think
about why we're different than data warehouse goes back
to how I answered the previous question,
and that we're focused right now, really,
on powering different types of data applications.
Data applications are UIs
in which people are really accessing
and getting insights from data by clicking buttons
versus writing more complex equal queries.
And when you click buttons and you get access to data,
what you want in terms
of an end user experience, is you want answers
to questions to come back almost immediately.
So you don't want to click a button
and then see a spinning dial
that goes on for minute and minutes
before an answer comes back.
You basically want results to come back immediately.
You want that experience no matter what types
of queries that you're issuing
or how many people are issuing those queries.
If you have thousands, if not tens of thousands
of people that are trying to access data exact same time,
you want to give a consistent user experience like Google,
which is one of my favorite products.
There're millions of people that use Google,
and ask questions
and they get their answers back immediately.
So we try to provide that same experience,
but instead of a generic search engine,
what we're doing is we're providing a system
that basically answers questions on data
and users get a very interactive
and fast experience when asking questions.
And that's something that I think is very different
than what data warehouses are primarily specialized in.
Data warehouses are really designed to be systems
in which people write very large complex sequel queries
that might take minutes or hours sometimes to run.
But the experience of using a data warehouse
to power and application is not a great one.
>> So, I'm just curious, FJ,
in the last couple of years, with, as I mentioned
before the access to real time data no longer
a nice to have, but it's something business critical
for so many industries,
did you see any industries in particular
in the recent years that were really primed candidates
for what Druid would can deliver?
>> Yeah, that's a great question.
And you can imagine that the industries
that really heavily rely on fast decision making
are the ones that are earliest
to adopt technologies like this.
So, in the security space, and the observability space,
as well as working with networking
and various forms of backend kind of metrics data,
this system has been very popular and it's been popular
because people need to triage (indistinct) as they occur,
they need to resolve problems,
and they also need immediate visibility,
as well as very fast queries on data.
Another space is online advertising.
Online advertising, nowadays
is almost entirely programmatic and digital.
So, response times are critical in order to make decisions.
And that's where Druid was actually born.
It was born for advertising
before it kind of went everywhere else.
We're seeing it more in fraud protection, fraud prevention
as well as fraud diagnostics nowadays.
We're seeing it in retail as well,
which is pretty interesting.
And, the goal, of course,
is I believe every industry and every vertical needs
the capabilities that we provide.
So hopefully, we see a whole lot more use cases
in the near future.
>> Right, it's absolutely horizontal these days.
So, 10-year history, you've got a community of thousands,
what's the future of Druid?
What do you see when you open the crystal ball
and look now down the 12 months, 18 months road?
>> Yeah.
So, I think as a technologist,
your goal as the technologist,
at least for me, is to try and create technology
that has as much applicability
as possible and solves problems
for as many people as possible.
That's always the way I think about it.
So, I want to do good engineering
and I want to build good systems.
And I think what the hallmark of a really good system
is you can solve all different types of problems
and condense all these different problems, actually
into the same set of models and the same set of principles.
And, a thing that makes me most excited
about Druid is the many, many different industries
that it's found value and the many different use cases
it's found value.
So, if I were to give 30,000 foot roadmap,
that's what we're trying to do
with the next generation of Druid.
We're actually doing
a pretty major engine upgrade right now,
and pretty major overhaul the entire system.
And the goal of that is to take all the learnings
that we've had over the last decade
and to create something new that can solve
an expanded set of problems that we've heard
from the community and from other places as well.
>> Excellent.
FJ, exciting work that you've done the last 10 years.
Congratulations on that.
Looking forward to the roadmap that you talked about.
Thanks for sharing what Druid is, the Imply connection,
and all the different use cases where it applies.
We appreciate your insights.
>> Appreciate you having me on the show.
Thank you very much.
>> My pleasure.
For FJ Yang, I'm Lisa Martin.
You're watching this CUBE Conversation,
the leader in live tech enterprise coverage.
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