Transcript of a BriefingsDirect podcast on how cloud data-analytics provider GoodData is making use of HP Vertica.

1. GoodData Developers Share Their Big Data Platform Wish
List
Transcript of a BriefingsDirect podcast on how cloud data-analytics provider GoodData is
making use of HP Vertica.
Listen to the podcast. Find it on iTunes. Sponsor: HP
Dana Gardner: Hello, and welcome to the next edition of the HP Discover Podcast Series. I'm
Dana Gardner, Principal Analyst at Interarbor Solutions, your host and
moderator for this ongoing sponsored discussion on IT innovation and how it’s
making an impact on people’s lives. Once again, we're focusing on how
companies are adapting to the new style of IT to improve IT performance and
deliver better user experiences, as well as better business results.
Our next innovation case study interview highlights how GoodData has created
a business intelligence (BI)-as-a-service capability across multiple industries to
allow users to take advantage of both big-data performance as well as cloud efficiencies.
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To learn more we are here with a panel. We are joined by Tomas Jirotka, Product Manager of
GoodData. Welcome, Tomas.
Tomas Jirotka: Hello. It's great to be here.
Gardner: We are also here with Eamon O'Neill, the Director of Product
Management at HP Vertica. Welcome, Eamon.
Eamon O'Neill: Thanks, Dana.
Gardner: And Karel Jakubec, Software Engineer at GoodData. Welcome.
Karel Jakubec: Thanks. It's great to be here.
Gardner: Let’s we start with you, Tomas. Tell us a bit about GoodData and why you've decided
that the cloud model, data warehouses, and BI as a service is the right fit for this marketplace?
Jirotka: GoodData was founded eight years ago, and from the beginning, it's been developed as
a cloud company. We provide software as a service (SaaS). We allow our customers to leverage
their data and not worry about hardware/software installations and other stuff. We just provide
Gardner

2. them a great service. Their experience is seamless, and our customers can simply enjoy the
product.
Gardner: So can you attach your data warehouse to any type of data or are you
focused on a certain kind? How flexible and agile are your services?
Jirotka: We provide a platform -- and the platform is very flexible. So it's
possible to have any type of data, and create insights to it there. You can
analyze data coming from marketing, sales, or manufacturing divisions no
matter in which industry you are.
Gardner: If I'm an enterprise and I want to do BI, why should I use your services rather than
build my own data center? What's the advantage for which your customers make this choice?
Cheaper solution
Jirotka: First of all, our solution is cheaper. We have a multi-tenant environment. So the
customers effectively share the resources we provide them. And, of course, we have experience
and knowledge of the industry. This is very helpful when you're a beginner in BI.
Gardner: So, in order to make sure that your cloud-based services are as competitive and even
much better in terms of speed, agility and cost, you need to have the right platform and the right
architecture.
Karel, what have been some of the top requirements you’ve had as you've gone
about creating your services in the cloud?
Jakubec: The priority was to be able to scale, as our customers are coming in
with bigger and bigger datasets. That's the reason we need technologies like
Vertica, which scales very well by just adding nodes to cluster. Without this
ability, you realize you cannot implement solution for the biggest customers as
you're already running the biggest machines on the market, yet they're still not
able to finish computation in a reasonable time.
Gardner: I've seen that you have something on the order of 40,000 customers. Is that correct?
Jirotka: Something like that.
Gardner: Does the size and volume of the data for each of these vary incredibly, or are most of
them using much larger datasets? How diverse and how varied is the amount of data that you're
dealing with, customer by customer?
Jirotka
Jakubec

