This document provides an overview of big data architectural patterns and best practices. It discusses challenges like how to structure data systems, what tools to use, and reference architectures. Key principles discussed include building decoupled systems, using the right tool for specific jobs, and leveraging managed AWS services. Different data types and characteristics are covered, along with common data stores like databases, streams, files and their best uses. Common big data processing types like batch, interactive, streaming and AI are outlined. Example architectures are provided for real-time analytics, interactive and batch analytics. The document concludes with a customer story from DNA Oy about their move to real-time data architectures.
Big Data Architectural Patterns and Best Practices on AWSAmazon Web Services
In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. John Pignata, AWS Startup Solutions Architect, will discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. He will provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
BDA307 Real-time Streaming Applications on AWS, Patterns and Use CasesAmazon Web Services
In this session, you will learn best practices for implementing simple to advanced real-time streaming data use cases on AWS. First, we will review decision points on near real-time versus real time scenarios. Next, we will take a look at streaming data architecture patterns that include Amazon Kinesis Analytics, Amazon Kinesis Firehose, Amazon Kinesis Streams, Spark Streaming on Amazon EMR, and other open source libraries. Finally, we will dive deep into the most common of these patterns and cover design and implementation considerations.
Big Data Architectural Patterns and Best Practices on AWSAmazon Web Services
In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. John Pignata, AWS Startup Solutions Architect, will discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. He will provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
BDA307 Real-time Streaming Applications on AWS, Patterns and Use CasesAmazon Web Services
In this session, you will learn best practices for implementing simple to advanced real-time streaming data use cases on AWS. First, we will review decision points on near real-time versus real time scenarios. Next, we will take a look at streaming data architecture patterns that include Amazon Kinesis Analytics, Amazon Kinesis Firehose, Amazon Kinesis Streams, Spark Streaming on Amazon EMR, and other open source libraries. Finally, we will dive deep into the most common of these patterns and cover design and implementation considerations.
Cloud computing gives you a number of advantages, such as the ability to scale your web application or website on demand. If you have a new web application and want to use cloud computing, you might be asking yourself, "Where do I start?" Join us in this session to understand best practices for scaling your resources from zero to millions of users. We show you how to best combine different AWS services, how to make smarter decisions for architecting your application, and how to scale your infrastructure in the cloud.
Big Data Architectural Patterns and Best Practices on AWSAmazon Web Services
The world is producing an ever increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
ENT302 Deep Dive on AWS Management Tools and New LaunchesAmazon Web Services
As companies shift workloads into the cloud, IT organizations are required to manage an increasing number of cloud resources. AWS provides a broad set of services that help IT organizations with provisioning, tracking, auditing, configuration management, and cost management of their AWS resources. In this session, we will explore the AWS Management Tools suite of services that support the lifecycle management of AWS resources at scale and enable IT governance and compliance. The Deep Dive on AWS Management Tools session will benefit both new and experienced IT administrators, systems administrators, and developers operating infrastructure on AWS and interested in learning about the AWS resource management capabilities.
Big Data adoption success using AWS Big Data Services - Pop-up Loft TLV 2017Amazon Web Services
In today’s session we will share with you an overview of what the typical challenges when adoption Big Data are, and how the AWS Big Data platform allows you to tackle this challenges and leverage the right Analytical/Big Data solutions in order to become successful with your strategy (Whiteboard presentation)
AWS re:Invent 2016 was AWS’ largest event yet with over 32,000 attendees, 400 breakout sessions, and two keynotes of new product announcements. In this talk, we’ll explore the core themes of AWS re:Invent 2016 such as serverless and artificial intelligence. We will also drill down into several of the services and features unveiled including AWS Batch, AWS Shield, Aurora for Postgres, X-Ray, Polly, Lex, Rekognition, AWS Step Functions. Light appetizers and refreshments will be provided.
ENT314 Automate Best Practices and Operational Health for Your AWS ResourcesAmazon Web Services
"It can be challenging to optimize AWS resources across cost, performance, security and fault-tolerance, much less do it automatically. AWS Trusted Advisor is an online resource to help you do just that, by providing real time guidance to help you provision your resources following AWS best practices. In this session, we will go over how to safely automate these best practices using Amazon CloudWatch events and AWS Lambda along with samples for you to use.
