This document discusses serverless real-time data processing using AWS Lambda. It provides an overview of serverless real-time data processing and processing streaming data with Lambda and Amazon Kinesis. It also demonstrates streaming data processing and a data processing pipeline with Lambda and MapReduce. Finally, it discusses Fannie Mae's use of distributed computing with Lambda for financial modeling.
Building Cloud-Native App Series - Part 3 of 11
Microservices Architecture Series
AWS Kinesis Data Streams
AWS Kinesis Firehose
AWS Kinesis Data Analytics
Apache Flink - Analytics
The Multiplatform App Architecture offers the possibility to create mobile applications for multiple mobile platforms and at the same time offer the flexibility to use all native functionality of the mobile operating systems to realize an optimal user experience. Combined with a powerful development environment and a comprehensive programming language it offers an excellent way to develop and maintain rich mobile applications.
Capgemini helps customers to achieve mobile excellence and realizes mobile applications in an agile way using this architecture.
Disaster Recovery of on-premises IT infrastructure with AWS Amazon Web Services
The objective of this session is to enable customers with any level of DR experience to gain actionable guidance to advance their business up the ladder of DR readiness. AWS enables fast disaster recovery of critical on-premises IT systems without incurring the complexity and expense of a second physical site. With 28 availability zones in 11 regions around the world and a broad set of services, AWS can deliver rapid recovery of on-premises IT infrastructure and data. During this session we will walk you through the ascending levels of DR options made possible with AWS and review the technologies and services that help deliver various DR capabilities, starting from cloud backups all the way up to hot site DR. We will also explore various DR architectures and the balance of recovery time and cost.
As part of this session, I will be giving an introduction to Data Engineering and Big Data. It covers up to date trends.
* Introduction to Data Engineering
* Role of Big Data in Data Engineering
* Key Skills related to Data Engineering
* Role of Big Data in Data Engineering
* Overview of Data Engineering Certifications
* Free Content and ITVersity Paid Resources
Don't worry if you miss the video - you can click on the below link to go through the video after the schedule.
https://youtu.be/dj565kgP1Ss
* Upcoming Live Session - Overview of Big Data Certifications (Spark Based) - https://www.meetup.com/itversityin/events/271739702/
Relevant Playlists:
* Apache Spark using Python for Certifications - https://www.youtube.com/playlist?list=PLf0swTFhTI8rMmW7GZv1-z4iu_-TAv3bi
* Free Data Engineering Bootcamp - https://www.youtube.com/playlist?list=PLf0swTFhTI8pBe2Vr2neQV7shh9Rus8rl
* Join our Meetup group - https://www.meetup.com/itversityin/
* Enroll for our labs - https://labs.itversity.com/plans
* Subscribe to our YouTube Channel for Videos - http://youtube.com/itversityin/?sub_confirmation=1
* Access Content via our GitHub - https://github.com/dgadiraju/itversity-books
* Lab and Content Support using Slack
Building Cloud-Native App Series - Part 3 of 11
Microservices Architecture Series
AWS Kinesis Data Streams
AWS Kinesis Firehose
AWS Kinesis Data Analytics
Apache Flink - Analytics
The Multiplatform App Architecture offers the possibility to create mobile applications for multiple mobile platforms and at the same time offer the flexibility to use all native functionality of the mobile operating systems to realize an optimal user experience. Combined with a powerful development environment and a comprehensive programming language it offers an excellent way to develop and maintain rich mobile applications.
Capgemini helps customers to achieve mobile excellence and realizes mobile applications in an agile way using this architecture.
Disaster Recovery of on-premises IT infrastructure with AWS Amazon Web Services
The objective of this session is to enable customers with any level of DR experience to gain actionable guidance to advance their business up the ladder of DR readiness. AWS enables fast disaster recovery of critical on-premises IT systems without incurring the complexity and expense of a second physical site. With 28 availability zones in 11 regions around the world and a broad set of services, AWS can deliver rapid recovery of on-premises IT infrastructure and data. During this session we will walk you through the ascending levels of DR options made possible with AWS and review the technologies and services that help deliver various DR capabilities, starting from cloud backups all the way up to hot site DR. We will also explore various DR architectures and the balance of recovery time and cost.
As part of this session, I will be giving an introduction to Data Engineering and Big Data. It covers up to date trends.
