Introducing Events and Stream Processing into Nationwide Building Society
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Watch this talk here: https://www.confluent.io/online-talks/introducing-events-and-stream-processing-nationwide-building-society Open Banking regulations compel the UK’s largest banks, and building societies to enable their customers to share personal information with other regulated companies securely. As a result companies such as Nationwide Building Society are re-architecting their processes and infrastructure around customer needs to reduce the risk of losing relevance and the ability to innovate. In this online talk, you will learn why, when facing Open Banking regulation and rapidly increasing transaction volumes, Nationwide decided to take load off their back-end systems through real-time streaming of data changes into Apache Kafka®. You will hear how Nationwide started their journey with Apache Kafka®, beginning with the initial use case of creating a real-time data cache using Change Data Capture, Confluent Platform and Microservices. Rob Jackson, Head of Application Architecture, will also cover how Confluent enabled Nationwide to build the stream processing backbone that is being used to re-engineer the entire banking experience including online banking, payment processing and mortgage applications. View now to: -Explore the technologies used by Nationwide to meet the challenges of Open Banking -Understand how Nationwide is using KSQL and Kafka Streams Framework to join topics and process data. -Learn how Confluent Platform can enable enterprises such as Nationwide to embrace the event streaming paradigm -See a working demo of the Nationwide system and what happens when the underlying infrastructure breaks.
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Introducing Events and Stream Processing into Nationwide Building Society
- 1. Speed Layer Architecture April 2019 Rob Jackson and Pete Cracknell Nationwide Building Society
- 2. 2 Contents 1. Who is Nationwide Building Society? 2. What is the business challenge we’re responding to? 3. What is the Speed Layer? 4. Typical current state architecture 5. Target state architecture 6. How does data flow through the Speed Layer? 7. How we consume data from the Speed Layer 8. How the Speed Layer is deployed 9. Progress 10. Streaming assessment 11. Value achieved 12. Demo
- 3. 3 Who is Nationwide Building Society? • Formed in 1884 and renamed to become the Nationwide Building Society in 1970 • We’re the largest building society in the world. • A major provider of mortgages, loans, savings and current accounts in the UK and launched the first (or 2nd) Internet Banking Service in 1997 • We recently announced an investment of an additional £1.4 billion (total £4.1bn) over 5 years to simplify, digitise and transform our IT estate. • Confluent and Kafka form the heart of an important part o that investment.
- 4. 4 • Regulation such as Open Banking • Business growth • 24 x 7 availability expectations from customers and regulators • Cloud adoption • Capitalising on our data • A need for agility and innovation … and our existing platforms were making this difficult
- 5. The Speed Layer will be the preferred source of data for high-volume read-only data requests and event sourcing. It will deliver secure, near real time customer, account and transaction information from back end systems to front end systems with speed and resilience. It will use the latest technologies built for cloud as highly available and distributed. It will provide NBS with the first event based real-time data platform ready for digital. DEFINITION FOUR KEY CHARACTERISTICS SCALABILITY: The Speed Layer platform will be built on cloud ready PaaS architecture to allow for significant and frictionless scaling that is cost efficient. FAST AND AGILE: The Speed Layer will unlock data in systems of record enabling digital and agile development teams to rapidly deliver new features and services RICH DATA SET: Provide a rich accessible data set enhanced with data and analytics from OpenBanking and social media. It will also future proof for other interactions such as IoT RESILIENT: Reduce the load on core systems and isolate them from the demands of the digital platforms: mobile, internet and OpenBanking in particular. Built with proven scalable cloud ready components for greater capacity and with resilience 5 What is the Speed Layer?
- 6. 6 As is logical E2E Architecture API Gateway Channel Web Services Enterprise Web Services Back-end Services Mainframes Fairly normal, is there a problem?
