Workshop part2 – Big Data

•

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Amazon Web Services

Webit 2014 AWS workshop

Technology

THE MORE DATA YOU COLLECT
THE MORE VALUE YOU CAN
DERIVE FROM IT

Lower cost,
higher throughput
GENERATE ➔ STORE ➔ ANALYZE ➔ SHARE

Lower cost,
higher throughput
GENERATE ➔ STORE ➔ ANALYZE ➔ SHARE
Highly
constrained

+ ELASTIC AND HIGHLY SCALABLE
+ NO UPFRONT CAPITAL EXPENSE
+ ONLY PAY FOR WHAT YOU USE
+ AVAILABLE ON-DEMAND
!
= REMOVE CONSTRAINTS

AWS Import / Export
AWS Direct Connect
GENERATE ➔ STORE ➔ ANALYZE ➔ SHARE

Inbound data transfer is free
Multipart upload to S3
Physical media
AWS Direct Connect

Amazon S3,
Amazon Glacier,
Amazon DynamoDB,
Amazon RDS,
Amazon Redshift,
AWS Storage Gateway,
Data on Amazon EC2
GENERATE ➔ STORE ➔ ANALYZE ➔ SHARE

CASE STUDY:
SPOTIFY ADDS 20,000 TRACKS/DAY TO ITS CATALOGUE

AMAZON
DYNAMODB"
HIGH-PERFORMANCE, FULLY MANAGED
NoSQL DATABASE SERVICE

DURABLE &
AVAILABLE
CONSISTENT, DISK-ONLY
WRITES (SSD)

LOW LATENCY
AVERAGE READS < 5MS,
WRITES < 10MS

CASE STUDY:
SHAZAM SUPPORTED 500,000 WRITES/SEC
DURING SUPER BOWL

AMAZON
REDSHIFT"
FULLY MANAGED, PETA-BYTE SCALE
DATAWAREHOUSE ON AWS

AMAZON REDSHIFT LETS YOU
START SMALL AND GROW BIG
Extra Large Node (HS1.XL)
!
Single Node (2 TB)
!
Cluster 2-32 Nodes (4 TB – 64 TB)
Eight Extra Large Node (HS1.8XL)
Cluster 2-100 Nodes (32 TB – 1.6 PB)
8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL
8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL
8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL
8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL
8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL
8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL
8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL
8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL
8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL
8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL
XL
XL XL XL XL XL XL XL XL
XL XL XL XL XL XL XL XL
XL XL XL XL XL XL XL XL
XL XL XL XL XL XL XL XL

GENERATE ➔ STORE ➔ ANALYZE ➔ SHARE
Amazon EC2
Amazon Elastic
MapReduce

Instead of
$20+ MILLIONS
in infrastructure

GPU INSTANCES"
!
G2"
CG1
1x NVIDIA Kepler GK104
8 vCPU (Intel Xeon E5-2670)
$0.65/h
2x NVIDIA Fermi M2050
16 vCPU (Intel Xeon X5570)
$2.10/h

ON A SINGLE INSTANCE
COMPUTE TIME: 4h
COST: 4h x $2.1 = $8.4

ON MULTIPLE INSTANCES
COMPUTE TIME: 1h
COST: 1h x 4 x $2.1 = $8.4

AMAZON ELASTIC
MAPREDUCE
HADOOP AS A SERVICE

CASE STUDY:
"WITH AMAZON EMR WE CAN ANALYZE 100% OF THE DATA,
NOT JUST A SAMPLE"
- Sanjeevan Bala, Head of Data Planning & Analytics, Channel 4

Amazon S3,
Amazon DynamoDB,
Amazon RDS,
Amazon Redshift,
Data on Amazon EC2
GENERATE ➔ STORE ➔ ANALYZE ➔ SHARE

PUBLIC DATA SETS
http://aws.amazon.com/publicdatasets

GENERATE ➔ STORE ➔ ANALYZE ➔ SHARE
BATCH
PROCESSING

AMAZON KINESIS
REAL-TIME DATA STREAM PROCESSING

Hourly server logs: how your
systems went wrong an hour ago
Weekly / Monthly Bill: What you
spent this past billing cycle
Daily customer report from your
website: tells you what deal or ad
to try next time
Daily fraud reports: tells you if there
was fraud yesterday
Daily business reports: tells me
how customers used AWS services
yesterday
Real-time metrics: what just went
wrong now
Real-time spending alerts/caps:
guaranteeing you can’t
overspend
Real-time analysis: what to offer
the current customer now
Real-time detection: blocks
fraudulent use now
Fast ETL into Amazon Redshift:
how are customers using
services now