AWS Personal Health Dashboard (PHD) provides alerts and remediation guidance when AWS is experiencing events that may impact your AWS environment. The AWS Health API, the underlying service powering PHD integrates with Amazon CloudWatch Events, enabling you to trigger AWS Lambda functions to define automated remediation actions. We will also introduce you to AWS Health tools, a community-based source of tools to automate remediation actions and customize Health alerts.
Come join us to see how you can implement automation of AWS best practice recommendations from Trusted Advisor and remediation from the AWS Health API on your AWS resources."
ENT201 A Tale of Two Pizzas: Accelerating Software Delivery with AWS Develope...Amazon Web Services
Software release cycles are now measured in days instead of months. Cutting edge companies are continuously delivering high-quality software at a fast pace. In this session, we will cover how you begin your DevOps journey by sharing best practices and tools by the "two pizza" engineering teams at Amazon. We will showcase how you can accelerate developer productivity by implementing continuous integration and delivery workflows. We will also cover an introduction to AWS CodeCommit, AWS CodeBuild, AWS CodePipeline, and AWS CodeDeploy, the services inspired by Amazon's internal devloper tools and DevOps practice.
Data comes in a variety of forms and in order to gain insight from this data you need to have the right platform in place. AWS has the services to cover all types of data, whether you need databases for structured data, Hadoop for unstructured data or a streaming engine for high-velocity data. In this session we will cover the various data analytics services on AWS and when to use them.
BDA307 Real-time Streaming Applications on AWS, Patterns and Use CasesAmazon Web Services
In this session, you will learn best practices for implementing simple to advanced real-time streaming data use cases on AWS. First, we’ll review decision points on near real-time versus real time scenarios. Next, we will take a look at streaming data architecture patterns that include Amazon Kinesis Analytics, Amazon Kinesis Firehose, Amazon Kinesis Streams, Spark Streaming on Amazon EMR, and other open source libraries. Finally, we will dive deep into the most common of these patterns and cover design and implementation considerations.
Convert and Migrate Your NoSQL Database or Data Warehouse to AWS - July 2017Amazon Web Services
Learning Objectives:
- Understand the use cases for migrating or replicating databases to the cloud
- Learn about the benefits of cloud-native databases for performance and costs reduction
- See how AWS Database Migration Service helps with your migration and how AWS Schema Conversion Tool makes conversions simple and quick
Moving or replicating your databases to the cloud should be simple and inexpensive. AWS has recently enhanced the AWS Database Migration Service and the AWS Schema Conversion Tool with new data sources to increase your migration options. You can now export from MongoDB databases and Greenplum, IBM Netezza, HPE Vertica, Teradata, Oracle DW and Microsoft SQL Server data warehouses to AWS. Learn how to export and migrate your data and procedural code with minimal downtime to the cloud database of your choice, including cloud-native offerings such as Amazon Aurora, Amazon DynamoDB and Amazon Redshift.
ENT316 Keeping Pace With The Cloud: Managing and Optimizing as You ScaleAmazon Web Services
With cloud maturity comes operational efficiencies and endless potential for innovation and business growth. However, the complexities of governing cloud infrastructure are impeding without the right strategy. Visibility, accountability, and actionable insights are some of the most invaluable considerations. The AWS cloud clearly enables convenience and cost savings for organizations that know how to leverage its full potential. Amazon EC2 Reserved Instances (RIs), in particular, present a tremendous opportunity when scaling to save significantly on capacity but there are many considerations to fully reaping the benefits of RIs. In this session, CloudCheckr CTO Patrick Gartlan will present issues that every organization runs into when scaling, provide best practices for how to combat them and help you show your boss how RIs help you save money and move faster.
This session is brought to you by AWS Summit Chicago sponsor, CloudCheckr.
Database and Analytics on the AWS Cloud - AWS Innovate TorontoAmazon Web Services
Antoine Genereux, AWS Solutions Architect, takes us on a tour of database solutions available for the AWS Cloud, and powerful analytics and business intelligence reporting tools.
AWS re:Invent 2016: Big Data Architectural Patterns and Best Practices on AWS...Amazon Web Services
The world is producing an ever increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Cloud computing gives you a number of advantages, such as the ability to scale your web application or website on demand. If you have a new web application and want to use cloud computing, you might be asking yourself, "Where do I start?" Join us in this session to understand best practices for scaling your resources from zero to millions of users. We show you how to best combine different AWS services, how to make smarter decisions for architecting your application, and how to scale your infrastructure in the cloud.
Big Data Architectural Patterns and Best Practices on AWSAmazon Web Services
The world is producing an ever increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
ENT302 Deep Dive on AWS Management Tools and New LaunchesAmazon Web Services
As companies shift workloads into the cloud, IT organizations are required to manage an increasing number of cloud resources. AWS provides a broad set of services that help IT organizations with provisioning, tracking, auditing, configuration management, and cost management of their AWS resources. In this session, we will explore the AWS Management Tools suite of services that support the lifecycle management of AWS resources at scale and enable IT governance and compliance. The Deep Dive on AWS Management Tools session will benefit both new and experienced IT administrators, systems administrators, and developers operating infrastructure on AWS and interested in learning about the AWS resource management capabilities.
Big Data adoption success using AWS Big Data Services - Pop-up Loft TLV 2017Amazon Web Services
In today’s session we will share with you an overview of what the typical challenges when adoption Big Data are, and how the AWS Big Data platform allows you to tackle this challenges and leverage the right Analytical/Big Data solutions in order to become successful with your strategy (Whiteboard presentation)
AWS re:Invent 2016 was AWS’ largest event yet with over 32,000 attendees, 400 breakout sessions, and two keynotes of new product announcements. In this talk, we’ll explore the core themes of AWS re:Invent 2016 such as serverless and artificial intelligence. We will also drill down into several of the services and features unveiled including AWS Batch, AWS Shield, Aurora for Postgres, X-Ray, Polly, Lex, Rekognition, AWS Step Functions. Light appetizers and refreshments will be provided.
ENT314 Automate Best Practices and Operational Health for Your AWS ResourcesAmazon Web Services
"It can be challenging to optimize AWS resources across cost, performance, security and fault-tolerance, much less do it automatically. AWS Trusted Advisor is an online resource to help you do just that, by providing real time guidance to help you provision your resources following AWS best practices. In this session, we will go over how to safely automate these best practices using Amazon CloudWatch events and AWS Lambda along with samples for you to use.
AWS Personal Health Dashboard (PHD) provides alerts and remediation guidance when AWS is experiencing events that may impact your AWS environment. The AWS Health API, the underlying service powering PHD integrates with Amazon CloudWatch Events, enabling you to trigger AWS Lambda functions to define automated remediation actions. We will also introduce you to AWS Health tools, a community-based source of tools to automate remediation actions and customize Health alerts.
Come join us to see how you can implement automation of AWS best practice recommendations from Trusted Advisor and remediation from the AWS Health API on your AWS resources."
ENT201 A Tale of Two Pizzas: Accelerating Software Delivery with AWS Develope...Amazon Web Services
Software release cycles are now measured in days instead of months. Cutting edge companies are continuously delivering high-quality software at a fast pace. In this session, we will cover how you begin your DevOps journey by sharing best practices and tools by the "two pizza" engineering teams at Amazon. We will showcase how you can accelerate developer productivity by implementing continuous integration and delivery workflows. We will also cover an introduction to AWS CodeCommit, AWS CodeBuild, AWS CodePipeline, and AWS CodeDeploy, the services inspired by Amazon's internal devloper tools and DevOps practice.
Data comes in a variety of forms and in order to gain insight from this data you need to have the right platform in place. AWS has the services to cover all types of data, whether you need databases for structured data, Hadoop for unstructured data or a streaming engine for high-velocity data. In this session we will cover the various data analytics services on AWS and when to use them.
BDA307 Real-time Streaming Applications on AWS, Patterns and Use CasesAmazon Web Services
In this session, you will learn best practices for implementing simple to advanced real-time streaming data use cases on AWS. First, we’ll review decision points on near real-time versus real time scenarios. Next, we will take a look at streaming data architecture patterns that include Amazon Kinesis Analytics, Amazon Kinesis Firehose, Amazon Kinesis Streams, Spark Streaming on Amazon EMR, and other open source libraries. Finally, we will dive deep into the most common of these patterns and cover design and implementation considerations.
