RBCROYALBANK.COM

Streaming Data Empowers
Royal Bank of Canada
to be a Data-Driven
Organization

ConfluentControl Center

Multi-Datacenter
Replication

SchemaRegistry

As Canada’s biggest bank, and one of the largest in the world based onmarket capitalization, RBC has a diversified business model with a focus on
innovation and providing exceptional experiences to their 16 million clientsin Canada, the U.S. and 35 other countries. To continue the bank’s missionof providing worldclass products and services to its clients, RBC’s Data andHeadquarters
Montreal, Canada

Analytics (DNA) and Enterprise Cloud teams joined forces to build a real-time,

Industry
Financial Services

cloud, machine learning and AI initiatives.

Challenge
Improve the customer experiencethrough data while staying compliant
with industry regulations
Solution
Implemented Confluent Platform tostream data in real-time, migrate to an
event-based architecture and embark
on a microservices transformation
Results• Higher quality, faster andscalable customer services• Increased efficiency
in application building• Lowered anomaly detection
time from weeks to seconds

scalable and eventdriven data architecture for the bank’s growing number ofThe DNA and Cloud teams partner with several lines of business across thebank such as wealth management, digital marketing, corporate real estateand fraud, by providing access to technology, knowledge sharing and training
opportunities across the business groups. This partnership allows RBC todesign new services faster and meet the needs of RBC’s diverse client base.
They also help the business lines bring up new services faster while helping
the development teams navigate complex data ownership, security, cloudand regulatory compliance.

There isn’t another product that competes
with Confluent Platform. Streaming data
as events enables completely new ways forsolving problems at scale.

–Mike Krolnick, Head of Engineering, Enterprise Cloud, RBC• Implemented data reuse acrossteams for relevant business insights

To deliver the event-based architecture for RBC’s business lines, the DNA• Greatly reduced costs

and Cloud team selected Confluent Platform and Apache Kafka® to be thefoundation of their scalable, real-time streaming data architecture. RBC
now has a cross-functional team helping transform the bank to a technology©2020 Confluent, Inc. | confluent.io/insurance

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RBCROYALBANK.COMand data-driven organization. RBC’s goal through this“We realize that to prepare for a more competitive future andtransformation is to capitalize on the next generation

to extend our leadership in innovation, we need to be ableof open-source, cloud-enabled, real-time and responsive

to generate accurate data and business insights,” said Kerry

application and software development. By adopting

Joel, Sr. Director, Product Innovation, Data and Analytics.

Confluent Platform and Apache Kafka, RBC is realizing
this objective, as is evidenced by their deployment of over50 applications on their event-driven data architecture.

Due to regulation, security
and the need for auditing,data within these various groups
must be handled thoughtfully,
with insight on how to maximize
its value and quality without
introducing risk.

Part of the central technology stack at RBC is a
mainframe-based infrastructure that supports manyapplications. This infrastructure is the system of record
for a number of client profiles and before Confluent,accessing the data could be delayed, complex and costly
for the bank.
“We needed a way to rescue data off of theseaccumulated assets, including the mainframe, in a cloudnative, microservice-based fashion,” said Mike Krolnik,
Head of Engineering within the Enterprise Cloud team.
The mainframe serves data as clients search for
information on their website. This produced a workloadthat was high in reads, low in writes. Also, because itprocessed new transactions using a batch-oriented
database, providing real-time updates on account

Challenges
Like many banks with a long history and a large clientbalances was limited. While maintaining the writes to theclient profiles was necessary, the high read workload wasa candidate for elimination.

base, RBC’s accumulated assets are very complex. As thebusiness grows, technologies evolve and material events
like acquisitions occur, RBC exerts force to adapt itself toSolution

these changes. RBC’s data landscape exists across lines

RBC selected Confluent Platform based on Apache Kafka,

of business and within a centrally managed infrastructure.

and wrote a microservice to significantly reduce the reads

Due to regulation, security and the need for auditing, dataon the mainframe, saving RBC fixed infrastructure costs

within these various groups must be handled thoughtfully,(OPEX). RBC stayed compliant with bank regulations andwith insight on how to maximize its value and quality

business logic, and is now able to create new applications

without introducing risk to clients or the organization.

using the same event-based architecture.

Kafka transforms even the most basic of initiatives. Adoptionof Kafka at RBC has been massive and organic. Within the firstsix weeks after our launch of Kafka, we had 37 teams asking
to use Kafka for various projects and initiatives.

–Kerry Joel, Sr Director, Product Innovation, Data and Analytics©2020 Confluent, Inc. | confluent.io/insurance

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RBCROYALBANK.COM

After a significant number of additional successful

4.