Amazon S3,
Amazon DynamoDB,
Amazon RDS,
Amazon Redshift,
Data on Amazon EC2
Amazon S3,
Amazon Glacier,
Amazon DynamoDB,
Amazon RDS,
Amazon Redshift,
AWS Storage Gateway,
Data on Amazon EC2
GENERATE ➔ STORE ➔ ANALYZE ➔ SHARE
Amazon EC2
Amazon Elastic
MapReduce
AWS Import / Export
AWS Direct Connect

STREAM
PROCESSING
Amazon S3,
Amazon DynamoDB,
Amazon RDS,
Amazon Redshift,
Data on Amazon EC2
GENERATE ➔ ➔ SHARE
Amazon Kinesis
Stream Processing
on Amazon EC2

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Computer Vision con AWS

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Amazon Web Services e VMware organizzano un evento virtuale gratuito il prossimo mercoledì 14 Ottobre dalle 12:00 alle 13:00 dedicato a VMware Cloud ™ on AWS, il servizio on demand che consente di eseguire applicazioni in ambienti cloud basati su VMware vSphere® e di accedere ad una vasta gamma di servizi AWS, sfruttando a pieno le potenzialità del cloud AWS e tutelando gli investimenti VMware esistenti. Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi. La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.

Database Oracle e VMware Cloud on AWS i miti da sfatare

Amazon Web Services

Molte aziende oggi, costruiscono applicazioni con funzionalità di tipo ledger ad esempio per verificare lo storico di accrediti o addebiti nelle transazioni bancarie o ancora per tenere traccia del flusso supply chain dei propri prodotti. Alla base di queste soluzioni ci sono i database ledger che permettono di avere un log delle transazioni trasparente, immutabile e crittograficamente verificabile, ma sono strumenti complessi e onerosi da gestire. Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito. In questa sessione scopriremo come realizzare un'applicazione serverless completa che utilizzi le funzionalità di QLDB.

Crea la tua prima serverless ledger-based app con QLDB e NodeJS

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Con l’ascesa delle architetture di microservizi e delle ricche applicazioni mobili e Web, le API sono più importanti che mai per offrire agli utenti finali una user experience eccezionale. In questa sessione impareremo come affrontare le moderne sfide di progettazione delle API con GraphQL, un linguaggio di query API open source utilizzato da Facebook, Amazon e altro e come utilizzare AWS AppSync, un servizio GraphQL serverless gestito su AWS. Approfondiremo diversi scenari, comprendendo come AppSync può aiutare a risolvere questi casi d’uso creando API moderne con funzionalità di aggiornamento dati in tempo reale e offline. Inoltre, impareremo come Sky Italia utilizza AWS AppSync per fornire aggiornamenti sportivi in tempo reale agli utenti del proprio portale web.

API moderne real-time per applicazioni mobili e web

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Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi. La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali. In queste slide, gli esperti AWS e VMware presentano semplici e pratici accorgimenti per facilitare e semplificare la migrazione dei carichi di lavoro Oracle accelerando la trasformazione verso il cloud, approfondiranno l’architettura e dimostreranno come sfruttare a pieno le potenzialità di VMware Cloud ™ on AWS.

Database Oracle e VMware Cloud™ on AWS: i miti da sfatare

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Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.

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Effective data discovery is crucial for maintaining compliance and mitigating risks in today's rapidly evolving privacy landscape. However, traditional manual approaches often struggle to keep pace with the growing volume and complexity of data. Join us for an insightful webinar where industry leaders from TrustArc and Privya will share their expertise on leveraging AI-powered solutions to revolutionize data discovery. You'll learn how to: - Effortlessly maintain a comprehensive, up-to-date data inventory - Harness code scanning insights to gain complete visibility into data flows leveraging the advantages of code scanning over DB scanning - Simplify compliance by leveraging Privya's integration with TrustArc - Implement proven strategies to mitigate third-party risks Our panel of experts will discuss real-world case studies and share practical strategies for overcoming common data discovery challenges. They'll also explore the latest trends and innovations in AI-driven data management, and how these technologies can help organizations stay ahead of the curve in an ever-changing privacy landscape.

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MySQL Webinar, presented on the 25th of April, 2024. Summary: MySQL solutions enable the deployment of diverse Database Architectures tailored to specific needs, including High Availability, Disaster Recovery, and Read Scale-Out. With MySQL Shell's AdminAPI, administrators can seamlessly set up, manage, and monitor these solutions, ensuring efficiency and ease of use in their administration. MySQL Router, on the other hand, provides transparent routing from the application traffic to the backend servers in the architectures, requiring minimal configuration. Completely built in-house and supported by Oracle, these solutions have been adopted by enterprises of all sizes for their business-critical applications. In this presentation, we'll delve into various database architecture solutions to help you choose the right one based on your business requirements. Focusing on technical details and the latest features to maximize the potential of these solutions.

