The document discusses overcoming database scaling challenges with ScyllaDB, highlighting its advantages over legacy NoSQL databases, such as higher throughput and lower latency. It features insights from experts Peter Corless and Tomer Sandler, and provides real-world applications and examples of successful deployments. The discussion covers emergency scalability, performance limits, and planning for data at scale.

1. Overcoming Database
Scaling Challenges with a
New Approach to NoSQL
Peter Corless — Director of Technical Advocacy, ScyllaDB
Tomer Sandler — Director of Customer Success & TAM, ScyllaDB

2. Introductions
Peter Corless, Director of Technical Advocacy
+ Editor of and frequent contributor to the ScyllaDB blog
+ Program chair for ScyllaDB Summit and P99 CONF
+ Host of ScyllaDB Masterclass series
+ @PeterCorless on Twitter
Tomer Sandler, Director of Customer Success and TAM
+ Lifecycle support: from deployment to operations
+ Troubleshoots toughest real-time technical issues
+ Plays a mean saxophone

3. + Infoworld 2020 Technology of the Year!
+ Founded by designers of KVM Hypervisor
The Database Built for Gamechangers
3
“ScyllaDB stands apart...It’s the rare product
that exceeds my expectations.”
– Martin Heller, InfoWorld contributing editor and reviewer
“For 99.9% of applications, ScyllaDB delivers all the
power a customer will ever need, on workloads that other
databases can’t touch – and at a fraction of the cost of
an in-memory solution.”
– Adrian Bridgewater, Forbes senior contributor
+ Resolves challenges of legacy NoSQL databases
+ >5x higher throughput
+ >20x lower latency
+ >75% TCO savings
+ DBaaS/Cloud, Enterprise and Open Source solutions
+ Proven globally at scale

4. 4
+400 Gamechangers Leverage ScyllaDB
Seamless experiences
across content + devices
Fast computation of flight
pricing
Corporate fleet
management
Real-time analytics 2,000,000 SKU -commerce
management
Video recommendation
management
Threat intelligence service
using JanusGraph
Real time fraud detection
across 6M transactions/day
Uber scale, mission critical
chat & messaging app
Network security threat
detection
Power ~50M X1 DVRs with
billions of reqs/day
Precision healthcare via
Edison AI
Inventory hub for retail
operations
Property listings and
updates
Unified ML feature store
across the business
Cryptocurrency exchange
app
Geography-based
recommendations
Global operations- Avon,
Body Shop + more
Predictable performance for
on sale surges
GPS-based exercise
tracking
Serving dynamic live
streams at scale
Powering India's top
social media platform
Personalized
advertising to players
Distribution of game
assets in Unreal Engine

5. ?
How does a company know
when they’ve hit the wall?
Q1:
5

6. ?
What about throughput limits?
What’s different if you added more
workloads vs. bursty time-of-day
traffic?
6
Q2:

7. ?
Even with the best up-front planning
you can run into a stochastic event
that can go beyond your predictive
planning? What then?
7
Q3:

8. ?
Let’s define “emergency scalability”
as responding to unplanned demands
on your system. What examples have
you seen of “emergency scaling”
limits in the wild?
8
Q4:

9. ?
What’s different doing “emergency
scaling” with an on-premises
deployment vs.
a public cloud?
9
Q5:

10. ?
Is the cloud really just “someone
else’s computer?”
10
Q6:

11. ?
Is data volume itself a barrier to
scale? “I want a petabyte of storage
fully loaded by 8 AM tomorrow!”
11
Q7:

12. ?
What are other real-time limits you
can’t avoid? Like the latency of the
speed of light, or the raw time to
actually stream your data to the new
hardware?
12
Q8:

13. ?
Can you talk more about latency
issues? You can’t just throw
hardware at it. Because that could
even make latencies worse.
13
Q9:

14. ?
What advice can you give to
system architects planning for
data at scale?
14
Q10:

15. Poll
How much data do you under management of your
transactional database?

16. Q&A
WANT TO KEEP LEARNING?
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17. Thank you
for joining us today.
@scylladb scylladb/
slack.scylladb.com
@scylladb company/scylladb/
scylladb/