3. Jirotka: It really depends. A lot of customers, for example, uses Salesforce.com or other cloud
services like that. We can say that these data are somehow standardized. We know the APIs of
these services very well, and we can deliver the solution in just a couple of days or weeks.
Some of the customers are more complex. They use a lot of services from the Internet or
internally, and we need to analyze all of the sources and combine them. That's really hard work.
Gardner: In addition to scale and efficiency in terms of cost, you need to also be very adept at a
variety of different connection capabilities, APIs, different data sets, native data, and that sort of
thing.
Jirotka: Exactly. Agility, in this sense, is really curial.
Gardner: How long you have been using Vertica and how long have you been using BI through
Vertica for a variety of these platform services?
Working with Vertica
Jirotka: We started working with Vertica at the beginning of the last year. So, one and a half
years. We began moving some of our customers with the largest data marts to Vertica in the
Spring of last year.
Gardner: What were some of the driving requirements for changing from where you were
before?
Jirotka: The most important factor was performance. It's no secret that we also have Postgres in
our platform. Postgres simply doesn’t support big data. So we chose Vertica to have a solution
that is scalable up to terabytes of data.
Gardner: We're learning quite a bit more about Vertica and the roadmap. I'd like to check in
with Eamon and hear more about what some of the newer features are. What’s
creating excitement?
O’Neill: Far and away, the most exciting is about real-time personalized
analytics. This is going to allow GoodData to show a new kind of BI in the
cloud. A new feature we released last year in our latest 7.1 release is called Live
Aggregate Projections. It's for telling you about what’s going on in your electric
smart meter, that FitBit that you're wearing on your wrist, or even your cell-
phone plan or personal finances.
A few years ago, Vertica was blazing fast, telling you what a million people are doing right now
and looking for patterns in the data, but it wasn’t as fast in telling you about my data. So we've
changed that.
O'Neill

4. With this new feature, Live Aggregate Projections, you can actually get blazing fast analytics on
discrete data. That discrete data is data about one individual or one device. It could be that a cell
phone company wants to do analytics on one particular cell phone tower or one meter.
That’s very new and is going to open up a whole new kind of dashboarding for GoodData in the
cloud. People are going to now get the sub-second response to see changes in their power
consumption, what was the longest phone call they made this week, the shortest phone call they
made today, or how often do they go over their data roaming charges. They'll get real-time alerts
about these kinds of things.
When that was introduced last year. it was standing room only. They were showing some great
stats from power meters and then from houses in Europe. They were fed into Vertica and they
showed queries that last year we were taking Vertica one-and-half seconds. We're now taking 0.2
seconds. They were looking at 25 million meters in the space for a few minutes. This is going to
open up a whole new kind of dashboard for GoodData and new kinds of customers.
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Gardner: Tomas, does this sound like something your customers are interested in, maybe retail?
The Internet of Things is also becoming prominent, machine to machine, data interactions. How
do you view what we've just heard Eamon describe, how interesting is it?
More important
Jirotka: It sounds really good. Real-time, or near real-time, analytics is becoming a more-and-
more important topic. We hear it also from our customers. So we should definitely think about
this feature or how to integrate it into the platform.
Gardner: Any thoughts, Karel?
Jakubec: Once we introduce Vertica 7.1 to our platform, it will be definitely one of features we
will focus on. We have developed a quite complex caching mechanism for intermediate results
and it works like a charm for Postgres SQL, but unfortunately it doesn't perform so well for
Vertica. We believe that features like Live Aggregate Projection will improve this performance.
Gardner: So it's interesting. As HP Vertica comes out with new features, that’s something that
you can productize, take out to the market, and then find new needs that you could then take back
to Vertica. Is there a feedback loop? Do you feel like this is a partnership where you're displaying
your knowledge from the market that helps them technically create new requirements?

