Snowflake is a cloud-based data warehouse that is built for the cloud. It was founded in 2012 and has raised $1 billion in funding. Snowflake's architecture separates storage, compute, and metadata services, allowing it to offer unlimited scalability, multiple clusters that can access shared data with no downtime, and full transactional consistency across the system. Snowflake has over 2000 customers including large enterprises that use it for analytics, data science, and sharing large volumes of data securely.
Snowflake concepts & hands on expertise to help get you started on implementing Data warehouses using Snowflake. Necessary information and skills that will help you master Snowflake essentials.
Organizations are struggling to make sense of their data within antiquated data platforms. Snowflake, the data warehouse built for the cloud, can help.
Snowflake: The Good, the Bad, and the UglyTyler Wishnoff
Learn how to solve the top 3 challenges Snowflake customers face, and what you can do to ensure high-performance, intelligent analytics at any scale. Ideal for those currently using Snowflake and those considering it. Learn more at: https://kyligence.io/
Snowflake's Kent Graziano talks about what makes a data warehouse as a service and some of the key features of Snowflake's data warehouse as a service.
Snowflake concepts & hands on expertise to help get you started on implementing Data warehouses using Snowflake. Necessary information and skills that will help you master Snowflake essentials.
Organizations are struggling to make sense of their data within antiquated data platforms. Snowflake, the data warehouse built for the cloud, can help.
Snowflake: The Good, the Bad, and the UglyTyler Wishnoff
Learn how to solve the top 3 challenges Snowflake customers face, and what you can do to ensure high-performance, intelligent analytics at any scale. Ideal for those currently using Snowflake and those considering it. Learn more at: https://kyligence.io/
Snowflake's Kent Graziano talks about what makes a data warehouse as a service and some of the key features of Snowflake's data warehouse as a service.
How to Take Advantage of an Enterprise Data Warehouse in the CloudDenodo
Watch full webinar here: [https://buff.ly/2CIOtys]
As organizations collect increasing amounts of diverse data, integrating that data for analytics becomes more difficult. Technology that scales poorly and fails to support semi-structured data fails to meet the ever-increasing demands of today’s enterprise. In short, companies everywhere can’t consolidate their data into a single location for analytics.
In this Denodo DataFest 2018 session we’ll cover:
Bypassing the mandate of a single enterprise data warehouse
Modern data sharing to easily connect different data types located in multiple repositories for deeper analytics
How cloud data warehouses can scale both storage and compute, independently and elastically, to meet variable workloads
Presentation by Harsha Kapre, Snowflake
As cloud computing continues to gather speed, organizations with years’ worth of data stored on legacy on-premise technologies are facing issues with scale, speed, and complexity. Your customers and business partners are likely eager to get data from you, especially if you can make the process easy and secure.
Challenges with performance are not uncommon and ongoing interventions are required just to “keep the lights on”.
Discover how Snowflake empowers you to meet your analytics needs by unlocking the potential of your data.
Agenda of Webinar :
~Understand Snowflake and its Architecture
~Quickly load data into Snowflake
~Leverage the latest in Snowflake’s unlimited performance and scale to make the data ready for analytics
~Deliver secure and governed access to all data – no more silos
Master the Multi-Clustered Data Warehouse - SnowflakeMatillion
Snowflake is one of the most powerful, efficient data warehouses on the market today—and we joined forces with the Snowflake team to show you how it works!
In this webinar:
- Learn how to optimize Snowflake
- Hear insider tips and tricks on how to improve performance
- Get expert insights from Craig Collier, Technical Architect from Snowflake, and Kalyan Arangam, Solution Architect from Matillion
- Find out how leading brands like Converse, Duo Security, and Pets at Home use Snowflake and Matillion ETL to make data-driven decisions
- Discover how Matillion ETL and Snowflake work together to modernize your data world
- Learn how to utilize the impressive scalability of Snowflake and Matillion
Data driven organizations can be challenged to deliver new and growing business intelligence requirements from existing data warehouse platforms, constrained by lack of scalability and performance. The solution for customers is a data warehouse that scales for real-time demands and uses resources in a more optimized and cost-effective manner. Join Snowflake, AWS and Ask.com to learn how Ask.com enhanced BI service levels and decreased expenses while meeting demand to collect, store and analyze over a terabyte of data per day. Snowflake Computing delivers a fast and flexible elastic data warehouse solution that reduces complexity and overhead, built on top of the elasticity, flexibility, and resiliency of AWS.
