The document discusses how AWS data lakes and analytics services can help organizations store and analyze large amounts of data at scale. It provides examples of how Epic Games uses AWS services like S3, Kinesis, EMR and DynamoDB to collect telemetry data from Fortnite players, perform real-time analytics and power game design decisions with data-driven insights. It also summarizes how a bank in Latin America leveraged AWS APIs, Fargate, S3 and other services to modernize its digital offerings and call center automation.

1. 2 0 1 9

2. Data Lakes & Analytics en
AWS
Luis Caro
Solutions Architect

3. Data is a strategic asset
for every organization
The world’s most valuable
resource is
*Copyright: The Economist, 2017, David Parkins

4. Thinking about data as an asset, not a
cost
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Stop
throwing
data away
Make it
available to
more users
Arm users
with more
data processing
technologies

5. A data lake is a centralized repository that
allows you to store all your structured and
unstructured data at any scale

6. Store exabytes of data
Stage from landing dock to transformed to curated–Make
available in each
Load, transform, and catalog once
Make data available to many tools
Open formats and interfaces support innovation
Snowball
Snowmobile Kinesis
Data Firehose
Kinesis
Data Streams
Amazon S3
Amazon
Redshift
Amazon
EMR
Athena
Amazon
Kinesis Amazon
Elasticsearch
Service
Data lakes help you cost-effectively scale
Kinesis
Video Streams
AI Services
Amazon
QuickSight

7. AWS databases and analytics
Broad and deep portfolio, built for builders
AWS Marketplace
Amazon Redshift
Data warehousing
Amazon EMR
Hadoop + Spark
Athena
Interactive analytics
Kinesis Analytics
Real-time
Amazon Elasticsearch service
Operational Analytics
RDS
MySQL, PostgreSQL, MariaDB,
Oracle, SQL Server
Aurora
MySQL, PostgreSQL
Amazon
QuickSight
Amazon
SageMaker
DynamoDB
Key value, Document
ElastiCache
Redis, Memcached
Neptune
Graph
Timestream
Time Series
QLDB
Ledger Database
S3/Amazon Glacier
AWS Glue
ETL & Data Catalog
Lake Formation
Data Lakes
Database Migration Service | Snowball | Kinesis | Managed Streaming for Apache Kafka | Direct Connect | Transfer for SFTP
Data Movement
AnalyticsDatabases
Business Intelligence & Machine Learning
Data Lake
Managed
Blockchain
Blockchain
Templates
Blockchain
Amazon
Comprehend
Amazon
Rekognition
Amazon
Lex
Amazon
Transcribe
AWS DeepLens 250+ solutions
730+ Database
solutions
600+ Analytics
solutions
25+ Blockchain
solutions
20+ Data lake
solutions
30+ solutions
RDS on VMWare

8. More data lakes and analytics than anywhere else
More than 10,000 data lakes on AWS

9. 200+ million players
Data provides a constant feedback loop
for game designers
Up-to-the-minute analysis of gamer
satisfaction to drive gamer engagement
Resulting in the most popular
game played in the world
Fortnite

10. Epic Games uses Data Lakes and analytics
Entire analytics platform running on AWS
S3 leveraged as a Data Lake
All telemetry data is collected with Kinesis
Real-time analytics done through Spark on EMR,
DynamoDB to create scoreboards and real-time queries
Use Amazon EMR for large batch data processing
Game designers use data to inform their decisions
Game
clients
Game
servers
Launcher
Game
services
N E A R R E A L T I M E P I P E L I N E
N E A R R E A L T I M E P I P E L I N E
Grafana
Scoreboards API
Limited Raw Data
(real time ad-hoc SQL)
User ETL
(metric definition)
Spark on EMR DynamoDB
NEAR REALTIME PIPELINES
BATCH PIPELINES
ETL using
EMR
Tableau/BI
Ad-hoc SQLS3
(Data Lake)
Kinesis
APIs
Databases
S3
Other
sources

12. ¿Quiénes somos?
35años Estados Unidos
México
Nicaragua
Costa Rica
República
Dominicana
Panamá
El Salvador
Guatemala
Honduras
Colombia
Trabajando en la región
10Países

