Connecting Your Data Analytics Pipeline
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This document discusses AWS Glue, a fully managed extract, transform, and load (ETL) service that makes it easy to move data between data sources like databases and Amazon S3. It automates the time-consuming tasks of data preparation and transformation so customers can focus on analyzing their data and gaining insights. The document outlines the key components of AWS Glue including the data catalog, job execution, and job authoring capabilities and how it addresses the challenges of hand-coding and maintaining ETL jobs over time. It also provides an overview of how to populate the data catalog using crawlers, author jobs that are run in a serverless manner, and monitor job executions.
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Casi reali di Mass Migration nel Cloud: benefici tangibili ed intangibili
Casi reali di Mass Migration nel Cloud: benefici tangibili ed intangibili
Monitoring, Hold the Infrastructure - Getting the Most out of AWS Lambda - AW...
Monitoring, Hold the Infrastructure - Getting the Most out of AWS Lambda - AW...
From On-Premises to Cloud: Modernize Data Protection with Druva Phoenix and AWS
From On-Premises to Cloud: Modernize Data Protection with Druva Phoenix and AWS
Real-time Analytics using Data from IoT Devices - AWS Online Tech Talks
Real-time Analytics using Data from IoT Devices - AWS Online Tech Talks
BDA306 Building a Modern Data Warehouse: Deep Dive on Amazon Redshift
BDA306 Building a Modern Data Warehouse: Deep Dive on Amazon Redshift
Replicate and Manage Data Using Managed Databases and Serverless Technologies
Replicate and Manage Data Using Managed Databases and Serverless Technologies
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管理程式對AWS LAMBDA持續交付
管理程式部署到多個AWS Lambda函數和更新您的API閘道，常可能需要手動管理，也使其顯得費時。
在 這個議程，我們將向您介紹如何使用AWS CodePipeline ，一個架構於Amazon內部的自動化持續交付，去部署pipeline到AWS Lambda 。我們將討論如何使用版本控制，使您能夠更好地管理你的開發工作流程（例如，開發，分期和生產）、Lambda函數和API閘道方法的不同變化。我們將演 練如何自動從您的開發應用程序的整個發行過程中，分期，最後到生產;在每個階段執行自動整合測試。
初探AWS 平台上的 NoSQL 雲端資料庫服務
本場專題演講將從介紹非關連式 (NoSQL) 資料庫開始，並將與關連式 (SQL) 資料庫進行比較。另外也將詳細探索 DynamoDB 的功能及效益，並探討如何充分發揮 DynamoDB 資料庫的潛力，以及介紹如何設計高效率索引、掃描及查詢作業，以及深入了解多項新近發布的功能，如 JSON 文件支援、DynamoDB Streams 及其他。本專題演講結束後，您將學到 DynamoDB 的基礎知識，以及本款高效能 NoSQL 資料庫的常見使用案例及效益。
可靠分布式系统基础 Paxos的直观解释
本文解释了paxos如何在分布式系统中解决一致性问题。
本文通过若干实例展示了主从复制，quorum-rw和paxos之间的联系和差异，以及如何从最简单的复制算法推导出强一致性的paxos算法。
首先从主从-复制开始，然后通过增加一致性要求的限制，将主从-复制进化为quorum-rw。
再对quorum-rw增加原子性的要求，将2组quorum-rw组装成paxos的算法。
更多好货: https://blog.openacid.com/
Amazon Athena Capabilities and Use Cases Overview
Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. With Athena, users can analyze large-scale datasets and get results in seconds without having to load the data into databases or maintain any infrastructure. Athena supports querying data in formats like CSV, JSON, and columnar formats like Apache Parquet and ORC. Users pay only for the queries that they run.
Tackle Your Dark Data Challenge with AWS Glue - AWS Online Tech Talks
Learning Objectives:
- Discover dark data that you are currently not analyzing.
- Analyze dark data without moving it into your data warehouse.
- Visualize the results of your dark data analytics.
Introduction to AWS Glue
In this session, we introduce AWS Glue, provide an overview of its components, and share how you can use AWS Glue to automate discovering your data, cataloging it, and preparing it for analysis.
大數據運算媒體業案例分享 (Big Data Compute Case Sharing for Media Industry)
This document discusses big data and analytics on AWS. It defines big data as large, diverse, and growing volumes of data that are difficult to capture, curate, manage and process with traditional database systems. It notes that the majority of data is now unstructured and that data volumes are growing exponentially. The document outlines the AWS big data platform, which supports batch processing, real-time analytics and machine learning. It provides recommendations on which AWS data stores and analytics services to use depending on data type, access patterns, volume and other attributes.
GLOA:A New Job Scheduling Algorithm for Grid Computing
The paper review presentation of 'GLOA:A New Job Scheduling Algorithm for Grid Computing' published in International Journal of Artificial Intelligence and Interactive Multimedia, Vol. 2, Nº 1.
電子商務資料分析 上課投影片
包含：
web log processing
clickstream data
recommender systems
personalized recommendation
(Slides) Task scheduling algorithm for multicore processor system for minimiz...
Shohei Gotoda, Naoki Shibata and Minoru Ito : "Task scheduling algorithm for multicore processor system for minimizing recovery time in case of single node fault," Proceedings of IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2012), pp.260-267, DOI:10.1109/CCGrid.2012.23, May 15, 2012.
In this paper, we propose a task scheduling al-gorithm for a multicore processor system which reduces the
recovery time in case of a single fail-stop failure of a multicore
processor. Many of the recently developed processors have
multiple cores on a single die, so that one failure of a computing
node results in failure of many processors. In the case of a failure
of a multicore processor, all tasks which have been executed
on the failed multicore processor have to be recovered at once.
The proposed algorithm is based on an existing checkpointing
technique, and we assume that the state is saved when nodes
send results to the next node. If a series of computations that
depends on former results is executed on a single die, we need
to execute all parts of the series of computations again in
the case of failure of the processor. The proposed scheduling
algorithm tries not to concentrate tasks to processors on a die.
We designed our algorithm as a parallel algorithm that achieves
O(n) speedup where n is the number of processors. We evaluated
our method using simulations and experiments with four PCs.
We compared our method with existing scheduling method, and
in the simulation, the execution time including recovery time in
the case of a node failure is reduced by up to 50% while the
overhead in the case of no failure was a few percent in typical
scenarios.
BDA303 Serverless big data architectures: Design patterns and best practices
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.
The Power of Big Data - AWS Summit Bahrain 2017
Join us for an in-depth look at the current state of big data at AWS. Learn about the latest big data trends and industry use cases. Hear how other organizations are using the AWS big data platform to innovate and remain competitive. Take a look at some of the most recent AWS big data developments.
淺談系統監控與 AWS CloudWatch 的應用
2017/06/21, AWS User Group Taiwan
https://rickhw.github.io/2017/06/21/AWS/Stategies-System-Monitor_and_CloudWatch/
Introduction to YARN and MapReduce 2
As part of the recent release of Hadoop 2 by the Apache Software Foundation, YARN and MapReduce 2 deliver significant upgrades to scheduling, resource management, and execution in Hadoop.
At their core, YARN and MapReduce 2’s improvements separate cluster resource management capabilities from MapReduce-specific logic. YARN enables Hadoop to share resources dynamically between multiple parallel processing frameworks such as Cloudera Impala, allows more sensible and finer-grained resource configuration for better cluster utilization, and scales Hadoop to accommodate more and larger jobs.
如何利用 Amazon EMR 及Athena 打造高成本效益的大數據環境
曾在 Apache Hadoop、Spark 及資料倉儲設備上處理分析、資料處理和資料科學工作負載等作業的客戶，現在正從內部部署的作業方式遷移至 Amazon EMR，以節省成本、增加可用性以及改進效能。Amazon EMR 是一種受管服務，可讓您運用超過 15 種 Apache Hadoop 及 Spark 生態系統的最新版開放原始碼框架，以處理及分析極大量資料集。本專題演講的重點將放在辨別您現有環境中的元件及工作流程，以及提供將這類工作負載遷移至 Amazon EMR 的最佳實務。我們將講解如何從 HDFS 移至可做為長效耐用儲存層使用的 Amazon S3，以及如何運用Amazon EC2 競價型執行個體及 Auto Scaling（自動擴充）來降低成本。此外，我們也將示範如何運用無伺服器服務 Athena 在 Amazon S3 內部分析資料，無需管理任何基礎架構，再更進一步降低成本。最後將說明多項常見微調訣竅來加快生產速度。
The Impala Cookbook
The document outlines topics covered in "The Impala Cookbook" published by Cloudera. It discusses physical and schema design best practices for Impala, including recommendations for data types, partition design, file formats, and block size. It also covers estimating and managing Impala's memory usage, and how to identify the cause when queries exceed memory limits.
Hadoop, the Apple of Our Eyes (這些年，我們一起追的 Hadoop)
兩年前我們為大家簡單介紹過 Hadoop。兩年之後，Hadoop 不只本身有著蠻大的改變，整個 Hadoop Ecosystem也不斷開枝散葉，衍生出許多新的子計畫，與現有的 IT 系統整合的更為密切，同時也激發出大家的創意，透過其他不同的想法與作法，共同面對 Big Data 這個議題。演講內容涵蓋：
0. 前情提要 - Hadoop 簡介
1. 進擊的 Hadoop - Hadoop 2.x 的改進
2. Hadoop 家族 - Hadoop 家庭裡的大咖
3. Hadoop 戰隊 - 出名的 Hadoop Distribution
4. Hadoop 富二代 - 新一代的 Hadoop 相關技術
5. Hadoop 小圈圈 - Hadoop 的派系之爭
6. 喝咖啡騎大象的行前建議
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Tackle Your Dark Data Challenge with AWS Glue - AWS Online Tech Talks
Tackle Your Dark Data Challenge with AWS Glue - AWS Online Tech Talks
大數據運算媒體業案例分享 (Big Data Compute Case Sharing for Media Industry)
大數據運算媒體業案例分享 (Big Data Compute Case Sharing for Media Industry)
GLOA:A New Job Scheduling Algorithm for Grid Computing
GLOA:A New Job Scheduling Algorithm for Grid Computing
(Slides) Task scheduling algorithm for multicore processor system for minimiz...
(Slides) Task scheduling algorithm for multicore processor system for minimiz...
BDA303 Serverless big data architectures: Design patterns and best practices
BDA303 Serverless big data architectures: Design patterns and best practices
Similar to Connecting Your Data Analytics Pipeline
From Data Collection to Actionable Insights in 60 Seconds: AWS Developer Work...
From Data Collection to Actionable Insights in 60 Seconds: AWS Developer Workshop - Web Summit 2018
Columnar data formats such as Parquet and ORC are designed to optimize both query performance and costs for analytics scenarios. On the other hand, serverless computing platforms such as AWS Lambda allow you to run highly scalable applications without provisioning or managing servers. The combination of columnar storage and serverless computing can drastically simplify many of the pain points related to big data analytics, data collection, data exploration, and ETL orchestration, while at the same time reducing the total cost of ownership.
Speaker: Alex Casalboni - Technical Evangelist, AWS
BDA305 Building Data Lakes and Analytics on AWS
Over 90% of today’s data was generated in the last 2 years, and the rate of data growth isn’t slowing down. In this session, we’ll step through the challenges and best practices on how to capture all the data that is being generated, understand what data you have, and start driving insights and even predict the future using purpose built AWS Services. We’ll frame the session and demonstrations around common pitfalls of building Data Lakes and how to successful drive analytics and insights from the data. This session will focus on the architecture patterns bringing together key AWS Services and rather than a deep dive on any single service. We’ll show how services such as Amazon S3, Amazon Glue, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, and Amazon Kinesis, and Amazon Machine Learning services are put together to build a successful data lake for various role including both data scientists and business users.
Implementing a Data Lake
This document discusses implementing a data lake on AWS to securely store, categorize, and analyze all types of data in a centralized repository. It describes key attributes of a data lake like decoupled storage and compute, rapid ingestion and transformation, and schema on read. It then outlines various AWS services that can be used to build a data lake like S3, Athena, EMR, Redshift, Glue, and Kinesis. It provides examples of streaming IoT data into a data lake and running queries and analytics on the data.
AWS Data Lake: data analysis @ scale
AWS Data Lake: data analysis @ scale
AWS Webinar Series 2018
Giorgio Nobile, AWS Solutions Architect
Build Data Lakes & Analytics on AWS: Patterns & Best Practices - BDA305 - Ana...
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.
Adding Search to Amazon DynamoDB
by Darin Briskman, Technical Evangelist, AWS
DynamoDB queries enable consistent low latency at any workload, using the partition key, sort key, local secondary indexes, and global secondary indexes. Amazon Elasticsearch Service enables flexible search, including ranking and aggregation. Adding Elasticsearch to DynamoDB opens new capabilities to combine the power of query and search. Learn how Amazon.com uses this combination and how you can use it, too. Level: 200
Owning Your Own (Data) Lake House
Data Con LA 2020
Description
In this session, I introduce the Amazon Redshift lake house architecture which enables you to query data across your data warehouse, data lake, and operational databases to gain faster and deeper insights. With a lake house architecture, you can store data in open file formats in your Amazon S3 data lake.
Speaker
Antje Barth, Amazon Web Services, Sr. Developer Advocate, AI and Machine Learning
Adding Search to Amazon DynamoDB
DynamoDB queries enable consistent low latency at any workload, using the partition key, sort key, local secondary indexes, and global secondary indexes. Amazon Elasticsearch Service enables flexible search, including ranking and aggregation. Adding Elasticsearch to DynamoDB opens new capabilities to combine the power of query and search. Learn how Amazon.com uses this combination and how you can use it, too
Innovation Track AWS Cloud Experience Argentina - Data Lakes & Analytics en AWS
Innovation Track AWS Cloud Experience Argentina - Data Lakes & Analytics en AWS Amazon Web Services LATAM
Data lakes allow organizations to store all types of data in a centralized repository at scale. AWS Lake Formation makes it easy to build secure data lakes by automatically registering and cleaning data, enforcing access permissions, and enabling analytics. Data stored in data lakes can be analyzed using services like Amazon Athena, Redshift, and EMR depending on the type of analysis and latency required.Building Data Lakes and Analytics on AWS; Patterns and Best Practices - BDA30...
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.
Introduction to AWS Glue: Data Analytics Week at the SF Loft
Introduction to AWS Glue: Data Analytics Week at the San Francisco Loft
AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. You can create and run an ETL job with a few clicks in the AWS Management Console. You simply point AWS Glue to your data stored on AWS, and AWS Glue discovers your data and stores the associated metadata (e.g. table definition and schema) in the AWS Glue Data Catalog. Once cataloged, your data is immediately searchable, queryable, and available for ETL. AWS Glue generates the code to execute your data transformations and data loading processes.
Level: Intermediate
Speakers:
John Mallory - Principal Business Development Manager, Storage, AWS
Asim Kumar Sasmal - Big Data Consultant, AWS Professional Services
Big Data Analytics from Azure Cloud to Power BI Mobile
This document discusses using Azure services for big data analytics and data insights. It provides an overview of Azure services like Azure Batch, Azure Data Lake, Azure HDInsight and Power BI. It then describes a demo solution that uses these Azure services to analyze job posting data, including collecting data using a .NET application, storing in Azure Data Lake Store, processing with Azure Data Lake Analytics and Azure HDInsight, and visualizing results in Power BI. The presentation includes architecture diagrams and discusses implementation details.
Success has Many Query Engines- Tel Aviv Summit 2018
This document discusses different AWS services for data analytics and querying large datasets. It provides an overview of AWS Glue for ETL, Amazon Athena for interactive SQL queries on S3 data, and Amazon Redshift Spectrum for extending Amazon Redshift queries to data in S3. It then discusses a customer case study of NUVIAD who moved from a traditional data warehouse to using different AWS analytics services on a single S3 data lake.
Building Data Warehouses and Data Lakes in the Cloud - DevDay Austin 2017 Day 2
This document discusses building data warehouses and data lakes in the cloud using AWS services. It provides an overview of AWS databases, analytics, and machine learning services that can be used to store and analyze data at scale. These services allow customers to migrate existing data warehouses to the cloud, build new data warehouses and data lakes more cost effectively, and gain insights from their data more easily.
Semplificare l'analisi dei dati con architetture "Serverless": architetture e...
This document discusses serverless big data architectures using AWS services. It describes how serverless computing eliminates the need to provision and manage servers. AWS services like S3, DynamoDB, Kinesis, Lambda, Glue and Athena allow ingesting, storing, processing and analyzing data without managing infrastructure. These services scale automatically based on usage and provide built-in availability and fault tolerance.
Understanding AWS Managed Database and Analytics Services | AWS Public Sector...