Convert and Migrate Your NoSQL Database or Data Warehouse to AWS - July 2017Amazon Web Services
Learning Objectives:
- Understand the use cases for migrating or replicating databases to the cloud
- Learn about the benefits of cloud-native databases for performance and costs reduction
- See how AWS Database Migration Service helps with your migration and how AWS Schema Conversion Tool makes conversions simple and quick
Moving or replicating your databases to the cloud should be simple and inexpensive. AWS has recently enhanced the AWS Database Migration Service and the AWS Schema Conversion Tool with new data sources to increase your migration options. You can now export from MongoDB databases and Greenplum, IBM Netezza, HPE Vertica, Teradata, Oracle DW and Microsoft SQL Server data warehouses to AWS. Learn how to export and migrate your data and procedural code with minimal downtime to the cloud database of your choice, including cloud-native offerings such as Amazon Aurora, Amazon DynamoDB and Amazon Redshift.
ENT316 Keeping Pace With The Cloud: Managing and Optimizing as You ScaleAmazon Web Services
With cloud maturity comes operational efficiencies and endless potential for innovation and business growth. However, the complexities of governing cloud infrastructure are impeding without the right strategy. Visibility, accountability, and actionable insights are some of the most invaluable considerations. The AWS cloud clearly enables convenience and cost savings for organizations that know how to leverage its full potential. Amazon EC2 Reserved Instances (RIs), in particular, present a tremendous opportunity when scaling to save significantly on capacity but there are many considerations to fully reaping the benefits of RIs. In this session, CloudCheckr CTO Patrick Gartlan will present issues that every organization runs into when scaling, provide best practices for how to combat them and help you show your boss how RIs help you save money and move faster.
This session is brought to you by AWS Summit Chicago sponsor, CloudCheckr.
Database and Analytics on the AWS Cloud - AWS Innovate TorontoAmazon Web Services
Antoine Genereux, AWS Solutions Architect, takes us on a tour of database solutions available for the AWS Cloud, and powerful analytics and business intelligence reporting tools.
AWS re:Invent 2016: Big Data Architectural Patterns and Best Practices on AWS...Amazon Web Services
The world is producing an ever increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Big Data Architectural Patterns and Best Practices on AWSAmazon Web Services
by Dario Rivera, Solutions Architect, AWS
The world is producing an ever-increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
The world is producing an ever increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Presented by: Arie Leeuwesteijn, Principal Solutions Architect, Amazon Web Services
Customer Guest: Sander Kieft, Sanoma
講師: Xiaoyong Han, Solution Architect, AWS
Data collection and storage is a primary challenge for any big data architecture. In this webinar, gain a thorough understanding of AWS solutions for data collection and storage, and learn architectural best practices for applying those solutions to your projects. This session will also include a discussion of popular use cases and reference architectures. In this webinar, you will learn:
• Overview of the different types of data that customers are handling to drive high-scale workloads on AWS, and how to choose the best approach for your workload • Optimization techniques that improve performance and reduce the cost of data ingestion • Leveraging Amazon S3, Amazon DynamoDB, and Amazon Kinesis for storage and data collection
AWS November Webinar Series - Architectural Patterns & Best Practices for Big...Amazon Web Services
The world is producing an ever-increasing volume, velocity, and variety of data. For many consumers, batch analytics is no longer enough; they need sub-second analysis on fast-moving data. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how?
If you missed this popular presentation at re:Invent, attend this webinar where we simplify big data processing as a pipeline comprising various stages: ingest, store, process, analyze & visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, and durability. Finally, we provide a reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems.
Learning Objectives:
Understand key AWS Big Data services including S3, Amazon EMR, Kinesis, and Redshift
Learn architectural patterns for Big Data
Hear best practices for building Big Data applications on AWS
Didn’t make it to re:Invent? Here’s another chance to attend this popular presentation
Who Should Attend:
Architects, developers and data scientists who are looking to start a Big Data initiative
February 2016 Webinar Series - Architectural Patterns for Big Data on AWSAmazon Web Services
With an ever-increasing set of technologies to process big data, organizations often struggle to understand how to build scalable and cost-effective big data applications.
In this webinar, we will simplify big data processing as a pipeline comprising various stages; and then show you how to choose the right technology for each stage based on criteria such as data structure, design patterns, and best practices.
