SlideShare a Scribd company logo
1 of 17
Download to read offline
#TalendConnect
EURONEXT LIBÈRE
SON POTENTIEL ANALYTIQUE
GRÂCE AU CLOUD GOUVERNÉ
ABDERRAHMANE BELARFAOUI – CHIEF DATA OFFICER - EURONEXT
22
Unleashing analytics with AWS
Euronext at glance
Business drivers and prerequisites
for a cloud first strategy
Implementing the governed data
lake
Business outcomes and next steps
01
02
03
04
05
© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
For Data to Be a Differentiator, Customers Need
to Be Able to…
• Capture and store new non-relational data at
PB-EB scale in real time
• New type of analytics that go beyond batch
reporting to incorporate real-time, predictive,
voice, and image recognition
• Democratize access to data in a secure and
governed way
New types of analytics
Dashboards Predictive Image
Recognition
VoiceReal-time
New types of data
© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Traditionally, Analytics Used to Look Like This
OLTP ERP CRM LOB
Data Warehouse
Business Intelligence • Relational data
• TBs–PBs scale
• Schema defined prior to data load
• Operational reporting and ad hoc
• Large initial CAPEX + $10K–$50K/TB/Year
© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Data Lakes, Analytics, and ML Portfolio from AWS
Broadest, deepest set of analytic services
Amazon SageMaker
AWS Deep Learning AMIs
Amazon Rekognition
Amazon Lex
AWS DeepLens
Amazon Comprehend
Amazon Translate
Amazon Transcribe
Amazon Polly
Amazon Athena
Amazon EMR
Amazon Redshift
Amazon Elasticsearch service
Amazon Kinesis
Amazon QuickSight
Analytics
Machine Learning
AWS Direct Connect
AWS Snowball
AWS Snowmobile
AWS Database Migration Service
AWS IoT Core
Amazon Kinesis Data Firehose
Amazon Kinesis Data Streams
Amazon Kinesis Video Streams
Real-time
Data Movement
On-premises
Data Movement
Data Lake on AWS
Storage | Archival Storage | Data Catalog
© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Pay Only for the Resources You Use as you Scale
Lowest Cost
• Pay-as-you-go for the resources you consume
• As low as $0.05/GB scanned with Athena
• EMR and Athena can automatically scale down
resources after job completes, saving you costs
• Commit to a set term and save up to 75% with
Reserved Instance
• Run on spare compute capacity with EMR and save up
to 90% with Spot
Traditional approach leads to wasted capacity
Traditional: Rigid
AWS: Elastic
Capacity
Demand
Demand
Servers
Unmet demand
upset players
missed revenue
Excess capacity
wasted $$$
AWS approach: pay for the capacity you use
7 #TalendConnect
• 1st European stock-exchange
• Amsterdam, Brussels, Dublin,
Lisbon, Paris
• 1300 corporate issuers
• €3.7trn market cap
• 6 national regulators
• Home of the CAC 40, BEL 20,
AEX, PSI 20
#TalendConnect8
ISSUES AT HAND
Daily Post Trade Processing
AVG Time 24 June 2016
(BREXIT VOTE)
6H 12H
With high data processing
constraints
• Avg order round time < 100 µs
• 1.5 B messages per day
• 400B records on chase trading table
#TalendConnect9
BUSINESS DRIVERS
FOR DATA GOVERNANCE
In-depth analysis New data products
Real time operations Mergers & acquisitions
GDPR, MIFID IIAgility for growth
Analytics Monetization
Real-time
monitoring
Consolidation
AI Regulatory
compliance
Data
Governance
INTERNALDEMAND
EXTERNALEXPECTATIONS
#TalendConnect10
DATA STRATEGY PROGRAM
Cloud Transformation
Program
Cloud Setup
Cloud Strategy
Data Project
Coordination
DWH Replacement
Data Shop
Analytics
Data Portal
Surveillance
Data Governance
Data Lake Laying out the foundations for future use cases Data Lake
20182019
Data Quality
Data Classification
Data Ownership
Privacy by Design
Data Classification
Retention Policy
Reference Mgmt.
Data Strategy
Data Breach Mgmt.
Data Loss Prevention
GDPR
Info Security
#TalendConnect11
EURONEXT DATALAKE
Data Reporting Data Science Monetization Real Time Monitoring
Euronext Data Lake
Orders Reference DataTrade Post Trade 3rd Party Streaming
Euronext Cloud Data
Warehouse
Data Sandboxes with
AI Capabilities
Euronext Data
Shop Surveillance
#TalendConnect12
AUTOMATION &
ORCHESTRATION
#TalendConnect13
EURONEXT 7 DATA
GOVERNANCE PRINCIPLES
Data mapping
Data protection
Data lineage
Data quality
Regulatory compliance
Change management
Data Catalog
MDM
Enterprise repositories
OUTCOMES & NEXT STEPS
#TalendConnect15
SOME KEY BENEFITS
AGILITY
CI/CD pipelines
Full serverless and/ephemeral resources
Innovation
Amazon Redshift vs Netezza
Use of AWS Batch with Spot instances
TCO: At equal budget with 10x more data usage (stream and storage)
Serverless orchestration with Step Functions/ AWS Batch and Amazon EMR
Amazon S3 Storage/Use of Amazon Redshift Spectrum
Kafka and Data Analytics
Every single identified need for this data lake has its corresponding
service on AWS
COST SAVING
BREADTH OF
FUNCTIONALITY
ELASTICITY
#TalendConnect16
INSTANT DATA INSIGHTS
AT SCALE
Data under control
for compliance and
monetization
Elasticity
and limitless
scale-up
On demand
Data Science
capabilities
Euronext_AWS_talend_connect_paris_2018.pdf