The world is creating more data in more ways than ever before. The average internet user in 2017 generates 1.5GB of data per day, with the rate doubling every 18 months. A single autonomous vehicle can generate 4TB per day. Each smart manufacturing plant generates 1PB per day. Storing, managing, and analyzing this data requires integrated database and analytic services that provide reliability and security at scale. AWS offers a range of managed data services that let customers focus on making data useful, including Amazon Aurora, RDS, DynamoDB, Redshift, Spectrum, ElastiCache, Kinesis, EMR, Elasticsearch Service, and Glue. In this session, we discuss these services, share our vision for innovation, and show how our customers use these services today. Learn More: https://aws.amazon.com/government-education/
Understanding AWS Managed Database and Analytics Services | AWS Public Sector...
The world is creating more data in more ways than ever before. The average internet user in 2017 generates 1.5GB of data per day, with the rate doubling every 18 months. A single autonomous vehicle can generate 4TB per day. Each smart manufacturing plant generates 1PB per day. Storing, managing, and analyzing this data requires integrated database and analytic services that provide reliability and security at scale. AWS offers a range of managed data services that let customers focus on making data useful, including Amazon Aurora, RDS, DynamoDB, Redshift, Spectrum, ElastiCache, Kinesis, EMR, Elasticsearch Service, and Glue. In this session, we discuss these services, share our vision for innovation, and show how our customers use these services today. Learn More: https://aws.amazon.com/government-education/
Building Data Lakes and Analytics on AWS
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.
BDA311 Introduction to AWS Glue
AWS Glue is a fully managed, serverless extract, transform, and load (ETL) service that makes it easy to move data between data stores. AWS Glue simplifies and automates the difficult and time consuming tasks of data discovery, conversion mapping, and job scheduling so you can focus more of your time querying and analyzing your data using Amazon Redshift Spectrum and Amazon Athena. In this session, we introduce AWS Glue, provide an overview of its components, and share how you can use AWS Glue to automate discovering your data, cataloging it, and preparing it for analysis.
Introduction to AWS Glue
AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. You can create and run an ETL job with a few clicks in the AWS Management Console. You simply point AWS Glue to your data stored on AWS, and AWS Glue discovers your data and stores the associated metadata (e.g. table definition and schema) in the AWS Glue Data Catalog. Once cataloged, your data is immediately searchable, queryable, and available for ETL. AWS Glue generates the code to execute your data transformations and data loading processes.
Level: Intermediate
Speakers:
Ryan Malecky - Solutions Architect, EdTech, AWS
Rajakumar Sampathkumar - Sr. Technical Account Manager, AWS
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Success has Many Query Engines- Tel Aviv Summit 2018
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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.
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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.
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Event Agenda :
Open banking so far (short recap)
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• Scope
• Features
• Tech overview and Demo
The role of the Cloud
The Future of APIs
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• Monetizing data / APIs
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OpsWorks Configuration Management: automatizza la gestione e i deployment del...
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 Workloads
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.
Computer Vision con AWS
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.
Database Oracle e VMware Cloud on AWS i miti da sfatare
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 NodeJS
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.
API moderne real-time per applicazioni mobili e web
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 sfatare
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.
Tools for building your MVP on AWS
1) The document discusses building a minimum viable product (MVP) using Amazon Web Services (AWS).
2) It provides an example of an MVP for an omni-channel messenger platform that was built from 2017 to connect ecommerce stores to customers via web chat, Facebook Messenger, WhatsApp, and other channels.
3) The founder discusses how they started with an MVP in 2017 with 200 ecommerce stores in Hong Kong and Taiwan, and have since expanded to over 5000 clients across Southeast Asia using AWS for scaling.
How to Build a Winning Pitch Deck
This document discusses pitch decks and fundraising materials. It explains that venture capitalists will typically spend only 3 minutes and 44 seconds reviewing a pitch deck. Therefore, the deck needs to tell a compelling story to grab their attention. It also provides tips on tailoring different types of decks for different purposes, such as creating a concise 1-2 page teaser, a presentation deck for pitching in-person, and a more detailed read-only or fundraising deck. The document stresses the importance of including key information like the problem, solution, product, traction, market size, plans, team, and ask.