Learning Objectives:
Understand key AWS Big Data services including S3, Amazon EMR, Kinesis, and Redshift
Learn architectural patterns for Big Data
Hear best practices for building Big Data applications on AWS
Who Should Attend:
Architects, developers and data scientists who are looking to start a Big Data initiative
Big Data and Architectural Patterns on AWS - Pop-up Loft Tel AvivAmazon Web Services
The world is producing an ever increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
(BDT310) Big Data Architectural Patterns and Best Practices on AWSAmazon Web Services
The world is producing an ever increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Building a Data Processing Pipeline on AWS - AWS Summit SG 2017Amazon Web Services
AWS provides a broad platform of managed services to help you build, secure, and seamlessly scale end-to-end Big Data applications quickly and with ease. Want to get ramped up on how to use Amazon's big data web services? Learn when to use which service? Want to write your first big data application on AWS? Join us in this session as we discuss reference architecture, design patterns, and best practices for pulling together various AWS services to meet your big data challenges.
Antoine Genereux takes us on a detailed overview of the Database solutions available on the AWS Cloud, addressing the needs and requirements of customers at all levels. He also discusses Business Intelligence and Analytics solutions.
AWS provides a broad platform of managed services to help you build, secure, and seamlessly scale end-to-end Big Data applications quickly and with ease. Want to get ramped up on how to use Amazon's big data web services? Learn when to use which service? Want to write your first big data application on AWS? Join us in this session as we discuss reference architecture, design patterns, and best practices for pulling together various AWS services to meet your big data challenges.
BDA303 Serverless big data architectures: Design patterns and best practicesAmazon Web Services
Serverless technologies let you build and scale applications and services rapidly without the need to provision or manage servers. But how can you incorporate serverless concepts into your big data architectures?
In this session, we explore the key concepts and benefits of serverless architectures for big data, diving into design patterns to ingest, store, process, and visualize your data. Along the way, we explain when and how you can use serverless technologies to streamline data processing, minimize infrastructure management, and improve agility and robustness. We will share reference architectures using a combination of services that include AWS Lambda, Amazon Kinesis, Amazon Athena, Amazon QuickSight, and AWS Glue.
ABD201-Big Data Architectural Patterns and Best Practices on AWSAmazon Web Services
In this session, we simplify big data processing as a data bus comprising various stages: collect, store, process, analyze, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architectures, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Build Data Lakes and Analytics on AWS: Patterns & Best Practices - BDA305 - A...Amazon Web Services
In this session, we show you how to understand what data you have, how to drive insights, and how to make predictions using purpose-built AWS services. Learn about the common pitfalls of building data lakes and discover how to successfully drive analytics and insights from your data. Also learn how services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon ML services work together to build a successful data lake for various roles, including data scientists and business users.
Big Data Analytics Architectural Patterns and Best Practices (ANT201-R1) - AW...Amazon Web Services
In this session, we discuss architectural principles that helps simplify big data analytics.
We'll apply principles to various stages of big data processing: collect, store, process, analyze, and visualize. We'll disucss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on.
Finally, we provide reference architectures, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
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.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERRORTier1 app
Even though at surface level ‘java.lang.OutOfMemoryError’ appears as one single error; underlyingly there are 9 types of OutOfMemoryError. Each type of OutOfMemoryError has different causes, diagnosis approaches and solutions. This session equips you with the knowledge, tools, and techniques needed to troubleshoot and conquer OutOfMemoryError in all its forms, ensuring smoother, more efficient Java applications.
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
Enhancing Project Management Efficiency_ Leveraging AI Tools like ChatGPT.pdfJay Das
With the advent of artificial intelligence or AI tools, project management processes are undergoing a transformative shift. By using tools like ChatGPT, and Bard organizations can empower their leaders and managers to plan, execute, and monitor projects more effectively.
How Recreation Management Software Can Streamline Your Operations.pptxwottaspaceseo
Recreation management software streamlines operations by automating key tasks such as scheduling, registration, and payment processing, reducing manual workload and errors. It provides centralized management of facilities, classes, and events, ensuring efficient resource allocation and facility usage. The software offers user-friendly online portals for easy access to bookings and program information, enhancing customer experience. Real-time reporting and data analytics deliver insights into attendance and preferences, aiding in strategic decision-making. Additionally, effective communication tools keep participants and staff informed with timely updates. Overall, recreation management software enhances efficiency, improves service delivery, and boosts customer satisfaction.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
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See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
Experience our free, in-depth three-part Tendenci Platform Corporate Membership Management workshop series! In Session 1 on May 14th, 2024, we began with an Introduction and Setup, mastering the configuration of your Corporate Membership Module settings to establish membership types, applications, and more. Then, on May 16th, 2024, in Session 2, we focused on binding individual members to a Corporate Membership and Corporate Reps, teaching you how to add individual members and assign Corporate Representatives to manage dues, renewals, and associated members. Finally, on May 28th, 2024, in Session 3, we covered questions and concerns, addressing any queries or issues you may have.