More Related Content

What's hot

Leadership Session: AWS Semiconductor (MFG201-L) - AWS re:Invent 2018
Leadership Session: AWS Semiconductor (MFG201-L) - AWS re:Invent 2018Leadership Session: AWS Semiconductor (MFG201-L) - AWS re:Invent 2018
Leadership Session: AWS Semiconductor (MFG201-L) - AWS re:Invent 2018Amazon Web Services
 
OpenPOWER partner presentation - GTS Data
OpenPOWER partner presentation - GTS DataOpenPOWER partner presentation - GTS Data
OpenPOWER partner presentation - GTS DataGanesan Narayanasamy
 
Big Data, HPC and Streaming
Big Data, HPC and StreamingBig Data, HPC and Streaming
Big Data, HPC and StreamingAnjani Phuyal
 
Fanatics Ingests Streaming Data to a Data Lake on AWS
Fanatics Ingests Streaming Data to a Data Lake on AWSFanatics Ingests Streaming Data to a Data Lake on AWS
Fanatics Ingests Streaming Data to a Data Lake on AWSAmazon Web Services
 
Driving Business Outcomes with a Modern Data Architecture - Level 100
Driving Business Outcomes with a Modern Data Architecture - Level 100Driving Business Outcomes with a Modern Data Architecture - Level 100
Driving Business Outcomes with a Modern Data Architecture - Level 100Amazon Web Services
 
Big Data Governance in a Post-GDPR World (GPSCT310) - AWS re:Invent 2018
Big Data Governance in a Post-GDPR World (GPSCT310) - AWS re:Invent 2018Big Data Governance in a Post-GDPR World (GPSCT310) - AWS re:Invent 2018
Big Data Governance in a Post-GDPR World (GPSCT310) - AWS re:Invent 2018Amazon Web Services
 
IBM Cloud Pak for Data Improves Cataloging Technologies for Enterprise
IBM Cloud Pak for Data Improves Cataloging Technologies for EnterpriseIBM Cloud Pak for Data Improves Cataloging Technologies for Enterprise
IBM Cloud Pak for Data Improves Cataloging Technologies for EnterpriseTimothy Valihora
 
How to become an big data rockstar in 15 minutes - Akmal Chaudhri
How to become an big data rockstar in 15 minutes - Akmal ChaudhriHow to become an big data rockstar in 15 minutes - Akmal Chaudhri
How to become an big data rockstar in 15 minutes - Akmal ChaudhriDataconomy Media
 
AWSome Day Intro Stockholm 201509
AWSome Day Intro Stockholm 201509AWSome Day Intro Stockholm 201509
AWSome Day Intro Stockholm 201509Amazon Web Services
 
AWS Community Day Nordics 2018: Rolf Koski - Building Successful Enterprise C...
AWS Community Day Nordics 2018: Rolf Koski - Building Successful Enterprise C...AWS Community Day Nordics 2018: Rolf Koski - Building Successful Enterprise C...
AWS Community Day Nordics 2018: Rolf Koski - Building Successful Enterprise C...Rolf Koski
 
(ISM212) Mcdonald’s Uses AWS To Launch Applications Quickly & Globally
(ISM212) Mcdonald’s Uses AWS To Launch Applications Quickly & Globally(ISM212) Mcdonald’s Uses AWS To Launch Applications Quickly & Globally
(ISM212) Mcdonald’s Uses AWS To Launch Applications Quickly & GloballyAmazon Web Services
 
AWS Community Day Nordics 2018 - Aino Health: Transition to serverless and le...
AWS Community Day Nordics 2018 - Aino Health: Transition to serverless and le...AWS Community Day Nordics 2018 - Aino Health: Transition to serverless and le...
AWS Community Day Nordics 2018 - Aino Health: Transition to serverless and le...Rolf Koski
 
Blockchain in Retail (RET217) - AWS re:Invent 2018
Blockchain in Retail (RET217) - AWS re:Invent 2018Blockchain in Retail (RET217) - AWS re:Invent 2018
Blockchain in Retail (RET217) - AWS re:Invent 2018Amazon Web Services
 
Make your data fly - Building data platform in AWS
Make your data fly - Building data platform in AWSMake your data fly - Building data platform in AWS
Make your data fly - Building data platform in AWSKimmo Kantojärvi
 
Introduction to Big Data using AWS Services
Introduction to Big Data using AWS ServicesIntroduction to Big Data using AWS Services
Introduction to Big Data using AWS ServicesAnjani Phuyal
 
Building an IoT Kafka Pipeline in Under 5 Minutes
Building an IoT Kafka Pipeline in Under 5 MinutesBuilding an IoT Kafka Pipeline in Under 5 Minutes
Building an IoT Kafka Pipeline in Under 5 MinutesSingleStore
 
Building Real-Time Data Pipelines with Kafka, Spark, and MemSQL
Building Real-Time Data Pipelines with Kafka, Spark, and MemSQLBuilding Real-Time Data Pipelines with Kafka, Spark, and MemSQL
Building Real-Time Data Pipelines with Kafka, Spark, and MemSQLSingleStore
 
Driving the On-Demand Economy with Predictive Analytics
Driving the On-Demand Economy with Predictive AnalyticsDriving the On-Demand Economy with Predictive Analytics
Driving the On-Demand Economy with Predictive AnalyticsSingleStore
 

What's hot (20)

Leadership Session: AWS Semiconductor (MFG201-L) - AWS re:Invent 2018
Leadership Session: AWS Semiconductor (MFG201-L) - AWS re:Invent 2018Leadership Session: AWS Semiconductor (MFG201-L) - AWS re:Invent 2018
Leadership Session: AWS Semiconductor (MFG201-L) - AWS re:Invent 2018
 
OpenPOWER partner presentation - GTS Data
OpenPOWER partner presentation - GTS DataOpenPOWER partner presentation - GTS Data
OpenPOWER partner presentation - GTS Data
 
Big Data, HPC and Streaming
Big Data, HPC and StreamingBig Data, HPC and Streaming
Big Data, HPC and Streaming
 
Fanatics Ingests Streaming Data to a Data Lake on AWS
Fanatics Ingests Streaming Data to a Data Lake on AWSFanatics Ingests Streaming Data to a Data Lake on AWS
Fanatics Ingests Streaming Data to a Data Lake on AWS
 
Driving Business Outcomes with a Modern Data Architecture - Level 100
Driving Business Outcomes with a Modern Data Architecture - Level 100Driving Business Outcomes with a Modern Data Architecture - Level 100
Driving Business Outcomes with a Modern Data Architecture - Level 100
 
Big Data Governance in a Post-GDPR World (GPSCT310) - AWS re:Invent 2018
Big Data Governance in a Post-GDPR World (GPSCT310) - AWS re:Invent 2018Big Data Governance in a Post-GDPR World (GPSCT310) - AWS re:Invent 2018
Big Data Governance in a Post-GDPR World (GPSCT310) - AWS re:Invent 2018
 
IBM Cloud Pak for Data Improves Cataloging Technologies for Enterprise
IBM Cloud Pak for Data Improves Cataloging Technologies for EnterpriseIBM Cloud Pak for Data Improves Cataloging Technologies for Enterprise
IBM Cloud Pak for Data Improves Cataloging Technologies for Enterprise
 
How to become an big data rockstar in 15 minutes - Akmal Chaudhri
How to become an big data rockstar in 15 minutes - Akmal ChaudhriHow to become an big data rockstar in 15 minutes - Akmal Chaudhri
How to become an big data rockstar in 15 minutes - Akmal Chaudhri
 
A journey to faster, repeatable data commercialization
A journey to faster, repeatable data commercializationA journey to faster, repeatable data commercialization
A journey to faster, repeatable data commercialization
 
AWSome Day Intro Stockholm 201509
AWSome Day Intro Stockholm 201509AWSome Day Intro Stockholm 201509
AWSome Day Intro Stockholm 201509
 
AWS Community Day Nordics 2018: Rolf Koski - Building Successful Enterprise C...
AWS Community Day Nordics 2018: Rolf Koski - Building Successful Enterprise C...AWS Community Day Nordics 2018: Rolf Koski - Building Successful Enterprise C...
AWS Community Day Nordics 2018: Rolf Koski - Building Successful Enterprise C...
 
(ISM212) Mcdonald’s Uses AWS To Launch Applications Quickly & Globally
(ISM212) Mcdonald’s Uses AWS To Launch Applications Quickly & Globally(ISM212) Mcdonald’s Uses AWS To Launch Applications Quickly & Globally
(ISM212) Mcdonald’s Uses AWS To Launch Applications Quickly & Globally
 
AWS Community Day Nordics 2018 - Aino Health: Transition to serverless and le...
AWS Community Day Nordics 2018 - Aino Health: Transition to serverless and le...AWS Community Day Nordics 2018 - Aino Health: Transition to serverless and le...
AWS Community Day Nordics 2018 - Aino Health: Transition to serverless and le...
 
Blockchain in Retail (RET217) - AWS re:Invent 2018
Blockchain in Retail (RET217) - AWS re:Invent 2018Blockchain in Retail (RET217) - AWS re:Invent 2018
Blockchain in Retail (RET217) - AWS re:Invent 2018
 
Make your data fly - Building data platform in AWS
Make your data fly - Building data platform in AWSMake your data fly - Building data platform in AWS
Make your data fly - Building data platform in AWS
 
Introduction to Big Data using AWS Services
Introduction to Big Data using AWS ServicesIntroduction to Big Data using AWS Services
Introduction to Big Data using AWS Services
 
Building an IoT Kafka Pipeline in Under 5 Minutes
Building an IoT Kafka Pipeline in Under 5 MinutesBuilding an IoT Kafka Pipeline in Under 5 Minutes
Building an IoT Kafka Pipeline in Under 5 Minutes
 
Machine learning
Machine learningMachine learning
Machine learning
 
Building Real-Time Data Pipelines with Kafka, Spark, and MemSQL
Building Real-Time Data Pipelines with Kafka, Spark, and MemSQLBuilding Real-Time Data Pipelines with Kafka, Spark, and MemSQL
Building Real-Time Data Pipelines with Kafka, Spark, and MemSQL
 
Driving the On-Demand Economy with Predictive Analytics
Driving the On-Demand Economy with Predictive AnalyticsDriving the On-Demand Economy with Predictive Analytics
Driving the On-Demand Economy with Predictive Analytics
 

Similar to Euronext_AWS_talend_connect_paris_2018.pdf

Amazon Kinesis - Building Serverless real-time solution - Tel Aviv Summit 2018
Amazon Kinesis - Building Serverless real-time solution - Tel Aviv Summit 2018Amazon Kinesis - Building Serverless real-time solution - Tel Aviv Summit 2018
Amazon Kinesis - Building Serverless real-time solution - Tel Aviv Summit 2018Amazon Web Services
 
Modern Data Architectures for Business Insights at Scale
Modern Data Architectures for Business Insights at Scale Modern Data Architectures for Business Insights at Scale
Modern Data Architectures for Business Insights at Scale Amazon Web Services
 
Finding Meaning in the Noise: Understanding Big Data with AWS Analytics
Finding Meaning in the Noise: Understanding Big Data with AWS AnalyticsFinding Meaning in the Noise: Understanding Big Data with AWS Analytics
Finding Meaning in the Noise: Understanding Big Data with AWS AnalyticsAmazon Web Services
 
Data Warehouses & Data Lakes: Data Analytics Week at the SF Loft
Data Warehouses & Data Lakes: Data Analytics Week at the SF LoftData Warehouses & Data Lakes: Data Analytics Week at the SF Loft
Data Warehouses & Data Lakes: Data Analytics Week at the SF LoftAmazon Web Services
 
Building a Data Lake in Amazon S3 & Amazon Glacier (STG401-R1) - AWS re:Inven...
Building a Data Lake in Amazon S3 & Amazon Glacier (STG401-R1) - AWS re:Inven...Building a Data Lake in Amazon S3 & Amazon Glacier (STG401-R1) - AWS re:Inven...
Building a Data Lake in Amazon S3 & Amazon Glacier (STG401-R1) - AWS re:Inven...Amazon Web Services
 
Track 1 Session 6_建立安全高效的資料分析平台加速金融創新_HC+EMQ Cliff(已檢核,上下無黑邊).pptx
Track 1 Session 6_建立安全高效的資料分析平台加速金融創新_HC+EMQ Cliff(已檢核,上下無黑邊).pptxTrack 1 Session 6_建立安全高效的資料分析平台加速金融創新_HC+EMQ Cliff(已檢核,上下無黑邊).pptx
Track 1 Session 6_建立安全高效的資料分析平台加速金融創新_HC+EMQ Cliff(已檢核,上下無黑邊).pptxAmazon Web Services
 
How TrueCar Gains Actionable Insights with Splunk Cloud PPT
How TrueCar Gains Actionable Insights with Splunk Cloud PPTHow TrueCar Gains Actionable Insights with Splunk Cloud PPT
How TrueCar Gains Actionable Insights with Splunk Cloud PPTAmazon Web Services
 
Modern Cloud Data Warehousing ft. Equinox Fitness Clubs: Optimize Analytics P...
Modern Cloud Data Warehousing ft. Equinox Fitness Clubs: Optimize Analytics P...Modern Cloud Data Warehousing ft. Equinox Fitness Clubs: Optimize Analytics P...
Modern Cloud Data Warehousing ft. Equinox Fitness Clubs: Optimize Analytics P...Amazon Web Services
 
Big Data@Scale_AWSPSSummit_Singapore
Big Data@Scale_AWSPSSummit_SingaporeBig Data@Scale_AWSPSSummit_Singapore
Big Data@Scale_AWSPSSummit_SingaporeAmazon Web Services
 