* Introduction to Data Engineering
* Role of Big Data in Data Engineering
* Key Skills related to Data Engineering
* Role of Big Data in Data Engineering
* Overview of Data Engineering Certifications
* Free Content and ITVersity Paid Resources
Don't worry if you miss the video - you can click on the below link to go through the video after the schedule.
https://youtu.be/dj565kgP1Ss
* Upcoming Live Session - Overview of Big Data Certifications (Spark Based) - https://www.meetup.com/itversityin/events/271739702/
Relevant Playlists:
* Apache Spark using Python for Certifications - https://www.youtube.com/playlist?list=PLf0swTFhTI8rMmW7GZv1-z4iu_-TAv3bi
* Free Data Engineering Bootcamp - https://www.youtube.com/playlist?list=PLf0swTFhTI8pBe2Vr2neQV7shh9Rus8rl
* Join our Meetup group - https://www.meetup.com/itversityin/
* Enroll for our labs - https://labs.itversity.com/plans
* Subscribe to our YouTube Channel for Videos - http://youtube.com/itversityin/?sub_confirmation=1
* Access Content via our GitHub - https://github.com/dgadiraju/itversity-books
* Lab and Content Support using Slack
AWS re:Invent 2016: Building a Solid Business Case for Cloud Migration (ENT308)Amazon Web Services
Learn how to create a compelling business case for a large-scale migration to AWS. We present a framework and tools for creating your business case, and guidelines for using AWS services to maximize value and optimize cost for migrations to the AWS Cloud. Learn a new way of thinking about cost that includes automation, new technologies, organizational change, and other factors.
The Webinar takes participants through the entire cloud migration life-cycle – from initial analysis to final migration. We evaluate the leading cloud DBMS offerings from Amazon, Microsoft and Oracle. We also compare IaaS and DBaaS to better understand the two architectures and identify the most appropriate use case for each platform.
We finish by providing RDX’s recommended database migration procedures and the vendor utilities you can leverage to ensure trouble-free cloud transitions. Learn from experts who have migrated dozens of on-premises systems to the cloud!
Real-time Streaming and Querying with Amazon Kinesis and Amazon Elastic MapRe...Amazon Web Services
Originally, Hadoop was used as a batch analytics tool; however, this is rapidly changing, as applications move towards real-time processing and streaming. Amazon Elastic MapReduce has made running Hadoop in the cloud easier and more accessible than ever. Each day, tens of thousands of Hadoop clusters are run on the Amazon Elastic MapReduce infrastructure by users of every size — from university students to Fortune 50 companies. We recently launched Amazon Kinesis – a managed service for real-time processing of high volume, streaming data. Amazon Kinesis enables a new class of big data applications which can continuously analyze data at any volume and throughput, in real-time. Adi will discuss each service, dive into how customers are adopting the services for different use cases, and share emerging best practices. Learn how you can architect Amazon Kinesis and Amazon Elastic MapReduce together to create a highly scalable real-time analytics solution which can ingest and process terabytes of data per hour from hundreds of thousands of different concurrent sources. Forever change how you process web site click-streams, marketing and financial transactions, social media feeds, logs and metering data, and location-tracking events.
2025년도 까지의 데이터 시장의 전망을 살펴보고, 클라우드 상에서 데이터를 효율적으로 다루기 위한 서비스들을 소개합니다 | Explore the prospects for the data market by 2025 and introduce services to efficiently address data in the cloud
Breakthrough OLAP performance with Cassandra and SparkEvan Chan
Find out about breakthrough architectures for fast OLAP performance querying Cassandra data with Apache Spark, including a new open source project, FiloDB.
Modern data lakes are now built on cloud storage, helping organizations leverage the scale and economics of object storage while simplifying overall data storage and analysis flow
Amazon Relational Database Service (Amazon RDS) is a web service that makes it easier to set up, operate, and scale a relational database in the cloud. It provides cost-efficient, re-sizable capacity for an industry-standard relational database and manages common database administration tasks
Cloud Migration Cookbook: A Guide To Moving Your Apps To The CloudNew Relic
The process of building new apps or migrating existing apps to a cloud-based platform is complex. There are hundreds of paths you can take and only a few will make sense for you and your business. Get a step-by-step guide on how to plan for a successful app migration.
ArgoCD and Tekton: Match made in Kubernetes heaven | DevNation Tech TalkRed Hat Developers
Tekton Pipelines is an open-source project that focuses on providing a Kubernetes-native, lightweight, easy-to-manage serverless CI/CD framework. Tekton is built for Kubernetes and runs delivery pipelines in pods to scale on demand, and allow teams to fully control their pipelines and dependencies. ArgoCD is a declarative GitOps operator and makes continuous delivery possible by using Git as source of truth for declarative infrastructure and applications. In this session, you will learn how to combine the power of Tekton Pipelines with ArgoCD for a declarative approach to CI/CD based on GitOps principles.
In this session we will review Amazon EFS and how it delivers fully managed, petabyte-scale file storage for Amazon EC2 instances. Large scale and consistent performance make Amazon EFS ideal for web and content serving, enterprise applications, media processing, container storage, and Big Data analytics use cases. Session attendees will learn how to identify appropriate applications for use with Amazon EFS, understand performance details and security models, and hear how established customers are using it in production. The target audience is file system administrators, application developers, and application owners that operate or build file-based applications that require consistent latencies at cloud scale.