Join us to learn:
• Learn how Ask.com eliminates data redundancy, and simplifies and accelerates data load, unload, and administration
• Learn how to support new and fluid data consumption patterns with consistently high performance
• Best practices for scaling high data volume on Amazon EC2 and Amazon S3
Who should attend: CIOs, CTOs, CDOs, Directors of IT, IT Administrators, IT Architects, Data Warehouse Developers, Database Administrators, Business Analysts and Data Architects
Every day, businesses across a wide variety of industries share data to support insights that drive efficiency and new business opportunities. However, existing methods for sharing data involve great effort on the part of data providers to share data, and involve great effort on the part of data customers to make use of that data.
However, existing approaches to data sharing (such as e-mail, FTP, EDI, and APIs) have significant overhead and friction. For one, legacy approaches such as e-mail and FTP were never intended to support the big data volumes of today. Other data sharing methods also involve enormous effort. All of these methods require not only that the data be extracted, copied, transformed, and loaded, but also that related schemas and metadata must be transported as well. This creates a burden on data providers to deconstruct and stage data sets. This burden and effort is mirrored for the data recipient, who must reconstruct the data.
As a result, companies are handicapped in their ability to fully realize the value in their data assets.
Snowflake Data Sharing allows companies to grant instant access to ready-to-use data to any number of partners or data customers without any data movement, copying, or complex pipelines.
Using Snowflake Data Sharing, companies can derive new insights and value from data much more quickly and with significantly less effort than current data sharing methods. As a result, companies now have a new approach and a powerful new tool to get the full value out of their data assets.
What is elastic data warehousing, and how does Snowflake uniquely enable it? Learn about the requirements needed to support flexible, elastic data warehousing using cloud infrastructure.
A 30 day plan to start ending your data struggle with SnowflakeSnowflake Computing
Organizations everywhere are struggling to load, integrate, analyze and collaborate with data. This is largely thanks to their antiquated data platform, designed in a time when few people had the desire or need to interact with the database. Snowflake, the data warehouse built for the cloud, can help.
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.
Introduction to Snowflake Datawarehouse and Architecture for Big data company. Centralized data management. Snowpipe and Copy into a command for data loading. Stream loading and Batch Processing.
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
Differentiate Big Data vs Data Warehouse use cases for a cloud solutionJames Serra
It can be quite challenging keeping up with the frequent updates to the Microsoft products and understanding all their use cases and how all the products fit together. In this session we will differentiate the use cases for each of the Microsoft services, explaining and demonstrating what is good and what isn't, in order for you to position, design and deliver the proper adoption use cases for each with your customers. We will cover a wide range of products such as Databricks, SQL Data Warehouse, HDInsight, Azure Data Lake Analytics, Azure Data Lake Store, Blob storage, and AAS as well as high-level concepts such as when to use a data lake. We will also review the most common reference architectures (“patterns”) witnessed in customer adoption.
Delta Lake delivers reliability, security and performance to data lakes. Join this session to learn how customers have achieved 48x faster data processing, leading to 50% faster time to insight after implementing Delta Lake. You’ll also learn how Delta Lake provides the perfect foundation for a cost-effective, highly scalable lakehouse architecture.
Building Lakehouses on Delta Lake with SQL Analytics PrimerDatabricks
You’ve heard the marketing buzz, maybe you have been to a workshop and worked with some Spark, Delta, SQL, Python, or R, but you still need some help putting all the pieces together? Join us as we review some common techniques to build a lakehouse using Delta Lake, use SQL Analytics to perform exploratory analysis, and build connectivity for BI applications.