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Three things you will take away from the session: • How to run an effective tenant-to-tenant migration • Best practices for before, during, and after migration • Tips for using migration as a springboard to prepare for Copilot in Microsoft 365 Main ideas: Migration Overview: The presentation covers the current reality of cross-tenant migrations, the triggers, phases, best practices, and benefits of a successful tenant migration Considerations: When considering a migration, it is important to consider the migration scope, performance, customization, flexibility, user-friendly interface, automation, monitoring, support, training, scalability, data integrity, data security, cost, and licensing structure Next Wave: The next wave of change includes the launch of Copilot, which requires businesses to be prepared for upcoming changes related to Copilot and the cloud, and to consolidate data and tighten governance ShareGate: ShareGate can help with pre-migration analysis, configurable migration tool, and automated, end-user driven collaborative governance

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Discord is a free app offering voice, video, and text chat functionalities, primarily catering to the gaming community. It serves as a hub for users to create and join servers tailored to their interests. Discord’s ecosystem comprises servers, each functioning as a distinct online community with its own channels dedicated to specific topics or activities. Users can engage in text-based discussions, voice calls, or video chats within these channels. Understanding Discord Servers Discord servers are virtual spaces where users congregate to interact, share content, and build communities. Servers may revolve around gaming, hobbies, interests, or fandoms, providing a platform for like-minded individuals to connect. Communication Features Discord offers a range of communication tools, including text channels for messaging, voice channels for real-time audio conversations, and video channels for face-to-face interactions. These features facilitate seamless communication and collaboration. What Does NSFW Mean? The acronym NSFW stands for “Not Safe For Work,” indicating content that may be inappropriate for professional or public settings. NSFW Content NSFW content encompasses material that is sexually explicit, violent, or otherwise graphic in nature. It often includes nudity, profanity, or depictions of sensitive topics.

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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

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Following the popularity of "Cloud Revolution: Exploring the New Wave of Serverless Spatial Data," we're thrilled to announce this much-anticipated encore webinar. In this sequel, we'll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you're building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

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Created by Mozilla Research in 2012 and now part of Linux Foundation Europe, the Servo project is an experimental rendering engine written in Rust. It combines memory safety and concurrency to create an independent, modular, and embeddable rendering engine that adheres to web standards. Stewardship of Servo moved from Mozilla Research to the Linux Foundation in 2020, where its mission remains unchanged. After some slow years, in 2023 there has been renewed activity on the project, with a roadmap now focused on improving the engine’s CSS 2 conformance, exploring Android support, and making Servo a practical embeddable rendering engine. In this presentation, Rakhi Sharma reviews the status of the project, our recent developments in 2023, our collaboration with Tauri to make Servo an easy-to-use embeddable rendering engine, and our plans for the future to make Servo an alternative web rendering engine for the embedded devices industry. (c) Embedded Open Source Summit 2024 April 16-18, 2024 Seattle, Washington (US) https://events.linuxfoundation.org/embedded-open-source-summit/ https://ossna2024.sched.com/event/1aBNF/a-year-of-servo-reboot-where-are-we-now-rakhi-sharma-igalia

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Workshop part2 – Big Data

2. THE MORE DATA YOU COLLECT THE MORE VALUE YOU CAN DERIVE FROM IT

5. THE COST OF DATA GENERATION IS FALLING

8. GENERATE ➔ STORE ➔ ANALYZE ➔ SHARE

9. Lower cost, higher throughput GENERATE ➔ STORE ➔ ANALYZE ➔ SHARE

10. Lower cost, higher throughput GENERATE ➔ STORE ➔ ANALYZE ➔ SHARE Highly constrained

11. + ELASTIC AND HIGHLY SCALABLE + NO UPFRONT CAPITAL EXPENSE + ONLY PAY FOR WHAT YOU USE + AVAILABLE ON-DEMAND ! = REMOVE CONSTRAINTS

12. GENERATE ➔ STORE ➔ ANALYZE ➔ SHARE

13. AWS Import / Export AWS Direct Connect GENERATE ➔ STORE ➔ ANALYZE ➔ SHARE

14. Inbound data transfer is free Multipart upload to S3 Physical media AWS Direct Connect

15. Amazon S3, Amazon Glacier, Amazon DynamoDB, Amazon RDS, Amazon Redshift, AWS Storage Gateway, Data on Amazon EC2 GENERATE ➔ STORE ➔ ANALYZE ➔ SHARE

16. AMAZON S3 SIMPLE STORAGE SERVICE

17. CASE STUDY: SPOTIFY ADDS 20,000 TRACKS/DAY TO ITS CATALOGUE

18. AMAZON DYNAMODB" HIGH-PERFORMANCE, FULLY MANAGED NoSQL DATABASE SERVICE

19. DURABLE & AVAILABLE CONSISTENT, DISK-ONLY WRITES (SSD)

20. LOW LATENCY AVERAGE READS < 5MS, WRITES < 10MS

21. ! ! ! NO ADMINISTRATION

22. CASE STUDY: SHAZAM SUPPORTED 500,000 WRITES/SEC DURING SUPER BOWL

23. AMAZON REDSHIFT" FULLY MANAGED, PETA-BYTE SCALE DATAWAREHOUSE ON AWS

24.