Walking Through Spring Cloud Data FlowVMware Tanzu
SpringOne 2020
Walking Through Spring Cloud Data Flow
Glenn Renfro, Software Developer at VMware
Ilayaperumal Gopinathan, Staff Software Engineer at VMware
AWS re:Invent 2016: Building a Solid Business Case for Cloud Migration (ENT308)Amazon Web Services
Learn how to create a compelling business case for a large-scale migration to AWS. We present a framework and tools for creating your business case, and guidelines for using AWS services to maximize value and optimize cost for migrations to the AWS Cloud. Learn a new way of thinking about cost that includes automation, new technologies, organizational change, and other factors.
The Webinar takes participants through the entire cloud migration life-cycle – from initial analysis to final migration. We evaluate the leading cloud DBMS offerings from Amazon, Microsoft and Oracle. We also compare IaaS and DBaaS to better understand the two architectures and identify the most appropriate use case for each platform.
We finish by providing RDX’s recommended database migration procedures and the vendor utilities you can leverage to ensure trouble-free cloud transitions. Learn from experts who have migrated dozens of on-premises systems to the cloud!
Real-time Streaming and Querying with Amazon Kinesis and Amazon Elastic MapRe...Amazon Web Services
Originally, Hadoop was used as a batch analytics tool; however, this is rapidly changing, as applications move towards real-time processing and streaming. Amazon Elastic MapReduce has made running Hadoop in the cloud easier and more accessible than ever. Each day, tens of thousands of Hadoop clusters are run on the Amazon Elastic MapReduce infrastructure by users of every size — from university students to Fortune 50 companies. We recently launched Amazon Kinesis – a managed service for real-time processing of high volume, streaming data. Amazon Kinesis enables a new class of big data applications which can continuously analyze data at any volume and throughput, in real-time. Adi will discuss each service, dive into how customers are adopting the services for different use cases, and share emerging best practices. Learn how you can architect Amazon Kinesis and Amazon Elastic MapReduce together to create a highly scalable real-time analytics solution which can ingest and process terabytes of data per hour from hundreds of thousands of different concurrent sources. Forever change how you process web site click-streams, marketing and financial transactions, social media feeds, logs and metering data, and location-tracking events.
2025년도 까지의 데이터 시장의 전망을 살펴보고, 클라우드 상에서 데이터를 효율적으로 다루기 위한 서비스들을 소개합니다 | Explore the prospects for the data market by 2025 and introduce services to efficiently address data in the cloud
Breakthrough OLAP performance with Cassandra and SparkEvan Chan
Find out about breakthrough architectures for fast OLAP performance querying Cassandra data with Apache Spark, including a new open source project, FiloDB.
Modern data lakes are now built on cloud storage, helping organizations leverage the scale and economics of object storage while simplifying overall data storage and analysis flow
Amazon Relational Database Service (Amazon RDS) is a web service that makes it easier to set up, operate, and scale a relational database in the cloud. It provides cost-efficient, re-sizable capacity for an industry-standard relational database and manages common database administration tasks
Cloud Migration Cookbook: A Guide To Moving Your Apps To The CloudNew Relic
The process of building new apps or migrating existing apps to a cloud-based platform is complex. There are hundreds of paths you can take and only a few will make sense for you and your business. Get a step-by-step guide on how to plan for a successful app migration.
ArgoCD and Tekton: Match made in Kubernetes heaven | DevNation Tech TalkRed Hat Developers
Tekton Pipelines is an open-source project that focuses on providing a Kubernetes-native, lightweight, easy-to-manage serverless CI/CD framework. Tekton is built for Kubernetes and runs delivery pipelines in pods to scale on demand, and allow teams to fully control their pipelines and dependencies. ArgoCD is a declarative GitOps operator and makes continuous delivery possible by using Git as source of truth for declarative infrastructure and applications. In this session, you will learn how to combine the power of Tekton Pipelines with ArgoCD for a declarative approach to CI/CD based on GitOps principles.
In this session we will review Amazon EFS and how it delivers fully managed, petabyte-scale file storage for Amazon EC2 instances. Large scale and consistent performance make Amazon EFS ideal for web and content serving, enterprise applications, media processing, container storage, and Big Data analytics use cases. Session attendees will learn how to identify appropriate applications for use with Amazon EFS, understand performance details and security models, and hear how established customers are using it in production. The target audience is file system administrators, application developers, and application owners that operate or build file-based applications that require consistent latencies at cloud scale.
