Watch this webinar in full here: https://buff.ly/2IxM8Iy Watch all webinars from the Denodo Packed Lunch webinar series here: https://buff.ly/2IR3q6w While big data initiatives have become necessary for any business to generate actionable insights, big data fabric has become a necessity for any successful big data initiative. The best of breed big data fabrics should deliver actionable insights to the business users with minimal effort, provide end-to-end security to the entire enterprise data platform and provide real-time data integration, while delivering self-service data platform to business users. Attend this session to learn how big data fabric enabled by data virtualization: • Provides lightning fast self-service data access to business users • Centralizes data security, governance and data privacy • Fulfills the promise of data lakes to provide actionable insights

1. DATA VIRTUALIZATION PACKED LUNCH
WEBINAR SERIES
Sessions Covering Key Data Integration Challenges
Solved with Data Virtualization

2. Big Data Fabric: A Necessity For Any
Successful Big Data Initiative
Naren Sankaran
Sales Engineer, Denodo
Paul Moxon
VP Data Architectures & Chief Evangelist, Denodo

3. Agenda
1. The Reality of Data Lakes
2. Big Data Fabric
3. Customer Case Study – Logitech
4. Demo
5. Q&A
6. Next Steps

4. A Modern Data Ecosystem
4

5. The Promise of the Data Lake
5

6. …Data lakes lack semantic consistency and governed
metadata. Meeting the needs of wider audiences
require curated repositories with governance, semantic
consistency and access controls.”
6

7. Data Lake (System of Insight)
Information Management and Governance
Data Lakes and Big Data Fabric
8
1
Multiple repositories, organized based on
source and usage; hosted on appropriate
data platform for workload
1
Catalog of data, ownership, meaning and
permitted usage
2
No direct access to repositories3
Curation of all data to define meaning and
classifications
4
Business-led information governance and
management
5
Active monitoring and management of data6
Data-centric security7
Access to raw data to develop new
production analytics
8
Moderated, view-based self-service access to
data and analytics for Line of Business
9
2
3
4 5
6
7
8
9

8. Data Lake (System of Insight)
Information Management and Governance
Data Lakes and Big Data Fabric
9

9. Forrester’s Big Data Fabric
10

10. Big Data Fabric and Data Virtualization
11 11
Data Virtualization

11. Data Virtualization Reference Architecture
12
Data Access
Security
Governance and Metadata management
Data Warehouse
Unstructured Data
Structured Data
RDBMS
Excel
Flat Files
XML
Email
Sensors (IIoT)
Social Media
RFID
Wearables
Storage
Compute
RDBMS
Big Data Lakes
noSQL
IMDG
Data Ingestion
Real Time/Streaming
Batch
CDC
Metadata
Enrichment
Search and Index
Data Virtualization
Data Services
Data Insight
Data Mining
Dashboards
Data Discovery
and Self-Service
Reporting
DATA
VIRTUALIZATION

12. Data Virtualization Reference Architecture
13
Data Access
Security
Governance and Metadata management
Data Warehouse
Unstructured Data
Structured Data
RDBMS
Excel
Flat Files
XML
Email
Sensors (IIoT)
Social Media
RFID
Wearables
Storage
Compute
RDBMS
Big Data Lakes
noSQL
IMDG
Data Ingestion
Real Time/Streaming
Batch
CDC
Metadata
Enrichment
Search and Index
Data Virtualization
Data Services
Data Insight
Data Mining
Dashboards
Data Discovery
and Self-Service
Reporting
DATA
VIRTUALIZATION

13. Customer Case Study – Logitech
• Logitech also needed more agile (and cost effective) analytics platform for
customer analytics
• Moved analytics to AWS – utilized many different storage and compute capabilities
on AWS
• Redshift, Snowflake for EDW, RDS for relational data, S3 for data ingestion
• Spark, EMR, NLP for analytics
• Used Data Virtualization as data access layer
• Users could access data irrespective of storage or processing capability
• Data Virtualization Layer provided data security/access permissions
• Abstracted security from different data sources
• Data governance also provided by Data Virtualization Layer
• Data lineage, metadata management, data catalog and discovery, etc.
14

14. Customer Case Study – Logitech
15

15. Customer Case Study – Logitech
16

16. Product Demonstration
17
Sales Engineer, Denodo
Naren Sankaran

17. 18
Inventory Summary
INVENTORY_SUMMARY is a join between:
• REDSHIFT_ITEM
• REDSHIFT_ DATE_DIM
• IMPALA_INVENTORY
Join Conditions:
• impala_inventory.inv_item_sk = redshift_item.i_item_sk
• impala_inventory.inv_date_sk = redshift_date_dim.d_date_sk
Group By:
• i_item_desc
• i_brand
• d_year
Output:
• Add field “total” which sums the inv_quantity_on_hand

18. 19
Performance
Examine execution plan of view inventory_summary
Compare Execution Plans
• Example of the combination of data movement
to an MPP for query acceleration.
• In this case, multiple tables are moved to
Hadoop in parallel
• Static optimization also provide an aggregation
pushdown to the Hadoop cluster
• Execution time with optimization: 28408 ms
• Execution time w/out optimization: over 900
seconds
These strategies dramatically
reduces the data volumes
transferred through the network,
while pushes down to Impala most
of the "heavy lifting"
From VQL Shell Execute:
▪ DESC QUERYPLAN select * FROM inventory_summary context(‘datamovement' = 'off’)
▪ DESC QUERYPLAN select * FROM inventory_summary

19. Demo
20

20. Summary
• Big Data initiatives can add deep analytical insight to help
organizations find new opportunities or provide more efficient
services
• When defining your Big Data initiative, think about how users will
find and access the data
• A Big Data Fabric is key to making this easy
• And making the initiative successful
• Data Virtualization is the right technology for an agile and flexible
Big Data Fabric
21

21. Q&A

22. Next steps
Download Denodo Express:
www.denodoexpress.com
Access Denodo Platform in the Cloud!
30 day FREE trial available!
Denodo for Azure:
www.denodo.com/TrialAzure/PackedLunch
Denodo for AWS: www.denodo.com/TrialAWS/PackedLunch

23. Next session
Self-Service Analytics with Guard Rails
Thursday, June 21, 2018 at 11:00am (PST)
Paul Moxon
VP Data Architectures and Chief Evangelist

24. Thank you!
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