Big Data & Analytics - Innovating at the Speed of Light
Big Data & Analytics - Innovating at the Speed of LightBig Data & Analytics - Innovating at the Speed of Light
Big Data & Analytics - Innovating at the Speed of LightAmazon Web Services LATAM
 
雲上打造資料湖 (Data Lake):智能化駕馭商機 (Level 300)
雲上打造資料湖 (Data Lake):智能化駕馭商機 (Level 300)雲上打造資料湖 (Data Lake):智能化駕馭商機 (Level 300)
雲上打造資料湖 (Data Lake):智能化駕馭商機 (Level 300)Amazon Web Services
 
Modern Data Architectures for Business Outcomes
Modern Data Architectures for Business OutcomesModern Data Architectures for Business Outcomes
Modern Data Architectures for Business OutcomesAmazon Web Services
 
Big Data Meets AI - Driving Insights and Adding Intelligence to Your Solutions
 Big Data Meets AI - Driving Insights and Adding Intelligence to Your Solutions Big Data Meets AI - Driving Insights and Adding Intelligence to Your Solutions
Big Data Meets AI - Driving Insights and Adding Intelligence to Your SolutionsAmazon Web Services
 
Building Data Lakes for Analytics on AWS
Building Data Lakes for Analytics on AWSBuilding Data Lakes for Analytics on AWS
Building Data Lakes for Analytics on AWSAmazon Web Services
 
Migrating Financial and Accounting Systems from Oracle to Amazon DynamoDB (DA...
Migrating Financial and Accounting Systems from Oracle to Amazon DynamoDB (DA...Migrating Financial and Accounting Systems from Oracle to Amazon DynamoDB (DA...
Migrating Financial and Accounting Systems from Oracle to Amazon DynamoDB (DA...Amazon Web Services
 
BDA305 Building Data Lakes and Analytics on AWS
BDA305 Building Data Lakes and Analytics on AWSBDA305 Building Data Lakes and Analytics on AWS
BDA305 Building Data Lakes and Analytics on AWSAmazon Web Services
 
Implementazione di una soluzione Data Lake.pdf
Implementazione di una soluzione Data Lake.pdfImplementazione di una soluzione Data Lake.pdf
Implementazione di una soluzione Data Lake.pdfAmazon Web Services
 

Similar to Euronext_AWS_talend_connect_paris_2018.pdf (20)

Amazon Kinesis - Building Serverless real-time solution - Tel Aviv Summit 2018
Amazon Kinesis - Building Serverless real-time solution - Tel Aviv Summit 2018Amazon Kinesis - Building Serverless real-time solution - Tel Aviv Summit 2018
Amazon Kinesis - Building Serverless real-time solution - Tel Aviv Summit 2018
 
Modern Data Architectures for Business Insights at Scale
Modern Data Architectures for Business Insights at Scale Modern Data Architectures for Business Insights at Scale
Modern Data Architectures for Business Insights at Scale
 
Finding Meaning in the Noise: Understanding Big Data with AWS Analytics
Finding Meaning in the Noise: Understanding Big Data with AWS AnalyticsFinding Meaning in the Noise: Understanding Big Data with AWS Analytics
Finding Meaning in the Noise: Understanding Big Data with AWS Analytics
 
Data Warehouses & Data Lakes: Data Analytics Week at the SF Loft
Data Warehouses & Data Lakes: Data Analytics Week at the SF LoftData Warehouses & Data Lakes: Data Analytics Week at the SF Loft
Data Warehouses & Data Lakes: Data Analytics Week at the SF Loft
 
Building a Data Lake in Amazon S3 & Amazon Glacier (STG401-R1) - AWS re:Inven...
Building a Data Lake in Amazon S3 & Amazon Glacier (STG401-R1) - AWS re:Inven...Building a Data Lake in Amazon S3 & Amazon Glacier (STG401-R1) - AWS re:Inven...
Building a Data Lake in Amazon S3 & Amazon Glacier (STG401-R1) - AWS re:Inven...
 
Track 1 Session 6_建立安全高效的資料分析平台加速金融創新_HC+EMQ Cliff(已檢核,上下無黑邊).pptx
Track 1 Session 6_建立安全高效的資料分析平台加速金融創新_HC+EMQ Cliff(已檢核,上下無黑邊).pptxTrack 1 Session 6_建立安全高效的資料分析平台加速金融創新_HC+EMQ Cliff(已檢核,上下無黑邊).pptx
Track 1 Session 6_建立安全高效的資料分析平台加速金融創新_HC+EMQ Cliff(已檢核,上下無黑邊).pptx
 
How TrueCar Gains Actionable Insights with Splunk Cloud PPT
How TrueCar Gains Actionable Insights with Splunk Cloud PPTHow TrueCar Gains Actionable Insights with Splunk Cloud PPT
How TrueCar Gains Actionable Insights with Splunk Cloud PPT
 
Modern Cloud Data Warehousing ft. Equinox Fitness Clubs: Optimize Analytics P...
Modern Cloud Data Warehousing ft. Equinox Fitness Clubs: Optimize Analytics P...Modern Cloud Data Warehousing ft. Equinox Fitness Clubs: Optimize Analytics P...
Modern Cloud Data Warehousing ft. Equinox Fitness Clubs: Optimize Analytics P...
 