Walking Through Spring Cloud Data FlowVMware Tanzu
SpringOne 2020
Walking Through Spring Cloud Data Flow
Glenn Renfro, Software Developer at VMware
Ilayaperumal Gopinathan, Staff Software Engineer at VMware
Building Big Data Applications with Serverless Architectures - June 2017 AWS...Amazon Web Services
Learning Objectives:
- Use cases and best practices for serverless big data applications
- Leverage AWS technologies such as AWS Lambda and Amazon Kinesis
- Learn to perform ETL, event processing, ad-hoc analysis, real-time processing, and MapReduce with serverless
Building data processing applications is challenging and time-consuming, and often requires specialized expertise to deploy and operate. With serverless computing, you can perform real-time stream processing of multiple data types without needing to spin up servers or install software, allowing you to deploy big data applications quickly and more easily. Come learn how you can use AWS Lambda with Amazon Kinesis to analyze streaming data in real-time and then store the results in a managed NoSQL database such as Amazon DynamoDB. You’ll learn tips and tricks for doing in-line processing, data manipulation, and even distributed MapReduce on large data sets.
Serverless architecture can eliminate the need to provision and manage servers required to process files or streaming data in real time.
In this session, we will cover the fundamentals of using AWS Lambda to process data from sources such as Amazon DynamoDB Streams, Amazon Kinesis, and Amazon S3. We will walk through sample use cases for real-time data processing and discuss best practices on using these services together. We will then demonstrate how to set up a real-time stream processing solution using just Amazon Kinesis and AWS Lambda, all without the need to run or manage servers.
Serverless architectures can eliminate the need to provision and manage servers required to process files or streaming data in real time. In this session, we will cover the fundamentals of using AWS Lambda to process data from sources such as Amazon DynamoDB Streams, Amazon Kinesis, and Amazon S3. We will walk through sample use cases for real-time data processing and discuss best practices on using these services together. We will then demonstrate run a live demonstration on how to set up a real-time stream processing solution using just Amazon Kinesis and AWS Lambda, all without the need to run or manage servers.
Learning Objectives:
• Learn the fundamentals of using AWS Lambda with various AWS data sources
• Understand best practices of using AWS Lambda with Amazon Kinesis
Who Should Attend:
• Developers
AWS May Webinar Series - Streaming Data Processing with Amazon Kinesis and AW...Amazon Web Services
If you are interested to know more about AWS Chicago Summit, please use the following to register: http://amzn.to/1RooPPL
Amazon Kinesis is a fully managed, cloud-based service for real-time data processing over large, distributed data streams. AWS Lambda is a compute service that runs your code in response to events and automatically manages the compute resources for you. AWS Lambda can run code in response to data in Amazon Kinesis streams, making it easy to build big data applications that respond quickly to new information. In this webinar, we will cover key Kinesis and Lambda features, walk through sample use cases for stream processing, and discuss best practices on using the services together. We'll then demonstrate setting up an Amazon Kinesis stream and an associated Lambda function to capture and perform custom computations on click-stream data, all without setting up any infrastructure.
Learning Objectives: • Understand key Amazon Kinesis and AWS Lambda features • Learn how to setup streaming data capture and processing framework using AWS Lambda • Learn sample use cases, best practices and tips on using AWS Lambda with Amazon Kinesis
Who Should Attend: • Developers, Devops Engineers, IT Operations Professionals
Nesta sessão faremos uma demonstração de controle e defesa de tráfego aéreo utilizando processamento em tempo real. Trataremos das boas práticas para ingestão, armazenamento, processamento e visualização de dados através de serviços da AWS como Kinesis, DynamoDB, Lambda, Redshift, Quicksight e Amazon Machine Learning.
Deep Dive and Best Practices for Real Time Streaming ApplicationsAmazon Web Services
Get answers to technical questions, frequently asked by those starting to work with streaming data. Learn best practices for building a real-time streaming data architecture on AWS with Amazon Kinesis, Spark Streaming, AWS Lambda, and Amazon EMR. First, we will focus on building a scalable, durable streaming data ingestion workflow from data producers like mobile devices, servers, or even web browsers. We will provide guidelines to minimize duplicates and achieve exactly-once processing semantics in your stream-processing applications. Then, we will show some of the proven architectures for processing streaming data using a combination of tools including Amazon Kinesis Stream, AWS Lambda, and Spark Streaming running on Amazon EMR.
AWS April 2016 Webinar Series - Getting Started with Real-Time Data Analytics...Amazon Web Services
It is becoming increasingly important to analyze real time streaming data. It allows organizations to remain competitive by uncovering relevant, actionable insights. AWS makes it easy to capture, store, and analyze real-time streaming data.
In this webinar, we will guide you through some of the proven architectures for processing streaming data, using a combination of tools including Amazon Kinesis Streams, AWS Lambda, and Spark Streaming on Amazon Elastic MapReduce (EMR). We will then talk about common use cases and best practices for real-time data analysis on AWS.
Learning Objectives:
Understand how you can analyze real-time data streams using Amazon Kinesis, AWS Lambda, and Spark running on Amazon EMR
Learn use cases and best practices for streaming data applications on AWS
AWS Lambda allows any Node.js app to be run at scale in a massively parallel environment with no up-front costs or planning. This session shows how to use Lambda to build dynamic analytic data flows that can be tuned as they execute, based on initial results, to provide real-time output streamed to web clients. This process enables a cost-effective and responsive user experience for ad hoc big data jobs and lets developers focus on how data is consumed and presented, instead of how it is obtained.
NASA LandSat data can be stored, transformed, navigated, and visualized. In this session we will explore how the LandSat dataset is stored in Amazon Simple Storage Service (S3), one of the recommended cloud storage services in AWS for storage of petabytes of data, and how data stored in S3 can be processed on the server with the Lambda service, visualized for users, and made available to search engines.
Create by: Ben Snively, Senior Solutions Architect
Getting Started with Serverless Architectures | AWS Public Sector Summit 2016Amazon Web Services
By building your application with AWS Lambda, Amazon API Gateway, and Amazon DynamoDB, you can free yourself from the burden of managing servers while gaining agility and simple scaling. After introducing the basics of building microservices with AWS Lambda and Amazon API Gateway, the session highlights how the Democratic National Committee (DNC) Technology Team uses AWS Lambda and Amazon DynamoDB microservices to provide campaigns and state parties customized applications on top of a core data platform. This serverless architecture has helped the DNC Technology Team improve their microservice functionality and development process, ensuring their applications are performant through the extremely erratic usage levels of a campaign cycle.
AWS re:Invent 2016: Real-time Data Processing Using AWS Lambda (SVR301)Amazon Web Services
Serverless architecture can eliminate the need to provision and manage servers required to process files or streaming data in real time.
In this session, we will cover the fundamentals of using AWS Lambda to process data in real-time from push sources such as AWS Iot and pull sources such as Amazon DynamoDB Streams or Amazon Kinesis. We will walk through sample use cases and demonstrate how to set up some of these real-time data processing solutions. We'll also discuss best practices and do a deep dive into AWS Lambda real-time stream processing.
You also hear from speakers from Thomson Reuters, who discuss how the company leverages AWS for its Product Insight service. The service provides insights to collect usage analytics for Thomson Reuters products. The speakers walk through its architecture and demonstrate how they leverage Amazon Kinesis Streams, Amazon Kinesis Firehose, AWS Lambda, Amazon S3, Amazon Route 53, and AWS KMS for near real-time access to data being collected around the globe. They also outline how applying AWS methodologies benefited its business, such as time-to-market and cross-region ingestion, auto-scaling capabilities, low-latency, security features, and extensibility.
AWS re:Invent 2016: Event Handling at Scale: Designing an Auditable Ingestion...Amazon Web Services
How does McGraw-Hill Education use the AWS platform to scale and reliably receive 10,000 learning events per second? How do we provide near-real-time reporting and event-driven analytics for hundreds of thousands of concurrent learners in a reliable, secure, and auditable manner that is cost effective? MHE designed and implemented a robust solution that integrates AWS API Gateway, AWS Lambda, Amazon Kinesis, Amazon S3, Amazon Elasticsearch Service, Amazon DynamoDB, HDFS, Amazon EMR, Amazopn EC2, and other technologies to deliver this cloud-native platform across the US and soon the world. This session describes the challenges we faced, architecture considerations, how we gained confidence for a successful production roll-out, and the behind-the-scenes lessons we learned.
Similar to SMC303 Real-time Data Processing Using AWS Lambda (20)
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS.
In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Amazon Web Services
La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup.
Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti.
Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.
Ora puoi utilizzare Amazon Elastic Kubernetes Service (EKS) per eseguire pod Kubernetes su AWS Fargate, il motore di elaborazione serverless creato per container su AWS. Questo rende più semplice che mai costruire ed eseguire le tue applicazioni Kubernetes nel cloud AWS.In questa sessione presenteremo le caratteristiche principali del servizio e come distribuire la tua applicazione in pochi passaggi
Vent'anni fa Amazon ha attraversato una trasformazione radicale con l'obiettivo di aumentare il ritmo dell'innovazione. In questo periodo abbiamo imparato come cambiare il nostro approccio allo sviluppo delle applicazioni ci ha permesso di aumentare notevolmente l'agilità, la velocità di rilascio e, in definitiva, ci ha consentito di creare applicazioni più affidabili e scalabili. In questa sessione illustreremo come definiamo le applicazioni moderne e come la creazione di app moderne influisce non solo sull'architettura dell'applicazione, ma sulla struttura organizzativa, sulle pipeline di rilascio dello sviluppo e persino sul modello operativo. Descriveremo anche approcci comuni alla modernizzazione, compreso l'approccio utilizzato dalla stessa Amazon.com.
