Lviv IT Arena is a conference specially designed for programmers, designers, developers, top managers, inverstors, entrepreneurs and startuppers. Annually it takes place at the beginning of October in Lviv at Arena Lviv stadium. In 2016 the conference gathered more than 1800 participants and over 100 speakers from companies like Microsoft, Philips, Twitter, UBER and IBM. More details about the conference at itarena.lviv.ua.

1. BI INTHE CLOUDS
Wlodek Bielski
BIArchitect
www.itmagination.com

2. Consultants Capacity(MH/Miesiąc)
Delivered projects Revenue(mUSD)
176
2014
280
2015
5002016
29 568
2014
47 040
2015
840002016
90
2013
174
2014
3762015
6,1
2014
11,1
2015
20,52016
FACTS ■ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - ■

3. SOFTWARE DEVELOPMENT
CLOUD OUTSOURCING MANAGEDSERVICES
 Dedicated software production
 Application integration
 Application migration
 Application maintenance
 Managingthe process of software
development
 .NET, ASP.NET MVC, TFS
 Transition to the Cloud
 Microsoft Azure
 SharePoint Online
 Office365
 Cloud Managed Service
 Outsourcing IT professionals
 Time& Material accounting model
 Nearshoring / Offshoring
 Transparent business model 80/20
 Application Managed Services
 Infrastructure Managed Services
 Help Desk and Maintenance Managed Services
 Nearshoring / Offshoring
PERFORMANCE MANAGEMENT
 Budgeting, planning and forecasting
 Financial Consolidation
 Cashflow Management
 Production Controlling
 Integrated Reporting
 Predefined applications: Solvency II, COREP,
FINREP
 Exclusive distributor of Tagetik in Poland
DATAMANAGEMENT
 Analytical - reporting solutions
 Big Data
 Data Integration
 Data Warehousing
 Business Analytics
 Data Mining
 MobileBI Analytics
 DigitalMarketing Analytics

4. GLOBALDELIVERY ■ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - ■
Onshore
Offshore
International
contracts

5. AGENDA ■ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - ■
 Whatis Cloud BI?
 KeyDrivers of Cloud BI
 Cloud BI Delivery Models
 Data Lake vs Data Warehouse
 CaseStudy
 Conclusions

6. WHATIS BI? ■ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - ■
BI
Data
Integration
Data
Warehouse
Analytics
DataViz
BigData
DataScience

7. KEYDRIVERSOF CLOUD BI ■ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - ■
Speedof implementation and deployment
Instant Devand Test environments
Elasticity –scaling up/down instantly
Focuson corecapabilities –outsource if needed
Moreaffordable
Enhancedself-service capabilities
Available anytime, anywhere
LowerTCO
Less commitments
Cloud-born data

8. CLOUDDELIVERYMODELS ■ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - -- - - ■
On Premises
Storage
Servers
Networking
OS
ServerSoftware
Data
Applications
Youmanage
IaaS
Storage
Servers
Networking
OS
ServerSoftware
Data
Applications
Youmanage
ManagedbyCloud
PaaS
Storage
Servers
Networking
OS
ServerSoftware
Data
Applications
Youmanage
ManagedbyCloud
SaaS
Storage
Servers
Networking
OS
ServerSoftware
Data
Applications
Youmanage
ManagedbyCloud

9. CLOUDDELIVERYMODEL:AWS ■ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - ■
On Premises
Storage
Servers
Networking
OS
ServerSoftware
Data
Applications
IaaS
S3
EC2
VPC
Linux
MySQL
Databases
Applications
PaaS
S3
EC2
VPC
Managed
RDS/Redshift
Databases
Applications
SaaS
Managed
Managed
Managed
Managed
Managed
Managed
QuickSight

10. CLOUDDELIVERYMODEL:AZURE ■ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - ■
On Premises
Storage
Servers
Networking
OS
ServerSoftware
Data
Applications
IaaS
AzureStorage
AzureVM
AzureVNet
WinServ2016
SQL2016Ent
Databases
Applications
PaaS
Managed
Managed
Managed
Managed
SQLDB/DWH
Databases
Applications
SaaS
Managed
Managed
Managed
Managed
Managed
Managed
PowerBI

11. ON-PREMISESETLPROCESS ■ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - ■
Extraction Transformation Loading
 Physical or virtual servers
 Software licenses cost
 Closenetwork proximity
 Limited scalability

12. CLOUDETL:IAAS ■ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - ■
Extraction Transformation Loading
 Virtualinstances
 Software licenses cost / Pay-as-you-Go
 Proper network topology / VPN
 Securityand Compliance

13. CLOUDETL:PAAS ■ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - ■
 TypicallyELT, not ETL
 SOA-likeOrchestration
Extraction Loading Transformation
 Greatfor cloud-born data
 Flexibility and scalability
 Less moving parts
 Less control
 Managedservices
 Pay-As-You-Go
 Highdistribution
 Data Security

14. CLOUDETL:DATALAKE ■ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - ■
 TypicallyELT, not ETL
 SOA-likeOrchestration
Extraction Loading Transformation
 Greatfor cloud-born data
 Flexibility and scalability
 Less moving parts
 Less control
 Managedservices
 Pay-As-You-Go
 Highdistribution
 Data Security

15. DATALAKEVS DATAWAREHOUSE ■ - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - ■
DATA WAREHOUSEDATA LAKE
 Storesrawdatain its nativeformat
 Supportsmultiple dataformats
 Schema-on-Read
 Complexprocessing
 Batchprocessingof dataatscale
 Storescleansedandtransformeddata
 Optimizedforstructured,relationaldata
 Schema-on-Write
 Complexqueries
 Thousandsof users
 Clickstreamand logs
 Sensor data
 Images
 Transactions

16. CLOUDETL:HYBRID ■ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - ■
 Best of both worlds
 Leverages existing investments
 Future-proof
 Evolves as Cloud matures

17. CASE STUDY

18. CUSTOMER’SPROFILE ■ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - ■
 Coreplatform– mySQL
 Owntraffic– RESTAPI/JSON
 Competitorstraffic– RESTAPI/JSON
 Clickstreamdata-CSV/JSON
 Internalsystems
 GlobalE-Commerceplatform(C2C, B2C)
 BothHorizontalsandVerticals
 Strongfocuson services monetization
BUSINESS DATA
SYSTEMS
 Legacyon-premisesBI
 Predefinedreportsanddashboards

19. PHASE1:IAAS ■ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - ■
SQL Server 2016/Integration Services /Analysis Services
 IaaSin MicrosoftAzure
 AWSalsoconsidered
 Setofindependent ETLprocesses
MySQL
RESTAPI
Excel/ Web

20. PHASE2:HYBRID ■ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - ■
SQL Server 2016/Integration Services /Analysis Services
 PaaSservices
 DataLake
 DataWarehouse
 AdvancedAnalytics
MySQL
RESTAPI
Excel/ Web
Data Lake
Data Factory
R/U-SQL

21. CONCERNS ■ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - ■
DataSecurity and Compliance
Performance and networking issues
Complicated pricing models
Integration with on-premises solutions
Lackof control

22. CONCLUSIONS ■ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - ■
 Cloud BI cancomplement traditional on-premises BI
 Unlockspotential in alltypes of data, anywhere, anytime
 Production-ready, but needs to mature in some areas
 First choice for Cloud-born data
 Allows for true democratization of BI
 ... More fun to come!

23. THANK YOU!

24. Wlodek Bielski
BusinessIntelligenceArchitect
wlodzimierz.bielski@itmagination.com
CONTACT ■ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - ■