Watch the full session: Denodo DataFest 2016 sessions: https://goo.gl/Bvmvc9 Data prep and data blending are terms that have come to prominence over the last year or two. On the surface, they appear to offer functionality similar to data virtualization…but there are important differences! In this session, you will learn: • How data virtualization complements or contrasts technologies such as data prep and data blending • Pros and cons of functionality provided by data prep, data catalog and data blending tools • When and how to use these different technologies to be most effective 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. Comparing and Contrasting Data
Virtualization with Data Prep, Data
Blending, Data Catalog and Other
Technologies
Paul Moxon
Head of Product Management, Denodo

3. Agenda
1.Business Intelligence ‘Swim Lanes’
2.Data Prep – What is it and how does it work?
3.All you want to know about Data Blending
4.Data Catalogs – What, When, and How
5.Mapping to the Swim Lanes
6.Where Does Data Virtualization Fit?
7.Q&A
3

4. Business Intelligence ‘Swim Lanes’
4
• Task focused
• Productivity
• Self-service
• Quick and easy access
to data
• Automation (or
simplification) of data
gathering
• Tactical
• Team/Departmental
• Drives business
operations
• Shared data
• Process oriented
• Strategic
• Executive and KPI
dashboards
• Drives strategic
decisions
• Managed, governed
data
• Consistent data

5. Data Prep
What is it and how does it work?
5

6. Data preparation is the process of gathering,
combining, structuring and organizing data so it
can be analyzed as part of business intelligence or
analytics process.

7. Leading Data Prep Vendors
• Trifacta
• Paxata
• Alteryx
• Datameer
• Talend Data Preparation Desktop
• Informatica Rev
• SAS Data Loader
• IBM Watson
7

8. How Does It Work?
Interactive Data Prep process:
1. First data is ingested from data sources (or just a sample of data)
2. The user can define transformations to prepare the data
a. De-duplication, cleansing, combining data, pivoting, splitting rows/columns,
etc.
3. Run the transformation and export the data
a. Local file (typically CSV) or into Hadoop (Hive table or CSV file)
b. Alternatively export to BI Tool (e.g. Tableau Data Extract file)
Operationalize:
1. Schedule data prep transformations to generate new data files (à la ETL)
2. Publish results to collaboration environment
8

9. Data Prep Tools
9

10. Pros:
• Ease of use
• Iterative data transformation
• Very good with delimited files
• Sampling makes tools responsive
• Data profiling help detect ‘suspect’
data
Cons:
• Ad-hoc rather than operational
• Reuse is limited to collaborative data
sets
• Performance
• Consistency and governance – data
chaos?
Pros and Cons of Data Prep
10

11. Data Prep is great for ad-hoc discovery
and analytics
• “I need to combine this with that and run
it through my analytics application…”
Not so good for consistent, repeatable
integration
• (Think: BI swim lanes)
But…
• Data Prep provides valuable knowledge
that can be used in systematic data
integration
Data Prep and Systematic Data Integration
11

12. Data Prep and Virtual Sandboxes
12

13. Data Blending
13
All you want to know about…

14. Data blending is about working with multiple sources of
data by preparing them and joining them together for a
specific use case at a specific time. It’s different from data
integration, because data blending is about solving a
specific use case, whereas data integration typically gives
you a single source of truth…

15. Leading Data Blending ‘Vendors’
• Tableau
• Microstrategy
• SAP Business Objects
• IBM Cognos
• Qlik View
• etc.
15

16. How Does it Work?
Defining the data blending ‘model’:
1. Connect to data sources
a. Databases, Data Warehouse (via ODBC or JDBC), Files (Excel, CSV, etc.),
Hadoop, NoSQL, etc.
2. Select data you want to use – a sample is usually loaded
3. Build model using graphical tool to create Joins, Unions, etc.
4. Run the model for the full data set
5. Build your report or dashboard
Operationalize:
1. Model can be saved and expose as a ‘data source’ (usually in a ‘server’)
2. Accessed by other users
16

17. Data Blending
17

18. Pros:
• Built into BI/visualization tools
• Graphical query designer
• Provides semantic layer on top of
data sources
• Quick time from ‘data to analysis’ i.e.
removes wait for IT to provision a
data mart or similar
Cons:
• Ad-hoc rather than operational
• Specific to each BI/visualization tool
• Performance
• Consistency and governance
Pros and Cons of Data Blending
18

19. Francois Ajenstat, Chief Product Officer, Tableau
There are two flows; the ad-hoc and the operational…where we are
coming from is…I just want to integrate these two sources. It's not
formalized, per se, it's not a project. I just want to connect this and
this and I want to analyze it. How do we go from data to analysis as
quickly as possible? And when you want to formalize it, operationalize
it, make it repeatable, then [you use other tools].
19

20. Data Catalogs
What, when, and how?
20

21. Data Catalogs provide capabilities that enable any user –
from analysts to data scientists to developers – to discover,
understand, and consume data sources. Data Catalogs
typically include a crowdsourcing model of metadata and
annotations, and allow all users to contribute their
knowledge to build a community and culture of data.

22. Leading Data Catalog Vendors
• Alation/Teradata
• Cambridge Semantics Anzo Platform
• Informatica Enterprise Information Catalog
• Microsoft Azure Data Catalog
• Waterline Data
22

23. How Does it Work?
Building catalog:
1. Connect to data sources and consumers
a. Extract and analyze ‘technical’ metadata
b. Sample data and build data profile
2. Use NLP and ML for ‘auto-titling’ – based on defined business glossary
3. Use expert sourcing to validate catalog entries
4. Use crowd sourcing to build veracity profile
Accessing catalog:
1. Search tools for ‘natural language’ searches
2. APIs for tool integration
23

24. Data Catalog
24

25. Pros:
• Great for analyzing data source and
inferring meaning from technical
metadata
• Gather ‘tribal knowledge’ about data
within organization
• Allow curation of metadata
• Provide single tool to find – and
understand - data
Cons:
• Do not address ‘data provisioning’ –
you need another tool for this
• File-based data?
Pros and Cons of Data Blending
25

26. Summary
Back to the swim lanes…
26

27. Business Intelligence ‘Swim Lanes’
27
Data Blending
Data Catalog
Data Prep
Data Virtualization

28. Q&A

29. 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