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1. 1
Webinar:
Automation of Data Warehouses / Lakes / Stores
About the speaker
Our competencies
Company stats
This webinar
begins shortly
Petr Hájek
Senior Advisor
Information Management
SOFTWARE DEVELOPMENT
APPLICATION OUTSOURCING
ENTERPRISE INTEGRATION
BUSINESS INTELLIGENCE/DWH
BIG DATA AND DATA SCIENCE
22+ yrs.
On the
tech market
since 1998.
Prague
Headquarters
at cenrte of
Europe.
500+
Experienced
and enthusiastic
professionals.
Top 3
CAD company
in Czech Republic
(IDC study).
28M €
Company
revenue in
2019.
Multiple areas
Clients from
Finance, Insurance
and Telco industry.
– Profinit Data Management
Competency Senior Advisor
– 20+ years of professional
experience in Data Management,
Data Warehousing and Business
Intelligence
– Data Architect, Solution Architect,
Management Consultant
– Citigroup, KPMG

2. Petr Hájek February 3, 2021
Webinar:
Automation of Data Warehouses /
Data Lakes / Data Stores

3. About the Lecturer
› Profinit Data Management
Competency Senior Advisor
› 20+ years of professional
experience in Data Management,
Data Warehousing and Business
Intelligence
› Data Architect, Solution Architect,
Management Consultant
› Citigroup, KPMG
Petr Hájek
Seasoned and Windswept
Information Management
Professional

4. 4
About Profinit
ISO 9000 ISO 27000 ISO 20000
Our competencies
Company stats
SOFTWARE DEVELOPMENT
APPLICATION OUTSOURCING
ENTERPRISE INTEGRATION
BUSINESS INTELLIGENCE/DWH
BIG DATA AND DATA SCIENCE
22+ yrs.
On the
tech market
since 1998.
Prague
Headquarters
at cenrte of
Europe.
500+
Experienced
and enthusiastic
professionals.
Top 3
CAD company
in Czech Republic
(IDC study).
28M €
Company
revenue in
2019.
Multiple areas
Clients from
Finance, Insurance
and Telco industry.
Certifications, culture & quality
50+
We serve
many prominent
world clients.
A long history of technical engineering excellence has lead western
companies to rely heavily on skills and expertise from the Czech
Republic. We are proud of the quality of our services and the certificates
ISO 9001, ISO 27001, ISO 20000, PRINCE 2, underpinning our
commitment to provide high quality sustainable services.

5. 5
What are the specifics of “Data Oriented Solutions” ?
› Data Warehouses (DWH)
› Operational Data Stores (ODS)
› Data Lakes (DL)
› Accummulation of Large and
Historical Data from
Heterogenous Sources
› High Complexity & Robustness

6. 6
Data oriented solutions are usually:
UNDOCUMENTED FRAGILE
COMPLICATED

7. 7
What are the major challenges?
› Manual coding
› Too many people to
be organized
› Lack of transparency

8. 8
Reduce the complexity: elementary building blocks
Data Oriented Solutions are:
orchestrated steps of transformations and storage of data
Data Transformation Data Transformation Data Transformation Data

9. 9
Decomposition to Metadata / Rules / Templates
Metadata Templates
Rules

10. 10
Generating scripts like printing envelopes
Everything that
is variable (dynamic)
will be written in the
form of metadata.
Everything that
is repeated (static)
will be prepared in
templates.
Metadata Templates
Rules
Generator

11. 11
Framework
FRAMEWORK DATA SOLUTION
Metadata Templates
Rules
Smart
Automation
Reverse
engineering

12. 12
Investment into the framework
TIME &
COSTS
SCOPE, QUANTITY, COMPLEXITY, ROBUSTNESS
FRAME
WORK
SETUP
Gartner:
200
%
-
400
%
productivity
increase
Source:
Automating
Data
Warehouse
Development,
2020,
G00465794

13. 13
Transformations Patterns
Technical
› Surrogate key assignment
› Consolidation (Deduplication)
› Historization
Mapping
› 1:1
› Column level mapping
› Join & Lookup
› Aggregation
› Union

14. 14
Maintain the framework and automate the lifecycle!
BUSINESS
ANALYST
TECHNICAL
ANALYST
FRAMEWORK
ENGINEER
OPERATIONS
& SUPPORT
BUSINESS
USERS
Business
specs
Prepare
METADATA

15. 15
Enable agile development
BUSINESS
ANALYST
TECHNICAL
ANALYST
BUSINESS
USER
SQUAD
5
BUSINESS
ANALYST
TECHNICAL
ANALYST
BUSINESS
USER
SQUAD
4
BUSINESS
ANALYST
TECHNICAL
ANALYST
BUSINESS
USER
SQUAD
3
BUSINESS
ANALYST
TECHNICAL
ANALYST
BUSINESS
USER
SQUAD
2
FRAMEWORK
ENGINEER
OPERATIONS
& SUPPORT
BUSINESS ANALYST
TECHNICAL ANALYST
BUSINESS USER
SQUAD
1

16. 16
Case Study – DWH for Gambling Industry Regulation
 Data from ~80 public gambling companies loaded every 8 hours
 Maintaining over 25 TB of data in >100.000 database tables
 First version delivered in 5 months by a team of 5 engineers
 99.9 % of SQL code automatically generated
 "DATA_FRAME" - DWH Automation methodology & toolset

17. 17
Advantages of Smart Data Automation
We do the analysis by reusing pre-defined patterns
The result of the analysis are always both human and machine-readable
We foster prototyping
We enjoy the „license to make mistakes“ at almost no cost
We provide immediate feedback in case of errors in the designed metadata
We eliminate manual coding
We streamline the whole development lifecycle and minimize time-to-market
We enable data lineage tracking even before the solution is implemented

18. 18
Traditional Approach vs. Smart Automation
UNDOCUMENTED FRAGILE
TRANSPARENT AGILE
COMPLICATED
ORGANIZED

19. 19
Questions?

20. 20
Webinar:
Automation of Data Oriented Solutions
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Contacts
www.bigdataforbanking.com
linkedin.com/company/profinit
petr.hajek@profinit.eu
www.profinit.eu
Petr Hájek
Senior Advisor
Information Management

21. Profinit EU, s.r.o.
Tychonova 2, 160 00 Prague 6 | Phone + 420 224 316 016
Web
www.profinit.eu
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