25.

26. 30 MINUTES DOWN TO 12 SECONDS

27.

28. AMAZON REDSHIFT LETS YOU START SMALL AND GROW BIG Extra Large Node (HS1.XL) ! Single Node (2 TB) ! Cluster 2-32 Nodes (4 TB – 64 TB) Eight Extra Large Node (HS1.8XL) Cluster 2-100 Nodes (32 TB – 1.6 PB) 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL 8XL XL XL XL XL XL XL XL XL XL XL XL XL XL XL XL XL XL XL XL XL XL XL XL XL XL XL XL XL XL XL XL XL XL

29. JDBC/ODBC ! !

30.

31.

32.

33.

34. GENERATE ➔ STORE ➔ ANALYZE ➔ SHARE Amazon EC2 Amazon Elastic MapReduce

35. AMAZON EC2 ELASTIC COMPUTE CLOUD

36.

37.

38.

39.

40.

41. 3 HOURS FOR $4828.85/hr

42. Instead of $20+ MILLIONS in infrastructure

43. GPU INSTANCES" ! G2" CG1 1x NVIDIA Kepler GK104 8 vCPU (Intel Xeon E5-2670) $0.65/h 2x NVIDIA Fermi M2050 16 vCPU (Intel Xeon X5570) $2.10/h

44. ON A SINGLE INSTANCE COMPUTE TIME: 4h COST: 4h x $2.1 = $8.4

45. ON MULTIPLE INSTANCES COMPUTE TIME: 1h COST: 1h x 4 x $2.1 = $8.4

46.

47.

48.

49.

50. AMAZON ELASTIC MAPREDUCE HADOOP AS A SERVICE

51. CASE STUDY: "WITH AMAZON EMR WE CAN ANALYZE 100% OF THE DATA, NOT JUST A SAMPLE" - Sanjeevan Bala, Head of Data Planning & Analytics, Channel 4

52. Amazon S3, Amazon DynamoDB, Amazon RDS, Amazon Redshift, Data on Amazon EC2 GENERATE ➔ STORE ➔ ANALYZE ➔ SHARE

53. PUBLIC DATA SETS http://aws.amazon.com/publicdatasets

54.

55.

56. GENERATE ➔ STORE ➔ ANALYZE ➔ SHARE

57. GENERATE ➔ STORE ➔ ANALYZE ➔ SHARE BATCH PROCESSING

58. STREAM PROCESSING GENERATE ➔ ➔ SHARE

59. AMAZON KINESIS REAL-TIME DATA STREAM PROCESSING

60. Hourly server logs: how your systems went wrong an hour ago Weekly / Monthly Bill: What you spent this past billing cycle Daily customer report from your website: tells you what deal or ad to try next time Daily fraud reports: tells you if there was fraud yesterday Daily business reports: tells me how customers used AWS services yesterday Real-time metrics: what just went wrong now Real-time spending alerts/caps: guaranteeing you can’t overspend Real-time analysis: what to offer the current customer now Real-time detection: blocks fraudulent use now Fast ETL into Amazon Redshift: how are customers using services now

61.

62. GENERATE ➔ STORE ➔ ANALYZE ➔ SHARE

63. Amazon S3, Amazon DynamoDB, Amazon RDS, Amazon Redshift, Data on Amazon EC2 Amazon S3, Amazon Glacier, Amazon DynamoDB, Amazon RDS, Amazon Redshift, AWS Storage Gateway, Data on Amazon EC2 GENERATE ➔ STORE ➔ ANALYZE ➔ SHARE Amazon EC2 Amazon Elastic MapReduce AWS Import / Export AWS Direct Connect

64. STREAM PROCESSING GENERATE ➔ ➔ SHARE

65. STREAM PROCESSING Amazon S3, Amazon DynamoDB, Amazon RDS, Amazon Redshift, Data on Amazon EC2 GENERATE ➔ ➔ SHARE Amazon Kinesis Stream Processing on Amazon EC2

66. FROM DATA TO ACTIONABLE INFORMATION

Workshop part2 – Big Data

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