13. +300 Clientes SatisfechosOficinas y Sucursales
+900mil
Millones en depósitos
$3mil
Préstamos
+700mil

14. +20mm
De transacciones
mensuales
64%
Transacciones por
canales electrónicos

15. ¿Cómo
llegamos
a la

16. Feb
2018
Nov
2018
Dic
2018
Oct
2019
API Gateway
Integramos nuestros formularios
Web con el Backoffice del Banco,
hoy 15% de nuestras ventas
retail son electrónicas
API Gateway
Integramos nuestros Call centers con la
misma api, hoy procesamos 2 millones
de llamadas al año
Fargate
Automatizamos 55% de las llamadas
de Callcener usando RPA, hoy
tenemos más de 300 robots que
automatizan tareas de call center y
pruebas de Bancanet
S3 Presigned URL
Usamos S3 para nuestra tarjeta
prepago virtual, esperamos
procesar más de 100 mil
solicitudes en línea en los
próximos meses
Ene
2019
Kinesis/Elasticsearch
Analizamos en tiempo real logs de
Bancanet, hoy analizamos 2 millones
eventos al día
¿Cómo llegamos a la nube?

17. Feb
2018
Nov
2018
Dic
2018
Oct
2019
API Gateway
Integramos nuestros formularios
Web con el Backoffice del Banco,
hoy 15% de nuestras ventas
retail son electrónicas
API Gateway
Integramos nuestros Call centers con la
misma api, hoy procesamos 2 millones
de llamadas al año
Fargate
Automatizamos 55% de las llamadas
de Callcener usando RPA, hoy
tenemos más de 300 robots que
automatizan tareas de call center y
pruebas de Bancanet
S3 Presigned URL
Usamos S3 para nuestra tarjeta
prepago virtual, esperamos
procesar más de 100 mil
solicitudes en línea en los
próximos meses
Ene
2019
Kinesis/Elasticsearch
Analizamos en tiempo real logs de
Bancanet, hoy analizamos 2 millones
eventos al día
¿Cómo llegamos a la nube?
https://www.lafise.com/bancanet

18. Kinesis
Analytics
Amazon Simple
Notification Service
Salesforce
Service Cloud
Amazon
S3
Kinesis
Agent
Kinesis
Data Stream
Kinesis
Firehose
Amazon
Elasticsearch
AWS
Kibana
Amazon
Cognito
Monitoreo en
Tiempo Real
Detección de
Anomalías
Datos
Geoespaciales
On Premise
60 Segundos
ó 1Mb

19. Kibana

21. ¿Cómo

22. 5% 5%
6%
8%
15% 14%
17%
18%
19%
21%
Ene Feb Mar Abr May Jun Jul Ago Sep Oct
50%
56%
63%
70%
69%
71% 72%
72%
41% 47%
Uso de Bancanet 3.0
(% De los clients totales que usaron la plataforma)
Usuarios Móviles

23. 3.99%
3.54%
2.76%
1.40%
1.77%
0.86% 0.74% 0.66% 0.96% 0.69% 0.72%
94%
95%
96%
97%
98%
99%
100%
Ene Feb Mar Abr May Jun Jul Ago Sep Oct Nov
Errores en la Plataforma
(% de eventos de error versus el resto)

25. Store exabytes of data
Stage from landing dock to transformed to curated–Make
available in each
Load, transform, and catalog once
Make data available to many tools
Open formats and interfaces support innovation
Snowball
Snowmobile Kinesis
Data Firehose
Kinesis
Data Streams
Amazon S3
Amazon
Redshift
Amazon
EMR
Athena
Amazon
Kinesis Amazon
Elasticsearch
Service
Data lakes help you cost-effectively scale
Kinesis
Video Streams
AI Services
Amazon
QuickSight

26. AWS Lake Formation
• Build, secure, and manage a data lake in days
Build a data lake in days,
not months
Build and deploy a fully
managed data lake with a few
clicks
Enforce security
policies across multiple
services
Centrally define security,
governance, and auditing policies in
one place and enforce those policies
for all users and all applications
Combine different
analytics approaches
Empower analyst and data scientist
productivity, giving them self-
service discovery and safe access to
all data from a single catalog

27. DEMO

29. Califique nuestra charla

30. GRACIAS