The document discusses building a data lake using Amazon S3 and Amazon Glacier for storage. It covers topics like what is big data, what is a data lake, achievable business outcomes from a data lake, securing the data lake, and examples of what can be done with analytics services on AWS. The presentation provides examples of using services like Amazon Comprehend, Amazon Transcribe, Kinesis, Athena and QuickSight for natural language processing, audio analysis, real-time streaming and visualization.
Retail Analytics and BI with Looker, BigQuery, GCP & Leigha JarettDaniel Zivkovic
Leigha Jarett of GCP explains how to bring Cloud "superpowers" to your Data and modernize your Business Intelligence with Looker, BigQuery and Google Cloud services on an example of Cymbal Direct - one of Google Cloud's demo brands. The meetup recording with TOC for easy navigation is at https://youtu.be/BpzJU_S40ic.
P.S. For more interactive lectures like this, go to http://youtube.serverlesstoronto.org/ or sign up for our upcoming live events at https://www.meetup.com/Serverless-Toronto/events/
Embarking on building a modern data warehouse in the cloud can be an overwhelming experience due to the sheer number of products that can be used, especially when the use cases for many products overlap others. In this talk I will cover the use cases of many of the Microsoft products that you can use when building a modern data warehouse, broken down into four areas: ingest, store, prep, and model & serve. It’s a complicated story that I will try to simplify, giving blunt opinions of when to use what products and the pros/cons of each.
The new Microsoft Azure SQL Data Warehouse (SQL DW) is an elastic data warehouse-as-a-service and is a Massively Parallel Processing (MPP) solution for "big data" with true enterprise class features. The SQL DW service is built for data warehouse workloads from a few hundred gigabytes to petabytes of data with truly unique features like disaggregated compute and storage allowing for customers to be able to utilize the service to match their needs. In this presentation, we take an in-depth look at implementing a SQL DW, elastic scale (grow, shrink, and pause), and hybrid data clouds with Hadoop integration via Polybase allowing for a true SQL experience across structured and unstructured data.
Big Data and Data Warehousing Together with Azure Synapse Analytics (SQLBits ...Michael Rys
SQLBits 2020 presentation on how you can build solutions based on the modern data warehouse pattern with Azure Synapse Spark and SQL including demos of Azure Synapse.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Retail Analytics and BI with Looker, BigQuery, GCP & Leigha JarettDaniel Zivkovic
Leigha Jarett of GCP explains how to bring Cloud "superpowers" to your Data and modernize your Business Intelligence with Looker, BigQuery and Google Cloud services on an example of Cymbal Direct - one of Google Cloud's demo brands. The meetup recording with TOC for easy navigation is at https://youtu.be/BpzJU_S40ic.
P.S. For more interactive lectures like this, go to http://youtube.serverlesstoronto.org/ or sign up for our upcoming live events at https://www.meetup.com/Serverless-Toronto/events/
Embarking on building a modern data warehouse in the cloud can be an overwhelming experience due to the sheer number of products that can be used, especially when the use cases for many products overlap others. In this talk I will cover the use cases of many of the Microsoft products that you can use when building a modern data warehouse, broken down into four areas: ingest, store, prep, and model & serve. It’s a complicated story that I will try to simplify, giving blunt opinions of when to use what products and the pros/cons of each.
The new Microsoft Azure SQL Data Warehouse (SQL DW) is an elastic data warehouse-as-a-service and is a Massively Parallel Processing (MPP) solution for "big data" with true enterprise class features. The SQL DW service is built for data warehouse workloads from a few hundred gigabytes to petabytes of data with truly unique features like disaggregated compute and storage allowing for customers to be able to utilize the service to match their needs. In this presentation, we take an in-depth look at implementing a SQL DW, elastic scale (grow, shrink, and pause), and hybrid data clouds with Hadoop integration via Polybase allowing for a true SQL experience across structured and unstructured data.