RBC can decouple everything to increase the speed ofapplication deployments, RBC’s DNA and Cloud teams

innovation and create new flows without impacting

implemented Confluent Platform to act as a centralized

current operational systems, which is empowered byreal-time data platform to support the bank’s business

Confluent Platform’s ability to orchestrate complex

lines. Confluent Platform flexibly ingests data from RBC’sbusiness flows.

core banking platforms, such as the mainframe and datafrom their cloud-native applications. Ultimately, thebank has adopted an event-based architecture which isallowing developers, operators and the C-suite at RBC tore-imagine how data is stored and analyzed

Technology Patterns Realized
for Optimal Success

The simplicity of Confluent added to RBC’s decision toadopt the technology. “Our journey began when we looked
into event-based architecture,” said Kolnik. “We realized
the potential of that architecture to transform ourbusiness.”“The ability to stream events transforms even the mostbasic of initiatives,” said Joel. “Adoption at RBC has beenmassive and organic. Within the first six weeks after

RBC experienced the emergence of four technology

our launch of the Confluent Platform, we had 37 teams

patterns throughout this transformation:

identified as early Kafka adopters for various projects and1.

The first was moving the accumulated assets to ainitiatives.”cloud-native, microservices architecture without

In addition, RBC relies on Control Center, a monitoring

rewriting the core systems of record. This meant

tool in Confluent Enterprise, that gives the various

RBC could slice large monolithic applications into

operating teams the ability and confidence to manage

smaller, more agile components with real-time data

their environment company wide. “Control Center gives

synchronization. “We could begin the process of slicing

you full visibility of all messages as they move through theour large, monolithic applications into smaller, more

system, such as what the consumers and the producers

agile pieces,” said Krolnik.

are doing and whether they’re there or not,” said Krolnik.
“This is the number one question we get as a platform

2.

Using microservices, RBC built new functionality
with event processors rather than a database or ETL
patterns. This allows for more flexible and decoupled

3.

support team.”
To increase the speed of innovation, establishing thesystems without needing to rewrite the entire system.

schema became a foundational part of RBC’s successBusiness teams were now able to perform quick data

allowed RBC to speed operations without impacting otherdiscovery and analysis for data-driven insights. These

consumers and producers. “Right now, most of the things

insights span transactions, decision support andwe build are dependent on each other. If one changes theinteractions in real time.

other needs to change. That slows down time to market,with Confluent. The ability to evolve the schema has

Confluent created an open source event streaming platform
and reimagined it as an enterprise solution. We understand thecapabilities it gives our business and implemented it within ourenterprise data strategy.

–Kerry Joel, Sr Director, Product Innovation, Data and Analytics©2020 Confluent, Inc. | confluent.io/insurance

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that slows down change, it slows down innovation,” said

Lowered anomaly detection time from weeks to real-Krolnik. “With Kafka and our Schema evolution strategy,time. Before Kafka, RBC would spend weeks identifying

we’re able to decouple everything. So you can change

data anomalies. “At RBC, accuracy of data is incredibly

either side of the equation. I think that’s somethingimportant. We spend a substantial amount of time asking

unique in the platform.”‘why is this data wrong?’ To have Confluent answer that

The simplicity of implementing Kafka added to RBC’sdetect variances, we save many, many weeks of manual

decision to partner with Confluent. “Most of the open

data analysis. That’s time earned back that can be spentsource tools are challenging, but not Kafka,” said Krolnik.

on deriving business insights from that same data, instead

question for us by using machine learning techniques toof questioning it,” said Krolnik.

Results

Implemented data reuse across teams for relevantbusiness insights. Immediately after developing RBC’s“Confluent created an open source event streaming

first Kafka use case, a successful sales enablement tool,

platform and reimagined it as an enterprise solution,” said

the RBC team realized the power of true information

Joel. “We understand the capabilities it gives our businesssharing across the organization. “When we first put theand implemented it within our enterprise data strategy.”

data into Kafka we realized it was the first time we could

The results RBC has experienced since implementing thereally share data across teams. Kafka has created a lot

streaming platform are:of firsts for RBC,” said Krolnik. “With Kafka, we can have
many people talking to the same data source without

Increased efficiency in app building. RBC has built overthousands of apps across the business lines and hasinterfering with one another, which is a huge benefit for us.”hundreds of projects running at any time across the bank.

“RBC is a people business. As a financial institution, it’s

“Kafka is so much more efficient and stronger than what

always about our clients’ trust and safeguarding their

we were using before, and Confluent brings a lot more

information and money is always a top priority,” said Joel.

value to the way we build apps,” said Krolnik. “Kafka was“To manage that well, we focus on developing products anddifferent than I thought it was. Streaming eliminated theservices that reflect how clients want us to interact withproblems we had.”and support them.”

RBC is a people business. As a financial institution, it’s always
about our customers and we’re humbled by the fact that weare entrusted to hold their money. To manage that well, wefocus on developing products and services that reflect how
customers want us to interact and support them..

–Kerry Joel, Sr Director, Product Innovation, Data and Analytics

Learn More About Royal Bank Of Canada
https://www.rbcroyalbank.com/©2020 Confluent, Inc. | confluent.io/insurance

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