5. Jakubec: Definitely, it's a partnership and I would say a complex circle. A new feature is
released, we provide feedback, and you have a direction to do another feature or improve the
current one. It works very similarly with some of our customers.
O’Neill: It happens at a deeper level too. Karel’s coworkers flew over from Brno last year, to our
office in Cambridge, Massachusetts and hung out for a couple of days, exchanging design ideas.
So we learned from them as well.
They had done some things around multi-tenancy where they were ahead of us and they were
able to tell us how Vertica performed when they put extra schemers on a catalog. We learned
from that and we could give them advice about it. Engineer-to-engineer exchanges happen pretty
often in the conference rooms.
Gardner: Eamon, were there any other specific features that are popping out in terms of
interest?
O’Neil: Definitely our SQL on Hadoop enhancements. For a couple of years now we've been
enabling people to do BI on top of Hadoop. We had various connectors, but we have made it
even faster and cheaper now. In this most recent 7.1 release, you can now install Vertica on your
Hadoop cluster. So you no longer have to maintain dedicated hardware for Vertica and you don’t
have to make copies of the data.
The message is that you can now analyze your data, where it is and as it is, without converting
from the Hadoop format or a duplication. That’s going to save companies a lot of money. Now,
what we've done is brought the most sophisticated SQL on Hadoop to people without duplication
of data.
Gardner: Tomas, how does Hadoop factor into your future plans?
Using Hadoop
Jirotka: We employ Hadoop in our platform too. There are some ETL scripts, but we've used it
in a traditional form of MapReduce jobs for a long time. This is really costly and inefficient
approach because it takes much time to develop and debug it. So we may think about using
Vertica directly with Hadoop. This would dramatically decrease the time to deliver it to the
customer and also the running time of the scripts.
Gardner: Eamon, any other issues that come to mind in terms of prominence among
developers?
O’Neill: Last year, we had our Customer Advisory Board, where I got to ask them about those
things. Security came to the forefront again and again. Our new release has new features around
data-access control.

6. We now make it easy for them to say that, for example, Karel can access all the columns in a
table, but I can only access a subset of them. Previously, the developers could do this with
Vertica, but they had to maintain SQL views and they didn’t like that. Now it's done centrally.
They like the data-access control improvements, and they're saying t just keep it up. They want
more encryption at rest, and they want more integration. They particularly stress that they want
integration with the security policies in their other applications outside the database. They don’t
want have to maintain security in 15 places. They'd like Vertica to help them pull that together.
Gardner: Any thoughts about security governance and granularity of access control.
Jirotka: As we're a SaaS company, security is number one for us. So far, we have some solutions
that work for us, but these solutions are quite complex. Maybe we can discover new features
from Vertica and use that feature.
Jakubec: Any simplification of security and access controls is a great new. Restriction of access
for some users to just subset of values or some columns is very common use case for many
customers. We already have a mechanism to do it, but as Eamon said it involves maintenance of
views or complex filtering. If it is supported by Vertica directly, it’s great. I didn’t know that
before and I hope we can use it.
Gardner: Very good. I'm afraid we’ll have to leave it there. We've been hearing how GoodData,
based in San Francisco, a BI service provider, acts as a litmus test for how a platform should
behave in the market, both in terms of performance as well as economics. They've been telling us
their story as well as their interest in the latest version of HP Vertica.
So a big thank you to our guests. We've been here with Tomas Jirotka, Product Manager at
GoodData. Thank you, Tomas.
Jirotka: Thank you too.
Become a member of MyVertica
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Gardner: And Eamon O’Neill, Director of Product Management at HP Vertica. Thank you.
O’Neill: Thanks a lot, Dana.
Gardner: And Karel Jakubec, the Software Engineer at GoodData. Thank you, sir.
Jakubec: Thank you. It’s been a pleasure for me.
Gardner: And also a big thank you to our audience for joining this special new style of IT
discussion.

7. I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host for this ongoing series of
HP sponsored discussions. Thanks for joining, and don’t forget to come back next time.
Listen to the podcast. Find it on iTunes. Sponsor: HP
Transcript of a BriefingsDirect podcast on how cloud data-analytics provider GoodData is
making use of HP Vertica. Copyright Interarbor Solutions, LLC, 2005-2014. All rights reserved.
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