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
Denodo DataFest 2016: The Role of Data Virtualization in IoT Integration
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
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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
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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
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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.
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11. How does Denodo support IoT
Data Integration?
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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,…)
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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
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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
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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
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
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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.
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