In this webinar you'll learn how to quickly and easily improve your business using Snowflake and Matillion ETL for Snowflake. Webinar presented by Solution Architects Craig Collier (Snowflake) adn Kalyan Arangam (Matillion).
In this webinar:
- Learn to optimize Snowflake and leverage Matillion ETL for Snowflake
- Discover tips and tricks to improve performance
- Get invaluable insights from data warehousing pros
Continuous Data Replication into Cloud Storage with Oracle GoldenGateMichael Rainey
Continuous flow. Streaming. Near real-time. These are all terms used to identify the business’s need for quick access to data. It’s a common request, even if the data must flow from on-premises to the cloud. Oracle GoldenGate is the data replication solution built for fast data. In this session, we’ll look at how GoldenGate can be configured to extract transactions from the Oracle database and load them into a cloud object store, such as Amazon S3. There are many different use cases for this type of continuous load of data into the cloud. We’ll explore these solutions and the various tools that can be used to access and analyze the data from the cloud object store, leaving attendees with ideas for implementing a full source-to-cloud data replication solution.
Presented at ITOUG Tech Days 2019
How to Take Advantage of an Enterprise Data Warehouse in the CloudDenodo
Watch full webinar here: [https://buff.ly/2CIOtys]
As organizations collect increasing amounts of diverse data, integrating that data for analytics becomes more difficult. Technology that scales poorly and fails to support semi-structured data fails to meet the ever-increasing demands of today’s enterprise. In short, companies everywhere can’t consolidate their data into a single location for analytics.
In this Denodo DataFest 2018 session we’ll cover:
Bypassing the mandate of a single enterprise data warehouse
Modern data sharing to easily connect different data types located in multiple repositories for deeper analytics
How cloud data warehouses can scale both storage and compute, independently and elastically, to meet variable workloads
Presentation by Harsha Kapre, Snowflake
As cloud computing continues to gather speed, organizations with years’ worth of data stored on legacy on-premise technologies are facing issues with scale, speed, and complexity. Your customers and business partners are likely eager to get data from you, especially if you can make the process easy and secure.
Challenges with performance are not uncommon and ongoing interventions are required just to “keep the lights on”.
Discover how Snowflake empowers you to meet your analytics needs by unlocking the potential of your data.
Agenda of Webinar :
~Understand Snowflake and its Architecture
~Quickly load data into Snowflake
~Leverage the latest in Snowflake’s unlimited performance and scale to make the data ready for analytics
~Deliver secure and governed access to all data – no more silos
Master the Multi-Clustered Data Warehouse - SnowflakeMatillion
Snowflake is one of the most powerful, efficient data warehouses on the market today—and we joined forces with the Snowflake team to show you how it works!
In this webinar:
- Learn how to optimize Snowflake
- Hear insider tips and tricks on how to improve performance
- Get expert insights from Craig Collier, Technical Architect from Snowflake, and Kalyan Arangam, Solution Architect from Matillion
- Find out how leading brands like Converse, Duo Security, and Pets at Home use Snowflake and Matillion ETL to make data-driven decisions
- Discover how Matillion ETL and Snowflake work together to modernize your data world
- Learn how to utilize the impressive scalability of Snowflake and Matillion
Data driven organizations can be challenged to deliver new and growing business intelligence requirements from existing data warehouse platforms, constrained by lack of scalability and performance. The solution for customers is a data warehouse that scales for real-time demands and uses resources in a more optimized and cost-effective manner. Join Snowflake, AWS and Ask.com to learn how Ask.com enhanced BI service levels and decreased expenses while meeting demand to collect, store and analyze over a terabyte of data per day. Snowflake Computing delivers a fast and flexible elastic data warehouse solution that reduces complexity and overhead, built on top of the elasticity, flexibility, and resiliency of AWS.
