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    • Cloud Data Warehouse with PowerCenter and Amazon Redshift – Deep Dive & Demo
      Cloud Data Warehouse with PowerCenter and Amazon Redshift – Deep Dive & Demo Meera Srinivasan, Prateek Shrivastava, Andrew McIntyre, Product Management Recorded: May 2 2017 3:00 pm UTC 61 mins
    • In this technical session we will discuss the reasons many customers are extending PowerCenter to support cloud data warehouses on AWS with Amazon Redshift and review common patterns for Amazon Redshift adoption. We will overview the Amazon Redshift architecture and discuss available options from Informatica for extending PowerCenter to support Amazon Redshift. We will share details about using prebuilt Informatica connectors for Amazon Redshift and other AWS services such as Amazon S3, and overview advanced scaling features such as grid, partitioning and pushdown optimization.

      Key webinar learnings:
      • AWS journey and Amazon Redshift: Key benefits and capabilities.
      • Key use cases for extending PowerCenter with Amazon Redshift.
      • Extending with PowerCenter Redshift connector vs extending with Informatica Cloud Redshift connector.
      • Scalability features like grid, partioning and pushdown optimization to maximize the power of Amazon Redshift
      • AWS deployment options: Single-click deployment of PowerCenter on AWS EC2 and Informatica Cloud ‘Pay as You Go’ via AWS Marketplace
      • Demos of key use cases

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    • Building Big Data Solutions with Amazon EMR and Amazon Redshift
      Building Big Data Solutions with Amazon EMR and Amazon Redshift Rahul Pathak, Amazon EMR & Ian Meyers, Solutions Architecture Senior Manager, AWS Recorded: Sep 28 2016 11:30 am UTC 48 mins
    • Building big data applications often requires integrating a broad set of technologies to store, process, and analyze the increasing variety, velocity, and volume of data being collected by many organisations.

      Using a combination of Amazon EMR, a managed Hadoop framework and Amazon Redshift, a managed petabyte-scale data warehouse, organisations can effectively address many of these requirements.

      In this webinar, we will show how organisations are using Amazon EMR and Amazon Redshift to build more agile and scalable architectures for big data. We will look into how you can leverage Spark and Presto running on Amazon EMR, to address multiple data processing requirements. We will also share best practices and common use cases to integrate Amazon EMR and Amazon Redshift.

      Learning Objectives:
      • Best practices for building a big data architecture that includes Amazon EMR and Amazon Redshift
      • Understand how to use technologies such Amazon EMR, Presto and Spark to complement your data warehousing environment
      • Learn key use cases for Amazon EMR and Amazon Redshift

      Who Should Attend:
      • Data architects, Data management professionals, Data warehousing professionals, BI professionals


      Other sessions on the AWS Big Data Webinar Day - 28 September:

      10:00 - 11:00 GMT | Getting Started with Big Data on AWS
      Register » https://www.brighttalk.com/webcast/9019/221047?utm_campaign=Brighttalk

      11:15 - 12:15 GMT | Architectural Patterns for Big Data on AWS
      Register » https://www.brighttalk.com/webcast/9019/221063?utm_campaign=Brighttalk

      13:45 - 14:45 GMT | Deep Dive: Amazon Redshift for Big Data Analytics
      Register » https://www.brighttalk.com/webcast/9019/221409?utm_campaign=Brighttalk

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    • Deep Dive: Amazon Redshift for Big Data Analytics
      Deep Dive: Amazon Redshift for Big Data Analytics Ian Meyers, Sr. Mgr Solutions Architecture, AWS & Zach Christopherson, Database Engineer, AWS Recorded: Sep 28 2016 12:45 pm UTC 60 mins
    • Analysing big data quickly and efficiently requires a data warehouse optimised to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyse big data for a fraction of the cost of traditional data warehouses. By following a few best practices, you can take advantage of Amazon Redshift’s columnar technology and parallel processing capabilities to minimize I/O and deliver high throughput and query performance. This webinar will cover techniques to load data efficiently, design optimal schemas and tune query and database performance.

      Learning Objectives:
      • Get an inside look at Amazon Redshift's columnar technology and parallel processing capabilities
      • Learn how to migrate from existing data warehouses, optimise schemas and load data efficiently
      • Learn best practices for managing workload, tuning your queries and using Amazon Redshift's interleaved sorting features

      Who Should Attend:
      • Data Warehouse Developers, Big Data Architects, BI Managers and Data Engineers

      Other sessions on the AWS Big Data Webinar Day - 28 September:

      10:00 - 11:00 GMT | Getting Started with Big Data on AWS
      Register » https://www.brighttalk.com/webcast/9019/221047?utm_campaign=Brighttalk

      11:15 - 12:15 GMT | Architectural Patterns for Big Data on AWS
      Register » https://www.brighttalk.com/webcast/9019/221063?utm_campaign=Brighttalk

      12:30 - 13:30 GMT | Building Big Data Solutions with Amazon EMR and Amazon Redshift
      Register » https://www.brighttalk.com/webcast/9019/221145?utm_campaign=Brighttalk

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    • Deploying your Data Warehouse on AWS
      Deploying your Data Warehouse on AWS Ian Meyers, Head of CAT, DBS Outbound & Customer Prgrm, AWS Recorded: Oct 11 2017 10:15 am UTC 58 mins
    • Data warehousing is a critical component for analysing and extracting actionable insights from your data. Amazon Redshift allows you to deploy a scalable data warehouse in a matter minutes and start to analyse your data right away using your existing business intelligence tools. It’s a fast, fully-managed, and cost-effective data warehousing system. You can analyse all your data using standard SQL and your existing Business Intelligence (BI) tools. Amazon Redshift also includes Redshift Spectrum, allowing you to directly run SQL queries against exabytes of unstructured data in Amazon S3. In this, you will learn how to migrate from existing data warehouses, optimise schemas and load data efficiently. We will also cover analytics tools to help you build visualisations, perform ad-hoc analysis and quickly get business insights from your data.

      Learning Objectives:

      • Discover best practices for building a data warehouse using Amazon Redshift

      • Learn to use Amazon QuickSight for Business Intelligence and AWS Glue for ETL.

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    • Red Hat’s Adoption of Cloud Analytics Architecture
      Red Hat’s Adoption of Cloud Analytics Architecture Ian Firman, Red Hat; Andrew McIntyre, Informatica; Meera Srinivasan, Informatica Recorded: Aug 31 2017 5:00 pm UTC 62 mins
    • Enterprises adopt cloud for their analytics architecture to support high scalability, speed to value and timely analytics. Join Ian Firman, Data Warehouse Architect at Red Hat, to go under the hood and understand how they drive massive scale and performance of their analytics environment, deal with huge volumes of data across cloud and on-premises, and deliver trusted data for advanced analytics, data modeling and deep learning using Informatica Intelligent Cloud Services and Amazon Web Services Redshift.

      During this webinar, you will hear:

      • The challenges of the traditional data warehouse and how to overcome them with a cloud data warehouse
      • Architectural best practices to manage data across hybrid environment using an enterprise data warehouse on-premises and in the cloud
      • How Amazon Redshift helps in scaling, performance and speed to value

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    • AWS Masterclass - Analisi dei Dati
      AWS Masterclass - Analisi dei Dati Danilo Poccia Recorded: Sep 30 2015 9:00 am UTC 66 mins
    • In questo Webinar andremo ad approfondire le caratteristiche di Amazon Redshift, un servizio di data warehouse veloce, completamente gestito, e su scala di petabyte per meno di 1000$ per TB all’anno. Scopri come Amazon Redshift utilizza una tecnologia colonnare, hardware ottimizzato ed un sistema di processamento parallelo per ottenere risposte rapide alle query su data set di differente dimensione, da centinaia di Gigabyte ad uno o più Petabyte. Inoltre, andremo a scoprire tecniche di ottimizzazione della performance ed avrai la possibilità di ascoltare la testimonianza di uno specifico cliente, che racconterà come trae vantaggio dalle elevate performance su enormi dataset, grazie alle economie di scala sulla piattaforma AWS.

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    • Operationalizing Advanced Analytics on the Cloud for Quick Wins
      Operationalizing Advanced Analytics on the Cloud for Quick Wins Jody Porrazzo, Principal Data Scientist, UBM & Anwesa Chatterjee, Director of Product Marketing, Cloud, Informatica Recorded: Apr 27 2016 3:30 pm UTC 58 mins
    • The adoption of cloud analytics is accelerating in companies of all sizes and across industries. For an increasing number of companies in all industries around the globe, their center of gravity for mission-critical business data is moving to cloud applications. Companies are looking to use cloud analytics solutions to accelerate the delivery of actionable business insights. Cloud offers strong advantages in terms of speed and agility. Let us hear from UBM how they have implemented their cloud analytics environment using Amazon Redshift and visualize it via powerful dashboards using Tableau.

      Join Informatica and UBM to hear about the best practices fueling UBM’s innovative cloud analytics initiatives.