Big Data@Scale_AWSPSSummit_Singapore
Big Data@Scale_AWSPSSummit_SingaporeBig Data@Scale_AWSPSSummit_Singapore
Big Data@Scale_AWSPSSummit_Singapore
 
Big Data & Analytics - Innovating at the Speed of Light
Big Data & Analytics - Innovating at the Speed of LightBig Data & Analytics - Innovating at the Speed of Light
Big Data & Analytics - Innovating at the Speed of Light
 
Data Warehouses and Data Lakes
Data Warehouses and Data LakesData Warehouses and Data Lakes
Data Warehouses and Data Lakes
 
雲上打造資料湖 (Data Lake):智能化駕馭商機 (Level 300)
雲上打造資料湖 (Data Lake):智能化駕馭商機 (Level 300)雲上打造資料湖 (Data Lake):智能化駕馭商機 (Level 300)
雲上打造資料湖 (Data Lake):智能化駕馭商機 (Level 300)
 
Modern Data Architectures for Business Outcomes
Modern Data Architectures for Business OutcomesModern Data Architectures for Business Outcomes
Modern Data Architectures for Business Outcomes
 
Big Data Meets AI - Driving Insights and Adding Intelligence to Your Solutions
 Big Data Meets AI - Driving Insights and Adding Intelligence to Your Solutions Big Data Meets AI - Driving Insights and Adding Intelligence to Your Solutions
Big Data Meets AI - Driving Insights and Adding Intelligence to Your Solutions
 
Building Data Lakes for Analytics on AWS
Building Data Lakes for Analytics on AWSBuilding Data Lakes for Analytics on AWS
Building Data Lakes for Analytics on AWS
 
Migrating Financial and Accounting Systems from Oracle to Amazon DynamoDB (DA...
Migrating Financial and Accounting Systems from Oracle to Amazon DynamoDB (DA...Migrating Financial and Accounting Systems from Oracle to Amazon DynamoDB (DA...
Migrating Financial and Accounting Systems from Oracle to Amazon DynamoDB (DA...
 
BDA305 Building Data Lakes and Analytics on AWS
BDA305 Building Data Lakes and Analytics on AWSBDA305 Building Data Lakes and Analytics on AWS
BDA305 Building Data Lakes and Analytics on AWS
 
Implementazione di una soluzione Data Lake.pdf
Implementazione di una soluzione Data Lake.pdfImplementazione di una soluzione Data Lake.pdf
Implementazione di una soluzione Data Lake.pdf
 
Analysing Data in Real-time
Analysing Data in Real-timeAnalysing Data in Real-time
Analysing Data in Real-time
 
AWS Big Data Platform
AWS Big Data PlatformAWS Big Data Platform
AWS Big Data Platform
 

More from Amazon Web Services

Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
 
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Big Data per le Startup: come creare applicazioni Big Data in modalità Server...
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Amazon Web Services
 
Esegui pod serverless con Amazon EKS e AWS Fargate
Esegui pod serverless con Amazon EKS e AWS FargateEsegui pod serverless con Amazon EKS e AWS Fargate
Esegui pod serverless con Amazon EKS e AWS FargateAmazon Web Services
 
Costruire Applicazioni Moderne con AWS
Costruire Applicazioni Moderne con AWSCostruire Applicazioni Moderne con AWS
Costruire Applicazioni Moderne con AWSAmazon Web Services
 
Come spendere fino al 90% in meno con i container e le istanze spot
Come spendere fino al 90% in meno con i container e le istanze spot Come spendere fino al 90% in meno con i container e le istanze spot
Come spendere fino al 90% in meno con i container e le istanze spot Amazon Web Services
 
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Amazon Web Services
 
OpsWorks Configuration Management: automatizza la gestione e i deployment del...
OpsWorks Configuration Management: automatizza la gestione e i deployment del...OpsWorks Configuration Management: automatizza la gestione e i deployment del...
OpsWorks Configuration Management: automatizza la gestione e i deployment del...Amazon Web Services
 
Microsoft Active Directory su AWS per supportare i tuoi Windows Workloads
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsMicrosoft Active Directory su AWS per supportare i tuoi Windows Workloads
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
 
Database Oracle e VMware Cloud on AWS i miti da sfatare
Database Oracle e VMware Cloud on AWS i miti da sfatareDatabase Oracle e VMware Cloud on AWS i miti da sfatare
Database Oracle e VMware Cloud on AWS i miti da sfatareAmazon Web Services
 
Crea la tua prima serverless ledger-based app con QLDB e NodeJS
Crea la tua prima serverless ledger-based app con QLDB e NodeJSCrea la tua prima serverless ledger-based app con QLDB e NodeJS
Crea la tua prima serverless ledger-based app con QLDB e NodeJSAmazon Web Services
 
