Case Study

ETC

Confluent Helps ETC Reduce TCO by Optimizing
Infrastructure and Operational Costs While
Reducing Downtime Risk

Founded in 1999, Electronic Transaction Consultants, LLC (ETC) is a leading U.S. intelligent
transportation systems and smart mobility provider, developing and delivering bestin-class solutions for tolling, congestion management, urban mobility, and multimodal
Headquarters
Richardson, Texas

transportation needs. ETC’s passionate and innovative team has been driving the future ofmobility since 1999, with many industry firsts, including all-electronic tolling (AET), dynamicpricing, agency interoperability, hosted mobility solutions, and machine learning.

Industry
Transportation: Tolling and Smart
Mobility

For over two decades, ETC has delivered sophisticated solutions to many of the largest toll

Challenge
Reduce operational costs anddowntime risks while seamlessly
processing massive volumes oftransaction data pouring in froma growing number of roadside
sensors in real time.

$3 billion in revenues for our customers, incorporating the latest in evergreen open source andSolution
Confluent Cloud for a fast, agile
enterprise data-in-motion platformthat reduces the total cost ofownership by minimizing Apache Kafka®
operational costs and downtime risk.

“When we first started implementing Kafka, we didn’t realize how important it was goingResults• An average 20% infrastructure costsavings per customer for every new
customer added to the fully managed
Confluent Cloud platform vs. open
source Kafka• Significant reduction in downtime
risk, where every minute of downtime
can cost tens of thousands of dollars
per minute in lost revenue• ~50% reduction in in-house resource
demand for managing Kafka

authorities in the U.S., including statewide programs, county networks, and tolling-specificauthorities. ETC’s solutions process over two billion transactions annually, totaling over
SaaS technologies and big data architecture through our innovative riteSuite™ products.
Facing a rapidly increasing volume of data flowing in from the various sensors and devices
associated with its customers’ ever-expanding toll and smart mobility projects, ETC
scaled up to meet surging demand by turning to Kafka for messaging queue agility and toConfluent Cloud to maintain high uptime for critically important transaction processing
systems and to reduce infrastructure-related and operational costs.
to become for us frankly,” said Josh LittleSun, ETC’s Director of Technology. “We thought
it was just going to be another tool in our toolbox. As it evolved though, we realized it had
become the beating heart and backbone of our solution, so that’s really the main reason
we switched over to Confluent.”
By bringing in Confluent Cloud, ETC was able to significantly reduce the amount of time its
engineers spend on managing Kafka, while reducing downtime risk and increasing agility.
Instead, ETC spends its valuable engineering resources on accelerating innovation andbusiness-differentiating projects.

“With Confluent, we’ve been able to optimize our in-house Kafka-support resources by~50% and redeploy our engineering and development talent to creating innovativeand value-added features for our customers instead. Additionally, engaging
Confluent gave us the peace of mind that comes with minimizing downtime andknowing that our Kafka systems are in the hands of one of the industry’s leading
data-in-motion experts.”
Josh LittleSun
Director of Technology, Roadside© 2022 Confluent, Inc. | Confluent.io1

DoctypeCase| Document
Study |Title
ETC

Technical Solution

Business Results

intelligent transportation systems and smart mobility industry,~50% Reduction in the In-House Resources
Dedicated to Managing Kafka

ETC needed to figure out three main challenges to support surging

Between compute, storage, and networking, ETC had begun to feel

As a leading systems and services provider for the rapidly evolving

demand and digitization:
1. How to get more information upstream in a manageable way
2. How to make that information auditable
3. How to continue to scale horizontally as they add more sensors,
without breaking the bank or over-consuming internal resources

the impact of increasing infrastructure costs as new customers
came on board. The company was having to manually add more
Amazon Elastic Compute instances to handle the increased
throughput, in addition to managing the high networking costsof complying with government-enforced, 30-day multi-zonal
persistence requirements. Thanks to Confluent Cloud, ETC

ETC began to incorporate Kafka into its IT architecture in 2015 tohas already begun to realize significant cost savings aroundaddress this need for increased scalability and flexibility. By theinfrastructure as it elastically scales to meet demand while adding

time ETC got its Kafka workloads into production four years later,

more customers.