- 7. 7 API Gateway Stream Processing Mainframes + other sources of data Kafka Topics Target System Architecture CDC Kafka Topics Microservices Channel Services Enterprise Services Protocol adapters WritesReads
- 8. System of Record(s) CDC Replication Engine Source DB Kafka Raw Topic – raw data Stream processingA Microservice Kafka Published Topic – processed data Materialisation Microservice NoSQL tables {REST APIs} 1. Change Data Capture (CDC) is deployed to the System of Record (SoR) and pushes changes from source database to Kafka Topic 2. Kafka topics contain data in the format of the source system. There will be one raw topic per table replicated. Data is typically held here for c7 days. 3. Streams processing (Kafka Streams framework) is used to transform data into processed data made available to consumers through “Published Topics” 4. Kafka Published Topics retain data long term (in line with retention policies and GDPR) and can be used by many Speed Layer Microservices. 5. Speed Layer Microservices are consumers of Kafka Published Topics and push the data they need into their persistence store (NoSQL, in-memory, etc.) 6. APIs expose data to consumers 7. Channel applications call Speed Layer Microservices to request data 8. Note, applications can subscribe to events and respond to events without materialising them in a database, e.g., push notification to device. 124 5 3 6 Consuming Applications 7 8 Data Flow Diagram
- 9. 9 There are three main approaches for consumption of data from Speed Layer. 1. Immediate real time message consumption in the Event Driven pattern, 2. Usage specific data sets are materialised and exposed through APIs in the Request Driven pattern. 3. Functionally aligned enterprise level data stores are materialised. Enterprise/ Functional microservices Consumption Ms & apps Consumers are microservices that subscribe to topics and materialise data to their requirements. A set of functional microservices are created. For example an “account” microservice from which all consuming microservices and applications read account data when needed. Kafka consumers listen and respond to messages that are arriving in near real time and take immediate action on receipt of the message. In this pattern there is no need to materialise the data. SL subs Producer Producer Producer Producer SL subs Producer Producer Producer Producer SL Producer Producer Producer Producer FUNCTIONAL SERVICEEVENT DRIVEN REQUEST DRIVEN Legacy applications and/or services can be re-written to consume data from the Speed Layer to improve performance and reduce compute demand from other systems. Consumption Patterns Overview
- 10. 10 Multi-site deployment and resilience Primary DC for SORs Standby DC for SORs Cloud hosting Deployment 1. CDC writes to a local Kafka Cluster, i.e., in the same DC as the mainframe 2. Kafka topics are replicated to a separate Kafka cluster in our 2nd DC 3. Independent database clusters in each datacentre. 4. When required, Kafka topics are replicated using Confluent Replicator to cloud providers
- 11. Progress so far… • Architectural PoC completed: 1. Initial logical proving 2. Functional and non functional proving 3. Load testing/benchmarking in Azure and IBM labs • Speed Layer project launched to deliver the production capability and first use cases 1. Split into 3 use cases, with the first one code complete, 2 & 3 progressing well • Adopting Confluent Kafka across multiple LOBs 1. Speed Layer 2. Event Based designs for originations journeys 3. High volume messaging in Payments • Working on Streaming Maturity Assessment with Confluent
- 12. Adopting an Enterprise Event-Streaming Platform is a Journey Nationwide nearly here - with Speed Layer + platforms for Mortgages & Payments - but more potential to share common ways of working and utilise a common platform for more use cases VALUE 1 Early interest 2 Identify a project / start to set up pipeline 3 Mission-critical, but disparate LOBs 4 Mission-critical, connected LOBs 5 Central Nervous System Projects Platform Developer downloads Kafka & experiments, Pilot(s). LOB(s); Small teams experimenting; → 1-3 basic pipeline use cases - moved into Production - but fragmented. Multiple mission critical use cases in production with scale, DR & SLAs. → Streaming clearly delivering business value, with C-suite visibility but fragmented across LOBs. Streaming Platform managing majority of mission critical data processes, globally, with multi-datacenter replication across on-prem and hybrid clouds. All data in the organization managed through a single Streaming Platform. Typically → Digital natives / digital pure players - probably using Machine Learning & AI.
- 13. Expected value (this time next year) Enables agility and autonomy in digital development teams The first use case alone will remove c7bn requests / year from the HPNS. Will help us maintain our service availability despite unprecedented demand Kafka and streaming being adopted across multiple lines of business The move to micro services with Confluent Kafka enables Nationwide to onboard new use cases quickly and easily Speed Layer, Streaming and Kafka will help Nationwide head-off the threat from agile challenger banks The Speed Layer will help Nationwide provide customers with a better customer experience leading to better customer retention and new revenue streams.
- 14. 14 Demo of Speed Layer Why we did the Proof of Concept Functional walk through Non-functional view
Editor's Notes
- Good morning all and thanks for joining this web cast I’m Rob Jackson HO Application Architecture for Nationwide and also from Nationwide we have Pete Cracknell who’ll be doing a demo for you today. Today we’re going to talk to you about an architecture we’ve called the “Speed Layer”. You might be familiar with the concept of a Speed Layer from Lambda Architectures and the world of Data architecture, but this isn’t that, it’s just a name that stuck, so sorry for any confusion we’ve caused with the name… I talk to you about the reasons why we’re doing this architecture, contrast it with our current state architecture Describe how we can consume data from the Speed Layer A bit about how it’s deployed Where we’re heading next The best bit is the demo and then we’ll do a Q&A with Tim (Vincent) from Confluent. I hope that’s ok!