Come spendere fino al 90% in meno con i container e le istanze spot Amazon Web Services
L’utilizzo dei container è in continua crescita.
Se correttamente disegnate, le applicazioni basate su Container sono molto spesso stateless e flessibili.
I servizi AWS ECS, EKS e Kubernetes su EC2 possono sfruttare le istanze Spot, portando ad un risparmio medio del 70% rispetto alle istanze On Demand. In questa sessione scopriremo insieme quali sono le caratteristiche delle istanze Spot e come possono essere utilizzate facilmente su AWS. Impareremo inoltre come Spreaker sfrutta le istanze spot per eseguire applicazioni di diverso tipo, in produzione, ad una frazione del costo on-demand!
In recent months, many customers have been asking us the question – how to monetise Open APIs, simplify Fintech integrations and accelerate adoption of various Open Banking business models. Therefore, AWS and FinConecta would like to invite you to Open Finance marketplace presentation on October 20th.
Event Agenda :
Open banking so far (short recap)
• PSD2, OB UK, OB Australia, OB LATAM, OB Israel
Intro to Open Finance marketplace
• Scope
• Features
• Tech overview and Demo
The role of the Cloud
The Future of APIs
• Complying with regulation
• Monetizing data / APIs
• Business models
• Time to market
One platform for all: a Strategic approach
Q&A
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Amazon Web Services
Per creare valore e costruire una propria offerta differenziante e riconoscibile, le startup di successo sanno come combinare tecnologie consolidate con componenti innovativi creati ad hoc.
AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta.
Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.
OpsWorks Configuration Management: automatizza la gestione e i deployment del...Amazon Web Services
Con l'approccio tradizionale al mondo IT per molti anni è stato difficile implementare tecniche di DevOps, che finora spesso hanno previsto attività manuali portando di tanto in tanto a dei downtime degli applicativi interrompendo l'operatività dell'utente. Con l'avvento del cloud, le tecniche di DevOps sono ormai a portata di tutti a basso costo per qualsiasi genere di workload, garantendo maggiore affidabilità del sistema e risultando in dei significativi miglioramenti della business continuity.
AWS mette a disposizione AWS OpsWork come strumento di Configuration Management che mira ad automatizzare e semplificare la gestione e i deployment delle istanze EC2 per mezzo di workload Chef e Puppet.
Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.
Dal riconoscimento facciale al riconoscimento di frodi o difetti di fabbricazione, l'analisi di immagini e video che sfruttano tecniche di intelligenza artificiale, si stanno evolvendo e raffinando a ritmi elevati. In questo webinar esploreremo le possibilità messe a disposizione dai servizi AWS per applicare lo stato dell'arte delle tecniche di computer vision a scenari reali.
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.
Crea la tua prima serverless ledger-based app con QLDB e NodeJSAmazon 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.
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.
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareAmazon Web Services
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.
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.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
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Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
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The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
2. Agenda
What’s Serverless Real-Time Data Processing?
Processing Streaming Data with Lambda and Kinesis
Streaming Data Processing Demo
Data Processing Pipeline with Lambda and MapReduce
Building a Big Data Processing Solution Demo
What’s Serverless Real-Time Data Processing?
Serverless Processing of Real-Time Streaming Data
Streaming Data Processing Demo
Serverless Data Processing with Distributed Computing
Customer Story:
Fannie Mae-Distributed Computing with Lambda
4. AWS Lambda
Efficient performance at scale Easy to author, deploy,
maintain, secure & manage. Focus on business logic
to build back-end services that perform at scale.
Bring Your Own Code: Stateless, event-driven code
with native support for Node.js, Java, Python and C#
languages.
No Infrastructure to manage: Compute without
managing infrastructure like Amazon EC2 instances
and Auto Scaling groups.
Cost-effective: Automatically matches capacity to
request rate. Compute cost 100 ms increments.
Triggered by events: Direct Sync & Async API calls,
AWS service integrations, and 3rd party triggers.