For more Tendenci AMS events, check out www.tendenci.com/events
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
2. Agenda
Big Data Challenges
Architecture principles
What technologies should you use? How? Why?
Reference architecture
Design patterns
Customer Story: The Move to real-time data architectures,
DNA Oy
6. Architectural Principles
Build decoupled systems
• Data → Store → Process → Store → Analyze → Answers
Use the right tool for the job
• Data structure, latency, throughput, access patterns
Leverage AWS managed services
• Scalable/elastic, available, reliable, secure, no/low admin
Use log-centric design patterns
• Immutable logs, materialized views
Be cost-conscious
• Big data ≠ big cost
7. Simplify Big Data Processing
COLLECT STORE PROCESS/
ANALYZE
CONSUME
Time to answer (Latency)
Throughput
Cost
8. Types of DataCOLLECT
Mobile apps
Web apps
Data centers
AWS Direct
Connect
RECORDS
Applications
In-memory data structures
Database records
AWS Import/Export
Snowball
Logging
Amazon
CloudWatch
AWS
CloudTrail
DOCUMENTS
FILES
LoggingTransport
Search documents
Log files
Messaging
Message MESSAGES
Messaging
Messages
Devices
Sensors &
IoT platforms
AWS IoT STREAMS
IoT
Data streams
Transactions
Files
Events
10. Hot Warm Cold
Volume MB–GB GB–TB PB–EB
Item size B–KB KB–MB KB–TB
Latency ms ms, sec min, hrs
Durability Low–high High Very high
Request rate Very high High Low
Cost/GB $$-$ $-¢¢ ¢
Hot data Warm data Cold data
Data Characteristics: Hot, Warm, Cold
20. Best Practice: Use the Right Tool for the Job
Data Tier
Search
Amazon Elasticsearch
Service
In-memory
Amazon ElastiCache
Redis
Memcached
SQL
Amazon Aurora
Amazon RDS
MySQL
PostgreSQL
Oracle
SQL Server
NoSQL
Amazon DynamoDB
Cassandra
HBase
MongoDB
22. COLLECT STORE
Mobile apps
Web apps
Data centers
AWS Direct
Connect
RECORDS
AWS Import/Export
Snowball
Logging
Amazon
CloudWatch
AWS
CloudTrail
DOCUMENTS
FILES
Messaging
Message MESSAGES
Devices
Sensors &
IoT platforms
AWS IoT STREAMS
Apache Kafka
Amazon Kinesis
Streams
Amazon Kinesis
Firehose
Amazon DynamoDB
Streams
Hot
Stream
Amazon SQS
Message
Amazon Elasticsearch
Service
Amazon DynamoDB
Amazon S3
Amazon ElastiCache
Amazon RDS
SearchSQLNoSQLCacheFile
LoggingIoTApplicationsTransportMessaging
Amazon ElastiCache
• Managed Memcached or Redis service
Amazon DynamoDB
• Managed NoSQL database service
Amazon RDS
• Managed relational database service
Amazon Elasticsearch Service
• Managed Elasticsearch service
23. Which Data Store Should I Use?
Data structure → Fixed schema, JSON, key-value
Access patterns → Store data in the format you will access it
Data characteristics → Hot, warm, cold
Cost → Right cost
25. Cost-Conscious Design
Example: Should I use Amazon S3 or Amazon DynamoDB?