- Formed in 1884 Renamed to the name we are now in 1970 Worlds largest building society In the UK we’re a major provider of mortgages, loans, etc. Launched our first IB in 1997 and first or second Recently announced a large investment of 4.1bn What I’m talking to you about today forms an important part of that investment
- I think the main headline for why we’re doing the speed layer is “digital disruption” Some might not see Open Banking as digital disruption as it’s a regulatory requirement However, it means we have to expose our data through APIs, and if we don’t offer good digital services through our own apps, customers will use other banks and organisations apps that use our APIs to disintermediate us. The other reason to mention Open Banking is that it was the catalyst to work on the Speed Layer. OB, had the potential for high and unpredictable read volumes along with stringent requirements for availability. We knew the other CMA9 were building OB with similar data caches to protect their core SORs from this load and we intended to do the same. I’m sure you’ll recognise the other headings there: higher volumes, 24x7 expectations, people expecting to use their data in new ways, e.g., how easy is it to search your emails vs. your bank’s transaction history? The final point is that, despite all the good work we do with them, our core ledgers do not make it easy to use data in new ways, aggregate multiple data sources, push events to customers and scale cost effectively.
- Speed Layer one of the answers to Digital Disruption… First looking at the definition: It’s a source of data that can be queried for a near real-time copy of mainframe data. On top of that it introduces event sourcing and stream processing into the society. It’s built using modern technologies: Confluent Kafka, MongoDB, Microservices, OpenShift and will initially be deployed on-prem, but is very much an enabler for cloud – which I’ll come back to later. The 4 key characteristics: Resilient: the application is designed to tolerate infrastructure failure with build in data redundancy, horizonal scaling and automated recovery. Fast and agile: once we’ve extracted data from SORs, we can allow consumers to join, aggregate, structure, query and search data in ways the SORs do not easily allow. Rich Data set: Allows is to enrich SOR data with analytics, 3rd party sources, etc. And Scalability: scalability is designed in to the technologies we’re using. Kafka and MongoDB are both heavility used in internet scale deployments. My favourite statistic for Kafka is that Ali Baba use it to source events at a peak rate of 425 million TPS. A big number for us is a small proportion of that.
- This isn’t really our current state, it’s just a representative sample of one small part of our estate and fairly common. But it allows me to describe a fairly normal transaction path A REST/http request for some data comes in from a device on the internet That hits an API Gateway in our datacentre, that then makes onward http requests until it eventually finds it’s way to the data in the mainframe If any of those layers is unresponsive the request will time out And of course, if the mainframe is not available, it doesn’t work at all. Thankfully, that doesn’t happen very often. If we want to move any of those components out into the cloud, that’s ok, but they still have to call back into our data centre to get to the mainframe. So, it’s not wrong and it’s how write requests will continue, perhaps with a simplified estate, fewer layers and modern technologies. However, for read requests, we can do something different.
- This shows how Speed layer for reads and event sourcing will sit alongside our enterprise middleware A write comes into the SOR by existing means: batch, middleware, legacy services, payments gateway, whatever… It’s picked up by CDC and pushed into Kafka where it’s processed and stored before being materialised, in our case that’s MongoDB. Using this pattern, read requests are removed from our SORs or even our Data Centres, replicated to where it’s needed and materialised to requirements. Going into that in a bit more detail…
- I’ll just talk you through the data flow… Step 1 is CDC on the mainframe. Of course, this enables multiple data sources, for example, batch files using Kafka Connect, applications creating events, but for us right now, it’s Change Data Capture on our Mainframes. So that’s how it works, next we’ll look at how consumers use it.
- These show the ways in which we can consume data from the speed layer There are cases for all of these, but I’m very much looking forward to seeing the first 2 come to fruition Event driven – consumers subscribe to topics and act on events. Requst driven – data is materialised to requirements Functional – these are our cored shared services we expect to be re-used.
- This shows how SL is an enabler for cloud and a good place to describe how resilience is baked into the architecture. Pete will show this for real during the demo.
- Slide 10
- Slide 11 We’re now getting into next steps We’re about to embark on a streaming assessment with Confluent’s help. We at around step 3, we’re using kafka, streaming, in SL, Mortgage and payments, but we’re currently doing things slightly differently on different platforms We want to look at new use cases, new demand and what capabilities we need to create to support that demand. This will feed into the various roadmaps, including the SI squad but also the IT Strategy.
- Final slide before questions.
- Pete was heading up the architectural proving team when we did this I approached him with an architecture I wanted to prove and I think Pete’s approach was to try to break it. He’ll let you know how he got on in the Q&A He can also cover the alternatives we looked at for stream processing and maybe some of the stuff we learnt along the way.