6. Serverless Real-Time Data Processing Is..
Capture Data
Streams
IoT Data
Financial
Data
Log Data
No servers to
provision or
manage
EVENT SOURCE
Node.js
Python
Java
C#
Process Data
Streams
FUNCTION
Clickstream
Data
Output
Data
DATABASE
CLOUD
SERVICES
7. Amazon
DynamoDB
Amazon
Kinesis
Amazon
S3
Amazon
SNS
ASYNCHRONOUS PUSH MODEL
STREAM PULL MODEL
Lambda Real-Time Event Sources
Amazon
Alexa
AWS
IoT
SYNCHRONOUS PUSH MODEL
Mapping owned by Event Source
Mapping owned by Lambda
Invokes Lambda via Event Source API
Lambda function invokes when new
records found on stream
Resource-based policy permissions
Lambda Execution role policy permissions
Concurrent executions
Sync invocation
Async Invocation
Sync invocation
Lambda polls the streams
HOW IT WORKS
9. Amazon Kinesis
Real-Time: Collect real-time data streams and
promptly respond to key business events and
operational triggers. Real-time latencies.
Easy to use: Focus on quickly launching data
streaming applications instead of managing
infrastructure.
Amazon Kinesis Offering: Managed services for
streaming data ingestion and processing.
• Amazon Kinesis Streams: Build applications
that process or analyze streaming data.
• Amazon Kinesis Firehose: Load massive
volumes of streaming data into Amazon S3
and Amazon Redshift.
• Amazon Kinesis Analytics: Analyze data
streams using SQL queries.
10. Processing Real-Time Streams: Lambda + Amazon Kinesis
Streaming data sent to Amazon
Kinesis and stored in shards
Multiple Lambda functions can be
triggered to process same Amazon
Kinesis stream for “fan out”
Lambda can process data and store
results ex. to DynamoDB, S3
Lambda can aggregate data to
services like Amazon Elasticsearch
Service for analytics
Lambda sends event data and
function info to Amazon CloudWatch
for capturing metrics and monitoring
Amazon
Kinesis
AWS
Lambda
Amazon
CloudWatch
Amazn
DynamoDB
AWS
Lambda
Amazon
Elasticsearch Service
Amazon
S3
11. Processing Streams: Set Up Amazon Kinesis Stream
Streams
Made up of Shards
Each Shard ingests/reads data up to 1 MB/sec
Each Shard emits/writes data up to 2 MB/sec
Each shard supports 5 reads/sec
Data
All data is stored and is replayable for 24 hours
Make sure partition key distribution is even to optimize parallel throughput
Partition key used to distribute PUTs across shards, choose key with more groups than
shards
Best Practice
Determine an initial size/shards to plan for expected maximum demand
Leverage “Help me decide how many shards I need” option in Console
Use formula for Number Of Shards:
max(incoming_write_bandwidth_in_KB/1000, outgoing_read_bandwidth_in_KB / 2000)
12. Processing Streams: Create Lambda functions
Memory
CPU allocation proportional to the memory configured
Increasing memory makes your code execute faster (if CPU bound)
Increasing memory allows for larger record sizes processed
Timeout
Increasing timeout allows for longer functions, but longer wait in case of errors
Permission model
Execution role defined for Lambda must have permission to access the stream
Retries
With Amazon Kinesis, Lambda retries until the data expires
(24 hours)
Best Practice
Write Lambda function code to be stateless
Instantiate AWS clients & database clients outside the scope of the function handler
13. Processing Streams: Configure Event Source
Amazon Kinesis mapped as event source in Lambda
Batch size
Max number of records that Lambda will send to one invocation
Not equivalent to effective batch size
Effective batch size is every 250 ms – Calculated as:
MIN(records available, batch size, 6MB)
Increasing batch size allows fewer Lambda function invocations with more
data processed per function
Best Practices
Set to “Trim Horizon” for reading from start of
stream (all data)
Set to “Latest” for reading most recent data (LIFO) (latest data)
14. Processing streams: How It Works
Polling
Concurrent polling and processing per shard
Lambda polls every 250 ms if no records found
Will grab as much data as possible in one GetRecords call (Batch)
Batching
Batches are passed for invocation to Lambda through
function parameters
Batch size may impact duration if the Lambda function
takes longer to process more records
Sub batch in memory for invocation payload
Synchronous invocation
Batches invoked as synchronous RequestResponse type
Lambda honors Amazon Kinesis at least once semantics
Each shard blocks in order of synchronous invocation
15. Processing streams: Tuning throughput
If put / ingestion rate is greater than the theoretical throughput, your
processing is at risk of falling behind
Maximum theoretical throughput
# shards * 2MB / Lambda function duration (s)
Effective theoretical throughput
# shards * batch size (MB) / Lambda function duration (s)
… …
Source
Amazon Kinesis
Destination
1
