The document discusses the importance of advanced analytics and machine learning in data processing, emphasizing the role of data virtualization in streamlining data pipelines and enhancing accessibility. It outlines the challenges faced in data science workflows, noting that a significant amount of time is spent on data preparation rather than analysis. The Denodo platform is highlighted as a solution that simplifies access to various data sources, helping organizations leverage data more efficiently for analytics and machine learning.

1. Advanced Analytics and
Machine Learning:
Removing Friction from the Data
Pipeline with Data Virtualization
Alexey Sidorov
Chief Evangelist Middle East & Eastern Europe
June 2020

2. Alexey Sidorov
Chief Evangelist Middle East &
Eastern Europe, Denodo
Speakers

3. 1. What are Advanced Analytics?
2. The Data Challenge
3. The Rise of Logical Data Architectures
4. Tackling the Data Pipeline Problem
5. Real-time Machine Learning with Data Virtualization
6. Key Takeaways
7. Q&A
8. Next Steps
Agenda

4. 4
AI and Machine Learning Needs Data

5. 5
The Economist
The world’s most valuable resource
is no longer oil, but data.

6. 6
Data – New Oil … but Not Easy To Extract & Deliver

7. 7
The Scale of the Problem

8. 8
Confirmation of the Constraints on ML/AI…

9. Logical Data Warehouse
9

10. Logical Data Warehouse Architecture
Reporting
Analytics
Data Science
Data Market Place
Data Monetization
AI/ML
iPaaS
Kafka
ETL
CDC
Sqoop
Flume
RawDataZoneStagingArea
CuratedDataZoneCoreDWHmodel
Data Warehouse
Data Lake
Data Virtualization Platform
Analytical Views
Data Science Views
λ Views
Real-Time Views
DWH Views
Hybrid Views
Cloud Views
UniversalCatalogofDataServices
CentralizedAccessControl
Logical Data Warehouse

11. 11
Gartner, Adopt the Logical Data Warehouse Architecture to Meet Your Modern
Analytical Needs, May 2018
“When designed properly, Data Virtualization can speed data
integration, lower data latency, offer flexibility and reuse, and
reduce data sprawl across dispersed data sources.
Due to its many benefits, Data Virtualization is often the first step
for organizations evolving a traditional, repository-style data
warehouse into a Logical Architecture”

12. Tackling the Data Pipeline Problem
12

13. 13
Typical Data Science Workflow
A typical workflow for a data scientist is:
1. Gather the requirements for the business problem
2. Identify and ingest data useful for the case
3. Cleanse data into a useful format
4. Analyze data
5. Prepare input for your algorithms
6. Execute data science algorithms (ML, AI, etc.)
7. Visualize and share

14. 14
Typical Data Science Workflow
80% of time – Finding and preparing the data
10% of time – Analysis
10% of time – Visualizing data

15. 15
Where Does Your Time Go?
A large amount of time and effort goes into tasks not intrinsically related to data science:
• Finding where the right data may be
• Getting access to the data
• Bureaucracy
• Understand access methods and technology (noSQL, REST APIs, etc.)
• Transforming data into a format easy to work with
• Combining data originally available in different sources and formats
• Profile and cleanse data to eliminate incomplete or inconsistent data points

16. Demonstration
Accelerating the Machine Learning Data Pipeline
with Data Virtualization
16

17. 17
https://flic.kr/p/x8HgrF
Can we predict the usage of the NYC bike
system based on data from previous years?

18. 18
Data Sources – Citibike

19. 19
https://flic.kr/p/CYT7SS

20. 20
Data Sources – NWS Weather Data

21. 21
What We’re Going To Do…
1. Connect to data and have a look
2. Format the data (prep it) so that we can look for significant factors
• e.g. bike trips on different days of week, different months of year, etc.
3. Once we’ve decided on the significant attributes, prepare that data for the ML
algorithm
4. Using Python, read the 2019 data and run it through our ML algorithm for training
5. Read the 2020 data, test the algorithm
6. Save the results and load them into the Denodo Platform

22. Demo
22

23. Key Takeaways
23

24. 24
The Key Ingredient for Advanced Analytics is…Data ☺
Input data for a data science project may come in a variety of systems and formats.
Some examples:
• Files (CSV, logs, Parquet)
• Relational databases (EDW, operational systems)
• NoSQL systems (key-value pairs, document stores, time series, etc.)
• SaaS APIs (Salesforce, Marketo, ServiceNow, Facebook, Twitter, etc.)
In addition, the Big Data community has also embraced data science as one of their
pillars. For example Spark and SparkML, and architectural patterns like the Data Lake

25. 25
Key Takeaways
• Finally…People don’t like to ride their bikes in the cold weather
• The Denodo Platform makes all kinds of data – from a variety of data sources –
readily available to your data analysts and data scientists
• Data virtualization shortens the ‘data wrangling’ phases of analytics/ML projects
• Avoids needing to write ‘data prep’ scripts in Python, R, etc.
• It’s easy to access and analyze the data from analytics tools such as Zeppelin or
Jupyter
• You can use the Denodo Platform to share the results of your analytics with others

26. Q&A

27. Customers
800+ customers
Many F500 & G2000
Offices
Head quarters : A Coruna (Spain) and Palo-Alto
California
Offices: Paris, Munich, London, Madrid, Dubai,
Riyahd …
Denodo
20 years of experience in Data Virtualisation
Recognized as the leader by independent analysts
(Forrester, Gartner)
Many IT Industry rewards and nominations
NEXT STEPS
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drive (1h)
Get Denodo training
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28. Thanks!
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