“I’m currently scoping out a project. The design calls for
many small files, perhaps up to a billion during peak. The
total size would be on the order of 1.5 TB per month…”
Request rate
(Writes/sec)
Object size
(Bytes)
Total size
(GB/month)
Objects per month
300 2048 1483 777,600,000
29. Batch
Takes minutes to hours
Example: Daily/weekly/monthly reports
Amazon EMR (MapReduce, Hive, Pig, Spark)
Interactive
Takes seconds
Example: Self-service dashboards
Amazon Redshift, Amazon Athena, Amazon EMR (Presto, Spark)
Message
Takes milliseconds to seconds
Example: Message processing
Amazon SQS applications on Amazon EC2
Stream
Takes milliseconds to seconds
Example: Fraud alerts, 1 minute metrics
Amazon EMR (Spark Streaming), Amazon Kinesis Analytics, KCL,
Storm, AWS Lambda
Artificial Intelligence
Takes milliseconds to minutes
Example: Fraud detection, forecast demand, text to speech
Amazon AI (Lex, Polly, ML, Rekognition), Amazon EMR (Spark
ML), Deep Learning AMI (MXNet, TensorFlow, Theano, Torch, CNTK and Caffe)
Analytics Types & Frameworks PROCESS / ANALYZE
Message
Amazon SQS apps
Amazon EC2
Streaming
Amazon Kinesis
Analytics
KCL
apps
AWS Lambda
Stream
Amazon EC2
Amazon EMR
Fast
Amazon Redshift
Presto
Amazon
EMR
FastSlow
Amazon Athena
BatchInteractive
Amazon
AI
AI
30. What About ETL?
https://aws.amazon.com/big-data/partner-solutions/
ETLSTORE PROCESS / ANALYZE
Data Integration Partners
Reduce the effort to move, cleanse, synchronize,
manage, and automatize data related processes. AWS Glue
AWS Glue is a fully managed ETL service that makes
it easy to understand your data sources, prepare the
data, and move it reliably between data stores
New
33. STORE CONSUMEPROCESS / ANALYZE
Amazon QuickSight
Apps & Services
Analysis&visualization
Notebooks
IDEAPI
Applications & API
Analysis and visualization
Notebooks
IDE
Business
users
Data scientist,
developers
COLLECT ETL
41. The Move to a real-time data
architecture
Jarno Kartela
Chief Data Scientist, DNA Oy
42. EUR 859 million
Net sales in 2016
EUR 91 million
Operating result in 2016
TV
Finland’s largest cable operator and the
leading pay TV provider
Mobile communications and fixed
network customer subscriptions
3.8 millionOUR VALUES
FAST
DNA's customers receive quick
and helpful service
STRAIGHTFORWARD
DNA’s approach is clear and
responsible
BOLD
We are direct, open-minded
and ready for change
The personnel's satisfaction with DNA
as an employer is at a record-breaking
high level
Strong employee satisfaction
DNA is one of the leading Finnish telecommunications groups
42
1,668
At the end of 2016, there were
1,668 employees working with DNA
Finland’s most extensive retailer of
mobile phones, other mobile devices and
mobile subscriptions
64 DNA stores
Customer
is in the center of DNA’s strategy
Public | DNA Today
68. Benefits
Customer
- unified experience
across channels
- personalized content
- better offers
- less time spent on
browsing DNA
services
- overall better CX
- coming: my data &
ability to change the
data about you as a
customer
Business
- time spent on
marketing 10x less
than before
- sales up 3x across AI-
driven campaigns
- sales up 2x-10x
across campaigns
triggered by real-time
data
- near real time (5 min)
insight on what’s
happening
- insight across
channels
IT / Analytics
- better service quality
with less time &
resources
- devops, automation
- endless scale for
analytics & data
- ease of try-fail-try-
again
- cost effectiveness
- one source of truth for
data
- ability to serve all
channels & functions
with one platform
- 6 months to full scale
production
69. Coming up:
Rekognition for retail analytics,
Lex/similar for voice dialogue
and chatbots,
GDPR -> create services
instead of fear
71. Summary
Build decoupled systems
• Use Amazon S3 as the data fabric of your data lake
• Data → Store → Process → Store → Analyze → Answers
Use the right tool for the job
• Data structure, latency, throughput, access patterns
Leverage AWS managed services
• Scalable/elastic, available, reliable, secure, no/low admin
Use log-centric design patterns
• Immutable log, batch, interactive & real-time views
Be cost-conscious
• Big data ≠ big cost
72. Building a Data Lake on AWS
Kinesis Firehose
Athena
Query Service
73. Resources
• https://aws.amazon.com/blogs/big-data/introducing-the-data-
lake-solution-on-aws/
• AWS re:Invent 2016: Netflix: Using Amazon S3 as the fabric of
our big data ecosystem (BDM306)
• AWS re:Invent 2016: Deep Dive on Amazon S3 (STG303)
• https://aws.amazon.com/blogs/big-data/reinvent-2016-aws-big-
data-machine-learning-sessions/
• https://aws.amazon.com/blogs/big-data/implementing-
authorization-and-auditing-using-apache-ranger-on-amazon-emr/