Join us to learn:
• Learn how Ask.com eliminates data redundancy, and simplifies and accelerates data load, unload, and administration
• Learn how to support new and fluid data consumption patterns with consistently high performance
• Best practices for scaling high data volume on Amazon EC2 and Amazon S3
Who should attend: CIOs, CTOs, CDOs, Directors of IT, IT Administrators, IT Architects, Data Warehouse Developers, Database Administrators, Business Analysts and Data Architects
Every day, businesses across a wide variety of industries share data to support insights that drive efficiency and new business opportunities. However, existing methods for sharing data involve great effort on the part of data providers to share data, and involve great effort on the part of data customers to make use of that data.
However, existing approaches to data sharing (such as e-mail, FTP, EDI, and APIs) have significant overhead and friction. For one, legacy approaches such as e-mail and FTP were never intended to support the big data volumes of today. Other data sharing methods also involve enormous effort. All of these methods require not only that the data be extracted, copied, transformed, and loaded, but also that related schemas and metadata must be transported as well. This creates a burden on data providers to deconstruct and stage data sets. This burden and effort is mirrored for the data recipient, who must reconstruct the data.
As a result, companies are handicapped in their ability to fully realize the value in their data assets.
Snowflake Data Sharing allows companies to grant instant access to ready-to-use data to any number of partners or data customers without any data movement, copying, or complex pipelines.
Using Snowflake Data Sharing, companies can derive new insights and value from data much more quickly and with significantly less effort than current data sharing methods. As a result, companies now have a new approach and a powerful new tool to get the full value out of their data assets.
What is elastic data warehousing, and how does Snowflake uniquely enable it? Learn about the requirements needed to support flexible, elastic data warehousing using cloud infrastructure.
A 30 day plan to start ending your data struggle with SnowflakeSnowflake Computing
Organizations everywhere are struggling to load, integrate, analyze and collaborate with data. This is largely thanks to their antiquated data platform, designed in a time when few people had the desire or need to interact with the database. Snowflake, the data warehouse built for the cloud, can help.
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.
Introduction to Snowflake Datawarehouse and Architecture for Big data company. Centralized data management. Snowpipe and Copy into a command for data loading. Stream loading and Batch Processing.
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
Differentiate Big Data vs Data Warehouse use cases for a cloud solutionJames Serra
It can be quite challenging keeping up with the frequent updates to the Microsoft products and understanding all their use cases and how all the products fit together. In this session we will differentiate the use cases for each of the Microsoft services, explaining and demonstrating what is good and what isn't, in order for you to position, design and deliver the proper adoption use cases for each with your customers. We will cover a wide range of products such as Databricks, SQL Data Warehouse, HDInsight, Azure Data Lake Analytics, Azure Data Lake Store, Blob storage, and AAS as well as high-level concepts such as when to use a data lake. We will also review the most common reference architectures (“patterns”) witnessed in customer adoption.
Delta Lake delivers reliability, security and performance to data lakes. Join this session to learn how customers have achieved 48x faster data processing, leading to 50% faster time to insight after implementing Delta Lake. You’ll also learn how Delta Lake provides the perfect foundation for a cost-effective, highly scalable lakehouse architecture.
Building Lakehouses on Delta Lake with SQL Analytics PrimerDatabricks
You’ve heard the marketing buzz, maybe you have been to a workshop and worked with some Spark, Delta, SQL, Python, or R, but you still need some help putting all the pieces together? Join us as we review some common techniques to build a lakehouse using Delta Lake, use SQL Analytics to perform exploratory analysis, and build connectivity for BI applications.
In this webinar you'll learn how to quickly and easily improve your business using Snowflake and Matillion ETL for Snowflake. Webinar presented by Solution Architects Craig Collier (Snowflake) adn Kalyan Arangam (Matillion).