Big Data and Data Warehousing Together with Azure Synapse Analytics (SQLBits ...Michael Rys
SQLBits 2020 presentation on how you can build solutions based on the modern data warehouse pattern with Azure Synapse Spark and SQL including demos of Azure Synapse.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Amazon Elastic MapReduce is one of the largest Hadoop operators in the world. Since its launch five years ago, AWS customers have launched more than 5.5 million Hadoop clusters.
In this talk, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters and other Amazon EMR architectural patterns. We talk about how to scale your cluster up or down dynamically and introduce you to ways you can fine-tune your cluster. We also share best practices to keep your Amazon EMR cluster cost efficient.
Speakers:
Ian Meyers, AWS Solutions Architect
Ian McDonald, IT Director, SwiftKey
At wetter.com we build analytical B2B data products and heavily use Spark and AWS technologies for data processing and analytics. I explain why we moved from AWS EMR to Databricks and Delta and share our experiences from different angles like architecture, application logic and user experience. We will look how security, cluster configuration, resource consumption and workflow changed by using Databricks clusters as well as how using Delta tables simplified our application logic and data operations.
Build a simple data lake on AWS using a combination of services, including AWS Glue Data Catalog, AWS Glue Crawlers, AWS Glue Jobs, AWS Glue Studio, Amazon Athena, Amazon Relational Database Service (Amazon RDS), and Amazon S3.
Link to the blog post and video: https://garystafford.medium.com/building-a-simple-data-lake-on-aws-df21ca092e32
Amazon Web Services gives you fast access to flexible and low cost IT resources, so you can rapidly scale and build virtually any big data and analytics application including data warehousing, clickstream analytics, fraud detection, recommendation engines, event-driven ETL, serverless computing, and internet-of-things processing regardless of volume, velocity, and variety of data.
In this one-hour webinar, we will look at the portfolio of AWS Big Data services and how they can be used to build a modern data architecture.
We will cover:
Using different SQL engines to analyze large amounts of structured data
Analysing streaming data in near-real time
Architectures for batch processing
Best practices for Data Lake architectures
This session is suited for:
Solution and enterprise architects
Data architects/ Data warehouse owners
IT & Innovation team members
This overview presentation discusses big data challenges and provides an overview of the AWS Big Data Platform by covering:
- How AWS customers leverage the platform to manage massive volumes of data from a variety of sources while containing costs.
- Reference architectures for popular use cases, including, connected devices (IoT), log streaming, real-time intelligence, and analytics.
- The AWS big data portfolio of services, including, Amazon S3, Kinesis, DynamoDB, Elastic MapReduce (EMR), and Redshift.
- The latest relational database engine, Amazon Aurora— a MySQL-compatible, highly-available relational database engine, which provides up to five times better performance than MySQL at one-tenth the cost of a commercial database.
Created by: Rahul Pathak,
Sr. Manager of Software Development
(BDT208) A Technical Introduction to Amazon Elastic MapReduceAmazon Web Services
"Amazon EMR provides a managed framework which makes it easy, cost effective, and secure to run data processing frameworks such as Apache Hadoop, Apache Spark, and Presto on AWS. In this session, you learn the key design principles behind running these frameworks on the cloud and the feature set that Amazon EMR offers. We discuss the benefits of decoupling compute and storage and strategies to take advantage of the scale and the parallelism that the cloud offers, while lowering costs. Additionally, you hear from AOL’s Senior Software Engineer on how they used these strategies to migrate their Hadoop workloads to the AWS cloud and lessons learned along the way.
In this session, you learn the benefits of decoupling storage and compute and allowing them to scale independently; how to run Hadoop, Spark, Presto and other supported Hadoop Applications on Amazon EMR; how to use Amazon S3 as a persistent data-store and process data directly from Amazon S3; dDeployment strategies and how to avoid common mistakes when deploying at scale; and how to use Spot instances to scale your transient infrastructure effectively."
