Watch the full session: Denodo DataFest 2016 sessions: https://goo.gl/DOrhiA Connected use cases are gaining momentum! Data integration is the foundation for enabling these connections. In this session, you will experience first-hand our customer case studies and implementation architectures of IoT solutions. In this session, you will learn: • The role of data virtualization in enabling IoT use cases • How our customers have successfully implemented IoT solutions using data virtualization • How our product complements other IoT technologies This session is part of the Denodo DataFest 2016 event. You can also watch more Denodo DataFest sessions on demand here: https://goo.gl/VXb6M6

1. O C T O B E R 1 8 , 2 0 1 6 S A N F R A N C I S C O B A Y A R E A , C A
#DenodoDataFest
RAPID, AGILE DATA STRATEGIES
For Accelerating Analytics, Cloud, and Big Data Initiatives.

2. The Role of Data Virtualization in IoT
Integration
Lakshmi Randall
Head of Product Marketing, Denodo
Twitter: @LakshmiLJ

3. Agenda
1.What’s so important about IoT Integration?
2.How does Denodo support IoT Data Integration?
3.Customer Case Study
3

4. IoT is invading our kitchen!
4

5. What’s so important about IoT
Integration?
5

6. The Importance of IoT Integration
 Investment in IoT devices is soaring
 IoT is proliferating across all business and
consumer sectors
 Data generated in the IoT offers a Data
Monetization Model
6

7. IOT Investment
 2016: IoT hardware purchases surpass
$2.5 million per minute.
 2021: one million IoT devices procured
and installed per hour.
IoT Proliferation
 2018: 6 billion connected devices
require support.
 2020: >21 billion connected devices
in operation.
 2020: industry-specific devices number
2.9 billion (nearly 200% growth since
2015).
IoT Investment & Proliferation Milestones
7
Source: Gartner 2016

8. IoT Monetization
 Enhance traditional products with sensors
and connectivity
 Offer bundled services for connected things
(e.g., connected cars)
 Collect, Aggregate, Anonymize and
Monetize.
8

9. IoT Use Cases
9
Preventative &
Proactive
Maintenance
Data Monetization
(information-
oriented products
& services)
Customer
Satisfaction &
Retention
Operational
Efficiency (asset &
equipment
optimization)
Safety Security
Fraud Detection
Real-time
Analytics
Patient care

10. IoT Endpoints for 2020 by Sector
10
Source: Gartner, April 2016

11. How does Denodo support IoT
Data Integration?
11
Data-in-transit and Data-at-rest

12. Big Data Connectivity
BigData and Cloud Databases Connectivity
■ Hadoop Ecosystem:
■ SQL on Hadoop: Hive, Impala, Presto,…
■ HDFS, Parquet, Avro, CSV…
■ Execution of map/reduce Jobs
■ Certified with major Hadoop distributions
■ In-memory platforms: Apache Spark, Presto DB, HANA,…
■ Parallel DWs and Appliances: Vertica, Impala, Teradata, Greenplum,…
■ Cloud RDBMS: Redshift, Snowflake, DynamoDB,…
■ NoSQL (MongoDB, CouchDB, Neo4J, Redis, Oracle NoSQL, Cassandra, etc.)
■ Streaming data (Spark streams, Splunk, IBM Streams, Kafka,…)
12
Enhanced Adapters for Big Data ecosystem

13. 13
Request-Response:
Named adapters for stream
services:
 Kafka
 IBM Streams
Streaming:
Extend current JMS support
with:
 Enhanced support for
temporary windows
 Support for MQTT
Enhanced Integration with IoT - Streaming
Enhanced Adapters for the Internet of Things Ecosystem
JMS
MQTT
JMS
MQTT

14. Data Ingestion
■ Batch, On-demand and Streaming Data
Ingestion
■ Simultaneously supports Batch and
Streaming data integration
■ Learns to extract structured data from
semi-structured content using Machine
Learning
■ Ingest the data in a schema-agnostic way
including schema-on-read and multiple
schemas
14
Batch, On-demand and Streaming Data Ingestions

15. Enrich Machine Data and Combine with Other Data
Ingest, Integrate & Deliver
Persisted
(In-memory, Hadoop)
Streams
(specific time window)
Message Queue
Machine-generated/Event data Alerts
Workflows
Operational
Processes
Analytical
Processes
Consumers
Visualization
Data Virtualization
Enrich and Combine IoT
Data with Other Data
Historians
Streams
ERP/SCM
DW
Analytical
DB
MDM
Apps
Data
Marts
Hadoop NoSQL

16. 16
Security
Data in Motion – secure channels
• Using SSL/TLS
• Client-to-Denodo and Denodo-to-source
• Available for all protocols (JDBC, ODBC, ADO.NET and WS)
Data at Rest – secure storage
• Cache: third party database. Can leverage its own encryption mechanism
• Swapping to disk: serialized temporarily stored in a configurable folder that can be encrypted by the OS
Encryption/Decryption and Data Masking
• Support for custom decryption for files and web services
• Transparent integration with RDBMs encryption
Authentication and Authorization
• LDAP/AD, Kerberos support, Granular data security,
Securing data

17. Customer Case Study
17

18. Leading Construction Manufacturer - Telematics &
Predictive Maintenance
Dealer
Maintenance
Parts Inventory
OSI PI Hadoop Cluster
Tableau: Dealer / Customer Dashboard

19. Business Benefits
 Improved asset performance and proactive maintenance.
 Reduced warranty costs due to proactive maintenance of
parts preventing parts failure.
 Optimized pricing for services and parts among global service
providers.
 New Business Model opportunities based on real-time
analysis of detailed sensor data.

20. Data Virtualization Benefits
Implement a Single Logical Data Lake Using Data Virtualization
Improves the
enterprise func-
tionality of data
lakes by
combining one or
more physical
data lakes with
other enterprise
data
Provides a way to
access data from
separate systems
through an
abstraction layer
that makes it
appear as if the
data were in a
single data lake
Improves an
organization’s
ability to govern
and extract more
value from its
data lakes by
extending them
as logical data
lakes
20

21. Key Takeaways
 Identify if and how IoT data will benefit your organization
 Identify your potential IoT Data sources
 Employ Data Virtualization to combine IoT data with other data to
enhance the use and value of data assets
 Employ a Logical Data Lake/Logical Data Warehouse architecture to
eliminate the cost of storing information in multiple places, to
govern IoT data access, and to prevent IoT data from becoming
siloed.
21

22. Q&A

23. Thank you!
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O C T O B E R 1 8 , 2 0 1 6 S A N F R A N C I S C O B A Y A R E A , C A
#DenodoDataFest