In this webinar:
- Learn to optimize Snowflake and leverage Matillion ETL for Snowflake
- Discover tips and tricks to improve performance
- Get invaluable insights from data warehousing pros
Continuous Data Replication into Cloud Storage with Oracle GoldenGateMichael Rainey
Continuous flow. Streaming. Near real-time. These are all terms used to identify the business’s need for quick access to data. It’s a common request, even if the data must flow from on-premises to the cloud. Oracle GoldenGate is the data replication solution built for fast data. In this session, we’ll look at how GoldenGate can be configured to extract transactions from the Oracle database and load them into a cloud object store, such as Amazon S3. There are many different use cases for this type of continuous load of data into the cloud. We’ll explore these solutions and the various tools that can be used to access and analyze the data from the cloud object store, leaving attendees with ideas for implementing a full source-to-cloud data replication solution.
Presented at ITOUG Tech Days 2019
Data Warehouse - Incremental Migration to the CloudMichael Rainey
A data warehouse (DW) migration is no small undertaking, especially when moving from on-premises to the cloud. A typical data warehouse has numerous data sources connecting and loading data into the DW, ETL tools and data integration scripts performing transformations, and reporting, advanced analytics, or ad-hoc query tools accessing the data for insights and analysis. That’s a lot to coordinate and the data warehouse cannot be migrated all at once. Using a data replication technology such as Oracle GoldenGate, the data warehouse migration can be performed incrementally by keeping the data in-sync between the original DW and the new, cloud DW. This session will dive into the steps necessary for this incremental migration approach and walk through a customer use case scenario, leaving attendees with an understanding of how to perform a data warehouse migration to the cloud.
Presented at RMOUG Training Days 2019
Demystifying Data Warehousing as a Service (GLOC 2019)Kent Graziano
Extended deck from the 2019 GLOC event in Cleveland. Discusses what a DWaaS is, the top 10 features of Snowflake that represent that, and a check list for what questions to ask when choosing a cloud based data warehouse.
Snowflake’s Cloud Data Platform and Modern AnalyticsSenturus
See a demo and learn how Snowflake meets the needs of performant BI. Designed to handle both structured and unstructured data, Snowflake can serve as a single data repository, providing elastic performance and scalability.
Senturus offers a full spectrum of services in business intelligence and training on Tableau, Power BI and Cognos. Our resource library has hundreds of free live and recorded webinars, blog posts, demos and unbiased product reviews available on our website at: http://www.senturus.com/senturus-resources/.
Delivering Data Democratization in the Cloud with SnowflakeKent Graziano
This is a brief introduction to Snowflake Cloud Data Platform and our revolutionary architecture. It contains a discussion of some of our unique features along with some real world metrics from our global customer base.
From the Data Work Out event:
Performant and scalable Data Science with Dataiku DSS and Snowflake
Managing the whole process of setting up a machine learning environment from end-to-end becomes significantly easier when using cloud-based technologies. The ability to provision infrastructure on demand (IaaS) solves the problem of manually requesting virtual machines. It also provides immediate access to compute resources whenever they are needed. But that still leaves the administrative overhead of managing the ML software and the platform to store and manage the data.
A fully managed end-to-end machine learning platform like Dataiku Data Science Studio (DSS) that enables data scientists, machine learning experts, and even business users to quickly build, train and host machine learning models at scale, needs to access data from many different sources and can also access data provided by Snowflake. Storing data in Snowflake has three significant advantages: a single source of truth, shorten the data preparation cycle, scale as you go.
Delivering rapid-fire Analytics with Snowflake and TableauHarald Erb
Until recently, advancements in data warehousing and analytics were largely incremental. Small innovations in database design would herald a new data warehouse every
2-3 years, which would quickly become overwhelmed with rapidly increasing data volumes. Knowledge workers struggled to access those databases with development intensive BI tools designed for reporting, rather than exploration and sharing. Both databases and BI tools were strained in locally hosted environments that were inflexible to growth or change.