Building Serverless ETL Pipelines with AWS Glue - AWS Summit Sydney 2018Amazon Web Services
Building Serverless ETL Pipelines with AWS Glue
In this session we will introduce key ETL features of AWS Glue and cover common use cases ranging from scheduled nightly data warehouse loads to near real-time, event-driven ETL flows for your data lake. We will also discuss how to build scalable, efficient, and serverless ETL pipelines.
Ben Thurgood, Solutions Architect, Amazon Web Services
A closer look at the MySQL and PostgreSQL compatible relational database built for the cloud that combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. We’ll explore how Aurora uses the AWS cloud to provide high reliability, high durability, and high throughput.
Speakers:
Steve Abraham - Principal Database Specialist Solutions Architect, AWS
Peter Dachnowicz - Sr. Technical Account Manager, AWS
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.
The introductory morning session will discuss big data challenges and provide an overview of the AWS Big Data Platform. We will also cover:
• How AWS customers leverage the platform to manage massive volumes of data from a variety of sources while containing costs.
• Reference architectures for popular use cases, including: connected devices (IoT), log streaming, real-time intelligence, and analytics.
• The AWS big data portfolio of services, including Amazon S3, Kinesis, DynamoDB, Elastic MapReduce (EMR) and Redshift.
• The latest relational database engine, Amazon Aurora - a MySQL-compatible, highly-available relational database engine which provides up to five times better performance than MySQL at a price one-tenth the cost of a commercial database.
• Amazon Machine Learning – the latest big data service from AWS provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology.
Apache Kafka With Spark Structured Streaming With Emma Liu, Nitin Saksena, Ra...HostedbyConfluent
Apache Kafka With Spark Structured Streaming With Emma LIU, Nitin Saksena, Ram Dhakne | Current 2022
A well-architected data lakehouse provides an open data platform that combines streaming with data warehousing, data engineering, data science and ML. This opens a world beyond streaming to solving business problems in real-time with analytics and AI. See how companies like Albertsons have used Databricks and Confluent together to combine Kafka streaming with Databricks for their digital transformation.
In this talk, you will learn:
- The built-in streaming capabilities of a lakehouse
- Best practices for integrating Kafka with Spark Structured Streaming
- How Albertsons architected their data platform for real-time data processing and real-time analytics
A data lake is an architectural approach that allows you to store massive amounts of data into a central location, so it's readily available to be categorized, processed, analyzed and consumed by diverse groups within an organization.In this session, we will introduce the Data Lake concept and its implementation on AWS.We will explain the different roles our services play and how they fit into the Data Lake picture.
AWS Floor 28 - Building Data lake on AWSAdir Sharabi
AWS makes it easy to build and operate a highly scalable and flexible data platforms to collect, process, and analyze data so you can get timely insights and react quickly to new information. In this session we will talk about how to improve over time using your data. How do you take your everyday data and build relevant business insights, to help and continuously improve your business processes, and keep your innovation going based on your data.
Amazon Elastic MapReduce is one of the largest Hadoop operators in the world. Since its launch five years ago, AWS customers have launched more than 5.5 million Hadoop clusters.
In this talk, we introduce you to Amazon EMR design patterns such as using Amazon S3 instead of HDFS, taking advantage of both long and short-lived clusters and other Amazon EMR architectural patterns. We talk about how to scale your cluster up or down dynamically and introduce you to ways you can fine-tune your cluster. We also share best practices to keep your Amazon EMR cluster cost efficient.
Speakers:
Ian Meyers, AWS Solutions Architect
Ian McDonald, IT Director, SwiftKey
At wetter.com we build analytical B2B data products and heavily use Spark and AWS technologies for data processing and analytics. I explain why we moved from AWS EMR to Databricks and Delta and share our experiences from different angles like architecture, application logic and user experience. We will look how security, cluster configuration, resource consumption and workflow changed by using Databricks clusters as well as how using Delta tables simplified our application logic and data operations.
Build a simple data lake on AWS using a combination of services, including AWS Glue Data Catalog, AWS Glue Crawlers, AWS Glue Jobs, AWS Glue Studio, Amazon Athena, Amazon Relational Database Service (Amazon RDS), and Amazon S3.