Lambda
Destination
2
FunctionsShards
Lambda will scale automaticallyScale Amazon Kinesis by splitting or merging shards
Waits for responsePolls a batch
16. Processing streams: Tuning Throughput w/ Retries
Retries
Will retry on execution failures until the record is expired
Throttles and errors impacts duration and directly impacts throughput
Best Practice
Retry with exponential backoff of up to 60s
Effective theoretical throughput with retries
( # shards * batch size (MB) ) / ( function duration (s) * retries until expiry)
… …
Source
Amazon Kinesis
Destination
1
Lambda
Destination
2
FunctionsShards
Lambda will scale automaticallyScale Amazon Kinesis by splitting or merging shards
Receives errorPolls a batch
Receives error
Receives success
17. Processing streams: Common observations
Effective batch size may be less than configured during low throughput
Effective batch size will increase during higher throughput
Increased Lambda duration -> decreased # of invokes and GetRecord calls
Too many consumers of your stream may compete with Amazon Kinesis read
limits and induce ReadProvisionedThroughputExceeded errors and metrics
Amazon
Kinesis
AWS
Lambda
18. Processing streams: Monitoring with Cloudwatch
• GetRecords: (effective throughput)
• PutRecord : bytes, latency, records, etc
• GetRecords.IteratorAgeMilliseconds: how old your
last processed records were
Monitoring Amazon Kinesis Streams
Monitoring Lambda functions
• Invocation count: Time function invoked
• Duration: Execution/processing time
• Error count: Number of Errors
• Throttle count: Number of time function throttled
• Iterator Age: Time elapsed from batch received &
final record written to stream
• Review All Metrics
• Make Custom logs
• View RAM consumed
• Search for log events
Debugging
AWS X-Ray
Coming soon!
21. Serverless Distributed Computing: Map-Reduce Model
Why Serverless Data Processing with Distributed
Computing?
Remove Difficult infrastructure management
Cluster administration
Complex configuration tools
Enable simple, elastic, user-friendly distributed data
processing
Eliminate complexity of state management
Bring Distributed Computing power to the masses
22. Serverless Distributed Computing: Map-Reduce Model
Why Serverless Data Processing with Distributed
Computing?
Eliminate utilization concerns
Makes code simpler by removes complexities of multi-
threading processing to optimize server usage
Cost-effective option to run ad hoc MapReduce jobs
Easier, automatic horizontal scaling
Provide ability to process scientific and analytics
applications
24. Serverless Distributed Computing: PyWren
PyWren Prototype Developed at University of California, Berkeley
Uses Python with AWS Lambda stateless functions for large scale data
analytics
Achieved @ 30-40 MB/s write and read performance per-core to S3
object store
Scaled to 60-80 GB/s across 2800 simultaneous functions
25. Serverless Distributed Computing: Benchmark
Using Amazon MapReduce Reference Architecture Framework
with Lambda
Dataset
Queries:
Scan query (90 M Rows, 6.36 GB of data)
Select query on Page Rankings
Aggregation query on UserVisits ( 775M rows, ~127GB of
data)
Rankings
(rows)
Rankings
(bytes)
UserVisits
(rows)
UserVisits
(bytes)
Documents
(bytes)
90 Million 6.38 GB 775 Million 126.8 GB 136.9 GB
26. Serverless Distributed Computing: Benchmark
Using Amazon MapReduce Reference Architecture Framework
with Lambda
Subset of the Amplab benchmark ran to compare with other data
processing frameworks
Performance Benchmarks: Execution time for each workload in seconds
TECHNOLOGY SCAN 1A SCAN 1B AGGREGATE 2A
Amazon Redshift (HDD) 2.49 2.61 25.46
Serverless MapReduce 39 47 200
Impala - Disk - 1.2.3 12.015 12.015 113.72
Impala - Mem - 1.2.3 2.17 3.01 84.35
Shark - Disk - 0.8.1 6.6 7 151.4
Shark - Mem - 0.8.1 1.7 1.8 83.7
Hive - 0.12 YARN 50.49 59.93 730.62
Tez - 0.2.0 28.22 36.35 377.48
39. Data Processing with AWS: Next steps
Learn more about AWS Serverless at
https://aws.amazon.com/serverless
Explore the AWS Lambda Reference Architecture on GitHub:
Real-Time Streaming:
https://github.com/awslabs/lambda-refarch-
streamprocessing
Distributed Computing Reference Architecture
(serverless MapReduce)
https://github.com/awslabs/lambda-refarch-mapreduce
40. Data Processing with AWS: Next steps
Create an Amazon Kinesis stream. Visit the Amazon Kinesis
Console and configure a stream to receive data Ex. data from
Social media feeds.
Create & test a Lambda function to process streams from Amazon
Kinesis by visiting Lambda console. First 1M requests each month
are on us!
Read the Developer Guide and try the Lambda and Amazon
Kinesis Tutorial:
http://docs.aws.amazon.com/lambda/latest/dg/with-
kinesis.html
Send questions, comments, feedback to the AWS Lambda Forums