The document discusses the challenges, solutions, and benefits of big data in corporate and investment banking, specifically highlighting Santander's infrastructure and regulatory requirements. It describes the complexities of integrating over 1000 systems while maintaining data integrity and organization through ontologies and views. Additionally, it outlines the architecture and design patterns employed to manage data effectively, ensuring compliance and facilitating business operations.

1. A journey to Big Data
Main Challenges, Solutions and Benefits
Corporate & Investment Banking October 2018

2. Who We Are?
2
Santander Highlights
Total assets (EUR trillion)
Gross cutormer loans (EUR billion excluding reverse repos)
Customer deposits + mutual funds (EUR billion excluding repos)
Branches
2017 Attributable profit (EUR million)
H1’18 Attributable profit (EUR million)
Market capitalisation (EUR billion; 26-06-18)
People (headcount)
Customers (millions)
Shareholders (millions)
Communities (million people helped in 2017)
1.43
863
895
13,482
6,619
3,752
74
200,961
140
4.2
2.1

3. Corporate & Investment Banking Business
3
Few contracts – Big profits
Big Clients
Details makes the difference
Strong Competence
Adapt to client systems
Ad hoc integration
Integration layer becomes huge
Lot of middleware
Big Client
Big Client
Big Client
Big Client
Big Client
Big Client
Big Client
Big Client
Big Client
Big Client

4. Legacy Systems Challenges
4
Regulation
Strong Regulation
We cannot simply integrate systems in a row.
We need to consolidate data for regulators
Systems
Lot of Systems
There are more than 1000 different
applications installed on Santander
Corporate & Investment Banking
Auditors
Auditors are Welcome
We need our systems ready to be audited
at any time.
Presence in Many Countries
Same pattern is repeated on each country
Countries

5. Big Data. Data Organization
5
Raw Data
Data is stored ‘as is’ without any
modification.
It is required all data to have its
own metadata
Landing
Data Ontologies
Data is grouped according to
functional criteria (ontologies).
Data is consolidated to
eliminate duplicates
Common
Business Views
Is where applications access
and process the data.
It is not a copy but a view of the
common layer
Business
Applications

6. Finland Architecture. A Thousand Lakes
6
API S3
Landing
(RAW)
Common
(Harmonized) Business (Consolidated & Views)
CDO
BATCH CLUSTER A
worker worker worker worker
Tools
BATCH CLUSTER B
worker worker worker worker
Tools
OTHER
BATCH CLUSTER C
worker worker worker worker
Tools

7. Finland Architecture. On Demand Evolution
7
API S3
Landing
(RAW)
Common
(Harmonized) Business (Consolidated & Views)
CDO
ON DEMAND INSTANTIATION
worker worker worker
Datalake
Distribution
Tools worker worker worker
Datalake
Distribution
Tools
PROCESING AND SAVING DATA
worker worker worker
Datalake
Distribution
Tools
FREE RESOURCES

8. Online Cluster Architecture
8
New
Applications
Legacy
Applications
Online Cache
Business
Rules
Visualization
Spring Data Flow
Manual Orchestation Automatic Choreography
REST API

9. Online Cluster. Design Pattern
9
Source
Stream
Online Cache
Business
Rules
AVRO
AVRO
IDX
AVRO
IDX
AVRO
IDX
AVRO
AVRO
Schema
Registry
AVRO
AVRO
IDX

10. Complete Finland Architecture
10
API S3
Landing
(RAW)
Common
(Harmonized) Business (Consolidated & Views)
CDO
ON PREMISE BATCH CLUSTER
worker worker worker
Datalake
Distribution
Tools
ON DEMAND BATCH CLUSTER
Online Cache
Business
Rules
Visualization
Spring Data Flow
Manual Orchestation Automatic Choreography
REST API
ONLINE CLUSTER
worker worker worker worker
Tools
Datalake
Distribution

11. Our purpose is to help people
and business prosper.
Our culture is based on believing
that everything we do should be:
Thank You.