      In this webinar, hosted by Cloud Computing Journal, you’ll learn:

      •How UBM achieves predictive insights for millions of their customers across various channels like events, online activity, social and mobile sources
      •Why Cloud Analytics can reduce the cost and improve the performance of large scale data by orders of magnitude
      •How using Informatica Cloud they have gained speed, and saved cost

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    • AWS Startup Webinar #8: Análisis de los datos
      AWS Startup Webinar #8: Análisis de los datos Carlos Conde Recorded: Nov 6 2014 3:30 pm UTC 35 mins
    • Ahora el equipo tiene más experiencia y una sólida base de usuarios.
      El último paso en su camino en la creación de una startup exitosa es ser capaz de mejorar su producto de forma continua a través de una mejor comprensión de los comportamientos y necesidades de los usuarios.

      En este capítulo se abordará el análisis de los datos y se explicará cómo hacer uso de servicios como Amazon EMR y Amazon Redshift para analizar los datos generados por los diferentes componentes de las aplicaciones, de modo que se pueda extraer información sobre el comportamiento de los usuarios.

      Servicios y características tratados:
      •Amazon Redshift
      •Amazon EMR

      Demostración:
      •Envío de datos de logs a Amazon S3
      •Arranque de un clúster Redshift e ingestión de datos de logs

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    • Komfortable Datensynchronisation nach Amazon Web Services mit Informatica Cloud
      Komfortable Datensynchronisation nach Amazon Web Services mit Informatica Cloud Michael Kohs, Senior Sales Consultant Cloud, Informatica GmbH Recorded: Apr 16 2015 12:00 pm UTC 55 mins
    • Immer mehr Unternehmen evaluieren Amazon Web Services als Zielplattform für die Umsetzung von neuen Projekten. Dabei kann auf eine umfangreiche Auswahl von Services, die vom Virtualisierungsservice (EC2) über relationale Datenbanken (RDS) bis hin zur Echtzeitverarbeitung von Streaming Big Data (Kinesis) reichen, zurückgegriffen werden. Unabhängig von der Serviceauswahl, stellt sich in jedem Projekt die Frage nach der Integration mit bereits existierenden und zukünftig in Frage kommenden Systemen.

      Informatica Cloud bietet Unternehmen eine Integrationsplattform, die speziell für die Anforderungen von Cloud Applikationen entwickelt wurde. Mit Hilfe von verfügbaren Konnektoren zu internen und Cloud-basierten Anwendungen, Datenbanken, Flat Files und sozialen Netzwerken, lassen sich auch hybride Integrationsszenarien problemlos abbilden. Dabei bieten Schritt-für-Schritt Assistenten eine Anleitung für gängige Anwendungsfälle der Cloud-Integration und ermöglichen es so auch unerfahrenen Anwendern schnell erste Integrationsprozesse zu implementieren.

      Mit Informatica Cloud für Amazon Web Services können Unternehmen mit Hilfe von verfügbaren Konnektoren, Daten von und nach Amazon S3, Amazon RDS, Amazon DynamoDB und Amazon Redshift synchronisieren. Insbesondere der Redshift-Konnektor vereinfacht und automatisiert den komplexen Ladeprozess und stellt sicher, dass der von Amazon empfohlene hochparallele Lademechanismus genutzt wird.

      Nehmen Sie an diesem Webinar teil um zu erfahren wie die Informatica Cloud Plattform Unternehmen hilft, Amazon Web Services effizienter und komfortabler zu nutzen. Anhand einer Demo sehen Sie wie ein exemplarischer End-to-End Prozess von der initialen Beladung einer Redshift-Datenbank bis zur Auswertung der Daten mit Tableau, realisiert werden kann.

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    • Data Loading to the Amazon Cloud for Faster Business Insights
      Data Loading to the Amazon Cloud for Faster Business Insights Chris Hayman, Data Architect at AWS; Brad Helicher, Attunity's Director of Cloud Business Recorded: Dec 12 2013 3:00 pm UTC 30 mins
    • In order to take full advantage of AWS cloud storage, including S3, RDS, and Redshift, as well as benefit from the promise of Big Data analytics, companies must first successfully load data from on-premises sources or move data between AWS regions. This can often be a challenging feat that involves a lot of complex and time-consuming scripting, data transfer, and inaccurate loading.

      In this webinar, presented by Chris Hayman, Data Architect at AWS, and Brad Helicher, Attunity's Director of Cloud Business, we will review the benefits and features of the various AWS repositories available, the common challenges of data loading to these repositories, and introduce Attunity CloudBeam as a best practices solution for overcoming the common, but often back-breaking data transfer bottleneck.

      Attend and you’ll also learn about:

      • Common data transfer pitfalls to avoid
      • Simplifying and accelerating data loading for Big Data Analytics
      • Best practices for maximizing the value of Amazon S3, RDS and Redshift

      Read more >