Building a web application without servers
This document discusses building serverless web applications using AWS services like API Gateway, Lambda, DynamoDB, S3 and Amplify. It provides an overview of each service and how they can work together to create a scalable, secure and cost-effective serverless application stack without having to manage servers or infrastructure. Key services covered include API Gateway for hosting APIs, Lambda for backend logic, DynamoDB for database needs, S3 for static content, and Amplify for frontend hosting and continuous deployment.
Fundraising Essentials
This document provides tips for fundraising from startup founders Roland Yau and Sze Lok Chan. It discusses generating competition to create urgency for investors, fundraising in parallel rather than sequentially, having a clear fundraising narrative focused on what you do and why it's compelling, and prioritizing relationships with people over firms. It also notes how the pandemic has changed fundraising, with examples of deals done virtually during this time. The tips emphasize being fully prepared before fundraising and cultivating connections with investors in advance.
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...
This document discusses Amazon's machine learning services for building conversational interfaces and extracting insights from unstructured text and audio. It describes Amazon Lex for creating chatbots, Amazon Comprehend for natural language processing tasks like entity extraction and sentiment analysis, and how they can be used together for applications like intelligent call centers and content analysis. Pre-trained APIs simplify adding machine learning to apps without requiring ML expertise.
Introduzione a Amazon Elastic Container Service
Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.
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Connecting Your Data Analytics Pipeline
- 1. © 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Cassandra Bonner Solutions Architect, Amazon Web Services Level 200 Connecting your Data Analytics Pipeline
- 2. Ingest Data Analytics Landscape Serving Amazon Redshift Data analysts Data scientists Business users Engagement platforms Automation / events Speed (Near real-time) Scale (Batch) AWS Glue Sources Flat Files Amazon S3 Amazon RDS On Prem DB
- 3. We Have Lots of ETL Partners Amazon Redshift Partner Page for Data Integration
- 4. Why Would Anyone Hand-code? Brittle Error-prone Laborious
- 5. You Also Need to Maintain this Code ► As data formats change ► As target schemas change ► As you add sources ► As data volume grows
- 6. Code is Flexible Code is Powerful You can unit test You can deploy with other code You know your dev tools
- 7. ETL is the Most Time-consuming Part of Analytics ETL Data Warehousing Business Intelligence Large % of time spent here Amazon Redshift Amazon QuickSight
- 8. The Data Gap This Leads to Dark Data 1990 2000 2010 2020 Generated Data Data Volume Gartner: User Survey Analysis: Key Trends Shaping the Future of Data Centre Infrastructure Through 2011 IDC: Worldwide Business Analytics Software 2012–2016 Forecast and 2011 Vendor Shares
- 9. Glue Automates The Undifferentiated Heavy-lifting Of ETL
- 10. AWS Glue: High-Level View
- 11. AWS Glue: Components Data Catalog Hive metastore compatible metadata repository of data sources. Crawls data source to infer table, data type, partition format. Job Execution Runs jobs in Spark containers – automatic scaling based on SLA. Glue is serverless - only pay for the resources you consume. Job Authoring Generates Python code to move data from source to destination. Edit with your favourite IDE; share code snippets using Git.
- 12. Glue Data Catalog Discover and organise your data sets
- 13. Glue Data Catalog We added a few extensions: Search metadata Connection info Classification Versioning
- 14. Crawlers: Populate Data Catalog Crawler Custom Classifiers Built—in Classifiers Amazon S3 Amazon RDS Amazon Redshift JDBC Data Stores 3 1 2 Connection 4 5
- 15. POPULATE DATA CATALOG DEMO
- 16. Job Authoring in Glue Make ETL job authoring like code development using your own tools
- 17. Authoring Jobs 1 Source 2 Target 3 4 Transform Data 6 Generates 5 Triggers PySpark script
- 18. Automatic Code Generation Domain Driven Design, Eric Evans
- 19. Orchestration & Resource Management Fully managed, serverless job execution
- 20. Running & Monitoring Jobs JDBC Amazon S3 Amazon RDS Amazon Redshift Data Sources JDBC Amazon S3 Amazon RDS Amazon Redshift Data Targets Triggers 1 2 Extracts data Job 4 Loads data 5 3 Transforms Runs
- 21. AUTHORING & RUNNING JOBS DEMO
- 22. Sign Up For Glue Preview You can sign up for a preview at aws.amazon.com/glue.
- 23. Thank you!