API moderne real-time per applicazioni mobili e web
API moderne real-time per applicazioni mobili e webAPI moderne real-time per applicazioni mobili e web
API moderne real-time per applicazioni mobili e webAmazon Web Services
 
Database Oracle e VMware Cloud™ on AWS: i miti da sfatare
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareDatabase Oracle e VMware Cloud™ on AWS: i miti da sfatare
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareAmazon Web Services
 
Tools for building your MVP on AWS
Tools for building your MVP on AWSTools for building your MVP on AWS
Tools for building your MVP on AWSAmazon Web Services
 
How to Build a Winning Pitch Deck
How to Build a Winning Pitch DeckHow to Build a Winning Pitch Deck
How to Build a Winning Pitch DeckAmazon Web Services
 
Building a web application without servers
Building a web application without serversBuilding a web application without servers
Building a web application without serversAmazon Web Services
 
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...AWS_HK_StartupDay_Building Interactive websites while automating for efficien...
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...Amazon Web Services
 
Introduzione a Amazon Elastic Container Service
Introduzione a Amazon Elastic Container ServiceIntroduzione a Amazon Elastic Container Service
Introduzione a Amazon Elastic Container ServiceAmazon Web Services
 

More from Amazon Web Services (20)

Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...
 
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Big Data per le Startup: come creare applicazioni Big Data in modalità Server...
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...
 
Esegui pod serverless con Amazon EKS e AWS Fargate
Esegui pod serverless con Amazon EKS e AWS FargateEsegui pod serverless con Amazon EKS e AWS Fargate
Esegui pod serverless con Amazon EKS e AWS Fargate
 
Costruire Applicazioni Moderne con AWS
Costruire Applicazioni Moderne con AWSCostruire Applicazioni Moderne con AWS
Costruire Applicazioni Moderne con AWS
 
Come spendere fino al 90% in meno con i container e le istanze spot
Come spendere fino al 90% in meno con i container e le istanze spot Come spendere fino al 90% in meno con i container e le istanze spot
Come spendere fino al 90% in meno con i container e le istanze spot
 
Open banking as a service
Open banking as a serviceOpen banking as a service
Open banking as a service
 
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...
 
OpsWorks Configuration Management: automatizza la gestione e i deployment del...
OpsWorks Configuration Management: automatizza la gestione e i deployment del...OpsWorks Configuration Management: automatizza la gestione e i deployment del...
OpsWorks Configuration Management: automatizza la gestione e i deployment del...
 
Microsoft Active Directory su AWS per supportare i tuoi Windows Workloads
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsMicrosoft Active Directory su AWS per supportare i tuoi Windows Workloads
Microsoft Active Directory su AWS per supportare i tuoi Windows Workloads
 
Computer Vision con AWS
Computer Vision con AWSComputer Vision con AWS
Computer Vision con AWS
 
Database Oracle e VMware Cloud on AWS i miti da sfatare
Database Oracle e VMware Cloud on AWS i miti da sfatareDatabase Oracle e VMware Cloud on AWS i miti da sfatare
Database Oracle e VMware Cloud on AWS i miti da sfatare
 
Crea la tua prima serverless ledger-based app con QLDB e NodeJS
Crea la tua prima serverless ledger-based app con QLDB e NodeJSCrea la tua prima serverless ledger-based app con QLDB e NodeJS
Crea la tua prima serverless ledger-based app con QLDB e NodeJS
 
API moderne real-time per applicazioni mobili e web
API moderne real-time per applicazioni mobili e webAPI moderne real-time per applicazioni mobili e web
API moderne real-time per applicazioni mobili e web
 
Database Oracle e VMware Cloud™ on AWS: i miti da sfatare
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareDatabase Oracle e VMware Cloud™ on AWS: i miti da sfatare
Database Oracle e VMware Cloud™ on AWS: i miti da sfatare
 
Tools for building your MVP on AWS
Tools for building your MVP on AWSTools for building your MVP on AWS
Tools for building your MVP on AWS
 
How to Build a Winning Pitch Deck
How to Build a Winning Pitch DeckHow to Build a Winning Pitch Deck
How to Build a Winning Pitch Deck
 
Building a web application without servers
Building a web application without serversBuilding a web application without servers
Building a web application without servers
 
Fundraising Essentials
Fundraising EssentialsFundraising Essentials
Fundraising Essentials
 
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...AWS_HK_StartupDay_Building Interactive websites while automating for efficien...
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...
 