Link to the blog post and video: https://garystafford.medium.com/building-a-simple-data-lake-on-aws-df21ca092e32
Amazon Web Services gives you fast access to flexible and low cost IT resources, so you can rapidly scale and build virtually any big data and analytics application including data warehousing, clickstream analytics, fraud detection, recommendation engines, event-driven ETL, serverless computing, and internet-of-things processing regardless of volume, velocity, and variety of data.
In this one-hour webinar, we will look at the portfolio of AWS Big Data services and how they can be used to build a modern data architecture.
We will cover:
Using different SQL engines to analyze large amounts of structured data
Analysing streaming data in near-real time
Architectures for batch processing
Best practices for Data Lake architectures
This session is suited for:
Solution and enterprise architects
Data architects/ Data warehouse owners
IT & Innovation team members
This overview presentation discusses big data challenges and provides an overview of the AWS Big Data Platform by covering:
- How AWS customers leverage the platform to manage massive volumes of data from a variety of sources while containing costs.
- Reference architectures for popular use cases, including, connected devices (IoT), log streaming, real-time intelligence, and analytics.
- The AWS big data portfolio of services, including, Amazon S3, Kinesis, DynamoDB, Elastic MapReduce (EMR), and Redshift.
- The latest relational database engine, Amazon Aurora— a MySQL-compatible, highly-available relational database engine, which provides up to five times better performance than MySQL at one-tenth the cost of a commercial database.
Created by: Rahul Pathak,
Sr. Manager of Software Development
(BDT208) A Technical Introduction to Amazon Elastic MapReduceAmazon Web Services
"Amazon EMR provides a managed framework which makes it easy, cost effective, and secure to run data processing frameworks such as Apache Hadoop, Apache Spark, and Presto on AWS. In this session, you learn the key design principles behind running these frameworks on the cloud and the feature set that Amazon EMR offers. We discuss the benefits of decoupling compute and storage and strategies to take advantage of the scale and the parallelism that the cloud offers, while lowering costs. Additionally, you hear from AOL’s Senior Software Engineer on how they used these strategies to migrate their Hadoop workloads to the AWS cloud and lessons learned along the way.
In this session, you learn the benefits of decoupling storage and compute and allowing them to scale independently; how to run Hadoop, Spark, Presto and other supported Hadoop Applications on Amazon EMR; how to use Amazon S3 as a persistent data-store and process data directly from Amazon S3; dDeployment strategies and how to avoid common mistakes when deploying at scale; and how to use Spot instances to scale your transient infrastructure effectively."
Building Serverless ETL Pipelines with AWS Glue - AWS Summit Sydney 2018Amazon Web Services
Building Serverless ETL Pipelines with AWS Glue
In this session we will introduce key ETL features of AWS Glue and cover common use cases ranging from scheduled nightly data warehouse loads to near real-time, event-driven ETL flows for your data lake. We will also discuss how to build scalable, efficient, and serverless ETL pipelines.
Ben Thurgood, Solutions Architect, Amazon Web Services
A closer look at the MySQL and PostgreSQL compatible relational database built for the cloud that combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. We’ll explore how Aurora uses the AWS cloud to provide high reliability, high durability, and high throughput.
Speakers:
Steve Abraham - Principal Database Specialist Solutions Architect, AWS
Peter Dachnowicz - Sr. Technical Account Manager, AWS
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.
The introductory morning session will discuss big data challenges and provide an overview of the AWS Big Data Platform. We will also cover:
• How AWS customers leverage the platform to manage massive volumes of data from a variety of sources while containing costs.
• Reference architectures for popular use cases, including: connected devices (IoT), log streaming, real-time intelligence, and analytics.
• The AWS big data portfolio of services, including Amazon S3, Kinesis, DynamoDB, Elastic MapReduce (EMR) and Redshift.
• The latest relational database engine, Amazon Aurora - a MySQL-compatible, highly-available relational database engine which provides up to five times better performance than MySQL at a price one-tenth the cost of a commercial database.