Kafka had started to take on a significantly more important role
Open Source
Apache Kafka

Confluent
CloudScaling

Scaling

Load balancing

Load balancing

Partition placement

Partition placement

Logical storage

Logical storage

Broker settings

Broker settings

ZooKeeper

ZooKeeper

Kafka patching

Kafka patching

After conducting an internal TCO analysis, ETC decided to move

JVM

JVM

from self-supporting Kafka in-house to Confluent Cloud in early

O/SO/S

2021. To support the growing number of expected new customers

VMsVMson board, ETC will save considerably on both infrastructure costs

Servers

Servers

and full-time employees previously tasked with managing andPhysical storage

Physical storage

maintaining Kafka.

Ethernet

Ethernet

Physical security

Physical security

than they had originally anticipated.
A year ago, ETC was leveraging Kafka to process close toten million events per day for its dynamic pricing solutions—a number that will likely double or triple in the next couple ofyears. With such explosive growth, ETC realized its total cost ofownership (TCO) for self-supporting open source Kafka—frominfrastructure to operations and maintenance to downtime
risk—was quickly piling up toward unacceptably high levels.

Minimizing downtime risk was another key reason ETC wanted tomove its workloads to Confluent Cloud, as Kafka gradually became“the central nervous system” of its data infrastructure andapplications. ETC’s engineers found that while it’s relatively easy
to run Kafka smoothly when things are going well, debugging or
preventing Kafka-associated downtime required more specialized
in-house resources than could be provided cost-effectively.
Essentially, Kafka had become “too big to fail.” In addition, theinternal technical team had to dedicate significant resources tosecurity and governance, because open source Apache Kafka does
not provide sufficient tools at the enterprise level.
Therefore, migrating to Confluent Cloud became the clear way
forward for ETC to be able to seamlessly and securely scale up for
new customers and support rapidly increasing data volumes.

You ManageProvider Managed

Additionally, Josh LittleSun estimated that, within a few
months, the introduction of Confluent has already enabled ETC
to reallocate ~50% of in-house FTE resources that had beenpreviously dedicated to supporting open source Kafka to morestrategic projects accelerating innovation. These efficiencies
come from the fact that, as a fully managed service, Confluent
Cloud takes care of a wide variety of tasks that would otherwise
consume a significant amount of valuable engineering resources,
including capacity planning, scaling, load balancing, Kafka
patching and performance, and government-required disasterrecovery testing. As a result, ETC’s engineers have been able tofocus on more strategic, high-priority projects that differentiate
its business rather than managing low-level infrastructure.
Notably, the ~50% in-house resource optimization is just for its
current workloads. As ETC leverages Confluent Cloud for additional
workloads, the realized cost savings are projected to grow over time.

© 2022 Confluent, Inc. | Confluent.io2

Case Study | ETC

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Other Benefits
Confluent Cloud helps ETC reduce
its TCO significantly• Increased processing speeds• Efficient and accurate auditing• Improved debugging and troubleshooting“Being able to take and copy topics and replay topics has lowered~50% Savings

our error rate in production and enabled us to solve issues for~20% Savings

our customers even faster than before,” Josh LittleSun said.
“Another benefit is the ability to decouple all our processing fromour reporting, which allows us to process events faster and keep
our database running seamlessly, while providing near-real-time

FTE Cost

Self-managed OSS AK

Infrastructure CostConfluent Cloud

analytics and quick reports to our customers.”

The FutureWith Confluent Cloud, ETC can operate significantly more efficiently,

Reduced Downtime Risk
In one notable example, ETC’s dynamically priced roadsidesolutions involve pushing the pricing generated based on traffic
data to the rate signs, a process that largely relies on dependable
performance from Kafka. “If it goes down, there are no rates
published to the sign which would result in foregone revenue,”with reduced infrastructure costs, maintenance overhead, anddowntime risk to cost-effectively meet surging demand.

Learn More About ETC
https://etcc.com

said Josh LittleSun. “In such a scenario, losses can potentiallybe substantial per each minute of downtime.” With Confluent
Cloud’s 99.95% uptime SLA, this alone accounts for significant
risk reduction for ETC from using Confluent Cloud, not to mention
the additional cost of pulling valuable engineering resourcesfrom other projects to deal with Kafka-related fire drills.

© 2022 Confluent, Inc. | Confluent.io3