Introduzione a Amazon Elastic Container Service
Introduzione a Amazon Elastic Container ServiceIntroduzione a Amazon Elastic Container Service
Introduzione a Amazon Elastic Container Service
 

Euronext_AWS_talend_connect_paris_2018.pdf

  • 1. #TalendConnect EURONEXT LIBÈRE SON POTENTIEL ANALYTIQUE GRÂCE AU CLOUD GOUVERNÉ ABDERRAHMANE BELARFAOUI – CHIEF DATA OFFICER - EURONEXT
  • 2. 22 Unleashing analytics with AWS Euronext at glance Business drivers and prerequisites for a cloud first strategy Implementing the governed data lake Business outcomes and next steps 01 02 03 04 05
  • 3. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. For Data to Be a Differentiator, Customers Need to Be Able to… • Capture and store new non-relational data at PB-EB scale in real time • New type of analytics that go beyond batch reporting to incorporate real-time, predictive, voice, and image recognition • Democratize access to data in a secure and governed way New types of analytics Dashboards Predictive Image Recognition VoiceReal-time New types of data
  • 4. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Traditionally, Analytics Used to Look Like This OLTP ERP CRM LOB Data Warehouse Business Intelligence • Relational data • TBs–PBs scale • Schema defined prior to data load • Operational reporting and ad hoc • Large initial CAPEX + $10K–$50K/TB/Year
  • 5. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Data Lakes, Analytics, and ML Portfolio from AWS Broadest, deepest set of analytic services Amazon SageMaker AWS Deep Learning AMIs Amazon Rekognition Amazon Lex AWS DeepLens Amazon Comprehend Amazon Translate Amazon Transcribe Amazon Polly Amazon Athena Amazon EMR Amazon Redshift Amazon Elasticsearch service Amazon Kinesis Amazon QuickSight Analytics Machine Learning AWS Direct Connect AWS Snowball AWS Snowmobile AWS Database Migration Service AWS IoT Core Amazon Kinesis Data Firehose Amazon Kinesis Data Streams Amazon Kinesis Video Streams Real-time Data Movement On-premises Data Movement Data Lake on AWS Storage | Archival Storage | Data Catalog
  • 6. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Pay Only for the Resources You Use as you Scale Lowest Cost • Pay-as-you-go for the resources you consume • As low as $0.05/GB scanned with Athena • EMR and Athena can automatically scale down resources after job completes, saving you costs • Commit to a set term and save up to 75% with Reserved Instance • Run on spare compute capacity with EMR and save up to 90% with Spot Traditional approach leads to wasted capacity Traditional: Rigid AWS: Elastic Capacity Demand Demand Servers Unmet demand upset players missed revenue Excess capacity wasted $$$ AWS approach: pay for the capacity you use
  • 7. 7 #TalendConnect • 1st European stock-exchange • Amsterdam, Brussels, Dublin, Lisbon, Paris • 1300 corporate issuers • €3.7trn market cap • 6 national regulators • Home of the CAC 40, BEL 20, AEX, PSI 20
  • 8. #TalendConnect8 ISSUES AT HAND Daily Post Trade Processing AVG Time 24 June 2016 (BREXIT VOTE) 6H 12H With high data processing constraints • Avg order round time < 100 µs • 1.5 B messages per day • 400B records on chase trading table
  • 9. #TalendConnect9 BUSINESS DRIVERS FOR DATA GOVERNANCE In-depth analysis New data products Real time operations Mergers & acquisitions GDPR, MIFID IIAgility for growth Analytics Monetization Real-time monitoring Consolidation AI Regulatory compliance Data Governance INTERNALDEMAND EXTERNALEXPECTATIONS
  • 10. #TalendConnect10 DATA STRATEGY PROGRAM Cloud Transformation Program Cloud Setup Cloud Strategy Data Project Coordination DWH Replacement Data Shop Analytics Data Portal Surveillance Data Governance Data Lake Laying out the foundations for future use cases Data Lake 20182019 Data Quality Data Classification Data Ownership Privacy by Design Data Classification Retention Policy Reference Mgmt. Data Strategy Data Breach Mgmt. Data Loss Prevention GDPR Info Security
  • 11. #TalendConnect11 EURONEXT DATALAKE Data Reporting Data Science Monetization Real Time Monitoring Euronext Data Lake Orders Reference DataTrade Post Trade 3rd Party Streaming Euronext Cloud Data Warehouse Data Sandboxes with AI Capabilities Euronext Data Shop Surveillance
  • 13. #TalendConnect13 EURONEXT 7 DATA GOVERNANCE PRINCIPLES Data mapping Data protection Data lineage Data quality Regulatory compliance Change management Data Catalog MDM Enterprise repositories
  • 15. #TalendConnect15 SOME KEY BENEFITS AGILITY CI/CD pipelines Full serverless and/ephemeral resources Innovation Amazon Redshift vs Netezza Use of AWS Batch with Spot instances TCO: At equal budget with 10x more data usage (stream and storage) Serverless orchestration with Step Functions/ AWS Batch and Amazon EMR Amazon S3 Storage/Use of Amazon Redshift Spectrum Kafka and Data Analytics Every single identified need for this data lake has its corresponding service on AWS COST SAVING BREADTH OF FUNCTIONALITY ELASTICITY
  • 16. #TalendConnect16 INSTANT DATA INSIGHTS AT SCALE Data under control for compliance and monetization Elasticity and limitless scale-up On demand Data Science capabilities