• Amazon Machine Learning – the latest big data service from AWS provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology.
Apache Kafka With Spark Structured Streaming With Emma Liu, Nitin Saksena, Ra...HostedbyConfluent
Apache Kafka With Spark Structured Streaming With Emma LIU, Nitin Saksena, Ram Dhakne | Current 2022
A well-architected data lakehouse provides an open data platform that combines streaming with data warehousing, data engineering, data science and ML. This opens a world beyond streaming to solving business problems in real-time with analytics and AI. See how companies like Albertsons have used Databricks and Confluent together to combine Kafka streaming with Databricks for their digital transformation.
In this talk, you will learn:
- The built-in streaming capabilities of a lakehouse
- Best practices for integrating Kafka with Spark Structured Streaming
- How Albertsons architected their data platform for real-time data processing and real-time analytics
A data lake is an architectural approach that allows you to store massive amounts of data into a central location, so it's readily available to be categorized, processed, analyzed and consumed by diverse groups within an organization.In this session, we will introduce the Data Lake concept and its implementation on AWS.We will explain the different roles our services play and how they fit into the Data Lake picture.
AWS Floor 28 - Building Data lake on AWSAdir Sharabi
AWS makes it easy to build and operate a highly scalable and flexible data platforms to collect, process, and analyze data so you can get timely insights and react quickly to new information. In this session we will talk about how to improve over time using your data. How do you take your everyday data and build relevant business insights, to help and continuously improve your business processes, and keep your innovation going based on your data.
The first step towards knowing your customer is to collect and extract insights and actionable information from your data. Learn how AWS enables you to cost efficiently store any amount of data and build an agile approach to data mining and visualization - helping you to make efficient business decisions and targeted offerings.
Big Data Meets AI - Driving Insights and Adding Intelligence to Your SolutionsAmazon Web Services
Over 90% of today’s data has been generated in the last two years, and growth rates continue to climb. In this session, we’ll step through challenges and best practices with data capturing, how to derive meaningful insights to help predict the future, and common pitfalls in data analysis.
Come discover how integrated solutions involving Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon Machine Learning, and Deep Learning on AWS result in effective data systems for data scientists and business users, alike.
Ben Snively, Principal Solutions Architect, AWS; Kate Werling, Solutions Architect, AWS
Building Your Geospatial Data Lake (WPS324) - AWS re:Invent 2018Amazon Web Services
In this chalk talk, we discuss how to design a data lake, and how to permission different groups and applications to access and analyze datasets. Learn from subject-matter experts about a variety of AWS technologies for populating your data lake, monitoring new ingestion, and processing data for meaningful analysis. We examine considerations for structured data, such as relevant database engines with geospatial support, as well as considerations for unstructured data in the form of object storage. In addition, we address how to protect and secure data based on an organization’s needs.
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.
Social Media Analytics with Amazon QuickSight (ANT370) - AWS re:Invent 2018Amazon Web Services
Realizing the value of social media analytics can bolster your business goals. This type of analysis has grown in recent years due to the large amount of available information and the speed at which it can be collected and analyzed. In this workshop, we build a serverless data processing and machine learning (ML) pipeline that provides a multi-lingual social media dashboard of tweets within Amazon QuickSight. We leverage API-driven ML services, AWS Glue, Amazon Athena and Amazon QuickSight. These building blocks are put together with very little code by leveraging serverless offerings within AWS.
Modern Cloud Data Warehousing ft. Equinox Fitness Clubs: Optimize Analytics P...Amazon Web Services
Most companies are overrun with data, yet they lack critical insights to make timely and accurate business decisions. They are missing the opportunity to combine large amounts of new, unstructured big data that resides outside their data warehouse with trusted, structured data inside their data warehouse. In this session, we discuss the most common use cases with Amazon Redshift, and we take an in-depth look at how modern data warehousing blends and analyzes all your data to give you deeper insights to run your business. Equinox Fitness Clubs joins us to share their journey from static reports, redundant data, and inefficient data intergration to a modern and flexible data lake and data warehouse architecture that delivers dynamic reports based on trusted data.