Snowflake and Tableau represent a fundamentally different approach. Snowflake’s multi-cluster shared data architecture was designed for the cloud and to handle logarithmically larger data volumes at blazing speed. Tableau was made to foster an interactive approach to analytics, freeing knowledge workers to use the speed of Snowflake to their greatest advantage.
Cloudera + Syncsort: Fuel Business Insights, Analytics, and Next Generation T...Precisely
Effective AI and ML projects require a perfect blend of scalable, clean data funneled from a variety of sources across the business. The only problem? Uncleaned data often lives in hard-to-access legacy systems, and it costs time and money to build the right foundation to deliver that data to answer ever-changing questions from business users. Together, Cloudera and Syncsort enable you to build a scalable foundation of data connections to reinvent the data lifecycle of all your projects in the most efficient way possible.
View this webinar on-demand to learn how innovative solutions from Cloudera and Syncsort enable AI and ML success. You will learn:
• Best practices for transforming complex data into clear, actionable insights for AI and ML projects
• How to visually assess the quality of the sources in your data lake and their completeness, consistency, and accuracy
• The value of an Enterprise Data Cloud and the newly unveiled Cloudera Data Platform
• How Syncsort Connect integrates natively with the Cloudera Data Platform
Watch full webinar here: https://bit.ly/3dhbZTK
What started to evolve as the most agile and real-time enterprise data fabric, data virtualization is proving to go beyond its initial promise and is becoming one of the most important enterprise big data fabrics.
Watch this session to learn:
- What data virtualization really is.
- How it differs from other enterprise data integration technologies.
- Why data virtualization is finding enterprise-wide deployment inside some of the largest organizations.
Enabling the Active Data Warehouse with Apache KuduGrant Henke
Apache Kudu is an open source data storage engine that makes fast analytics on fast and changing data easy. In this presentation, Grant Henke from Cloudera will provide an overview of what Kudu is, how it works, and how it makes building an active data warehouse for real time analytics easy. Drawing on experiences from some of our largest deployments, this talk will also include an overview of common Kudu use cases and patterns. Additionally, some of the newest Kudu features and what is coming next will be covered.
How to select a modern data warehouse and get the most out of it?Slim Baltagi
In the first part of this talk, we will give a setup and definition of modern cloud data warehouses as well as outline problems with legacy and on-premise data warehouses.
We will speak to selecting, technically justifying, and practically using modern data warehouses, including criteria for how to pick a cloud data warehouse and where to start, how to use it in an optimum way and use it cost effectively.
In the second part of this talk, we discuss the challenges and where people are not getting their investment. In this business-focused track, we cover how to get business engagement, identifying the business cases/use cases, and how to leverage data as a service and consumption models.
Orit Alul (Sr. Solutions Architect) @ AWS:
As data is growing at an exponential rate, we are interested not only in being able to analyze the past or present but also in predicting the future!
In this session, Orit will talk about the power of data combined with machine learning.
Building a highly scalable and flexible data architecture in the cloud to collect, process, and analyze data, in order to get timely insights and react quickly to new information.
In addition, Orit will present best practices, performance and optimization tips for building a Data Lake in the cloud.
Google take on heterogeneous data base replication Svetlin Stanchev
Datastream from Google is a serverless change data capture and replication service. This allows organizations to replicate data across multiple databases, storage systems and is especially useful for replicating OLTP data in MySQL into an OLAP database such as BigQuery. This talk walks through setting up connection profiles, streams and touch on some useful debugging if things don't go as planned
C cloud organizational_impacts_big_data_on-prem_vs_off-premise_john_singJohn Sing
Internet-scale cloud data centers and cloud technology has fundamentally changed the IT and Internet landscape. What is less apparent but absolutely essential, is the very different *IT organizational structure* that must exist in order to properly implement, manage, support, and scale a cloud IT infrastructure. This extensive chart deck, provided in full PowerPoint format, explains these significant and non-avoidable IT organizational changes required. Bottom line: it is (unfortunately) impossible for a traditional IT organization to provide a true modern autonomically managed, scalable, cost-effective cloud infrastructure
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.