Big Data on AWS - To infinity and beyond! - Tel Aviv Summit 2018Amazon Web Services
A data lake is an architectural approach that allows you to store massive amounts of data into a central location, so it's readily available to be categorized, processed, analyzed and consumed by diverse groups within an organization.
In this session, we will introduce the Data Lake concept and its implementation on AWS.
We will explain the different roles our services play and how they fit into the Data Lake picture.
The first step towards knowing your customer is to collect and extract insights and actionable information from your data. Learn how AWS enables you to cost efficiently store any amount of data and build an agile approach to data mining and visualization - helping you to make efficient business decisions and targeted offerings.
Data Analytics Week at the San Francisco Loft
Data Warehouses and Data Lakes
Organizations use reports, dashboards, and analytics tools to extract insights from their data, monitor performance, and support decision making. To support these tools, data must be collected and prepared for use. We'll look at two approaches: a structured centralized data repository as a Data Warehouse the less-structured repository of a Data Lake. We'll compare these approaches, examine the services that support each, and explore how they work together.
Speakers:
John Mallory - Principal Business Development Manager, Storage, AWS
Hemant Borole - Sr. Big Data Consultant, AWS
Building a Data Lake for Your Enterprise, ft. Sysco (STG309) - AWS re:Invent ...Amazon Web Services
Data lakes are transforming the way enterprises store, analyze, and learn insights from their data. While data lakes are a relatively new concept, many enterprises have already generated significant business value from the insights gleaned. In this session, AWS experts and technology leaders from Sysco, a Fortune 50 company and leader in food distribution and marketing, explain why Sysco decided to evolve its data management capabilities to include data lakes and how they customized them to support diverse querying capabilities and data science use cases. They also discuss how to architect different aspects of a data lake—ingestion from disparate sources, data consumption, and usability layers—and how to track data ingestion and consumption, monitor associated costs, enforce wanted levels of user access, manage data file formats, synchronize production and non-production environments, and maintain data integrity. Services to be discussed include Amazon S3 and S3 Select, Amazon Athena, Amazon EMR, Amazon EC2, and Amazon Redshift Spectrum.
Data Warehouses & Data Lakes: Data Analytics Week at the SF LoftAmazon Web Services
Data Warehouses and Data Lakes: Data Analytics Week at the San Francisco Loft
Organizations use reports, dashboards, and analytics tools to extract insights from their data, monitor performance, and support decision making. To support these tools, data must be collected and prepared for use. We'll look at two approaches: a structured centralized data repository as a Data Warehouse the less-structured repository of a Data Lake. We'll compare these approaches, examine the services that support each, and explore how they work together.
Level: Intermediate
Speakers:
Aser Moustafa - Data Warehouse Specialist Solutions Architect, AWS
Asim Kumar Sasmal - Big Data Consultant, AWS
What's New with Amazon Redshift ft. McDonald's (ANT350-R1) - AWS re:Invent 2018Amazon Web Services
Learn about the latest and hottest features of Amazon Redshift. We’ll deep dive into the architecture and inner workings of Amazon Redshift and discuss how the recent availability, performance, and manageability improvements we’ve made can significantly enhance your user experience. We’ll also share glimpse of what we are working on and our plans for the future. McDonald's will join us to share how they leverage a data lake powered by Redshift, Redshift spectrum and Athena to get quick insights.
Organizations use reports, dashboards, and analytics tools to extract insights from their data, monitor performance, and support decision making. To support these tools, data must be collected and prepared for use. We'll look at two approaches: a structured centralized data repository as a Data Warehouse the less-structured repository of a Data Lake. We'll compare these approaches, examine the services that support each, and explore how they work together.
Level: Intermediate
Speakers:
Jay Formosa - Solutions Architect, AWS
Aser Moustafa - Data Warehouse Specialist Solutions Architect, AWS
Saunak Chandra - Partner Solutions Architect, Redshift Specialist, AWS
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.