Amazon Redshift is a fast, petabyte-scale data warehouse that makes it easy to analyze your data for a fraction of the cost of traditional data warehouses.
In this webinar, you will learn how to easily migrate your data from other data warehouses into Amazon Redshift, efficiently load your data with Amazon Redshift's massively parallel processing (MPP) capabilities, and automate data loading with AWS Lambda and AWS Data Pipeline. You will also learn about ETL tools from our partners to extract, transform, and prepare data from disparate data sources before loading it into Amazon Redshift.
Learning Objectives:
Understand common patterns for migrating your data to Amazon Redshift
See live examples of the Copy command that fully parallelizes data ingestion
Learn how to automate the load process using AWS Lambda & AWS Data Pipleline
Techniques for real time data loading
Options for ETL tools from our partners
Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze 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 use work load management.
Learning Objectives:
• Get an inside look at Amazon Redshift's columnar technology and parallel processing capabilities
• Learn how to migrate from existing data warehouses, optimize 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
(ISM303) Migrating Your Enterprise Data Warehouse To Amazon RedshiftAmazon Web Services
Learn how Boingo Wireless and online media provider Edmunds gained substantial business insights and saved money and time by migrating to Amazon Redshift. Get an inside look into how they accomplished their migration from on-premises solutions. Learn how they tuned their schema and queries to take full advantage of the columnar MPP architecture in Amazon Redshift, how they leveraged third party solutions, and how they met their business intelligence needs in record time.
This document provides an overview and use cases for Amazon Redshift, a fast, fully managed, petabyte-scale data warehouse service from Amazon Web Services. It summarizes Redshift's features including columnar storage, data compression, and massively parallel query processing. It also provides examples of how Redshift is used by companies to reduce costs, improve query performance, and scale their data warehousing needs. Specific use cases and customers of Redshift are highlighted.
Deep Dive Amazon Redshift for Big Data Analytics - September Webinar SeriesAmazon Web Services
Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze 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, optimize schemas, and load data efficiently
• Learn best practices for managing workload, tuning your queries, and using Amazon Redshift's interleaved sorting features
This document discusses Amazon Redshift, a fully managed data warehousing service. It provides petabyte-scale data warehousing capabilities with performance up to 3x faster and 80% lower cost than traditional data warehousing solutions. The document outlines use cases, architecture details, pricing and total cost of ownership, security features, integration options and best practices. It also shares customer examples and an ecosystem of partners building solutions on Amazon Redshift.
AWS June Webinar Series - Getting Started: Amazon RedshiftAmazon Web Services
Amazon Redshift is a fast, fully-managed petabyte-scale data warehouse service, for less than $1,000 per TB per year. In this presentation, you'll get an overview of Amazon Redshift, including how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. Learn how, with just a few clicks in the AWS Management Console, you can set up with a fully functional data warehouse, ready to accept data without learning any new languages and easily plugging in with the existing business intelligence tools and applications you use today. This webinar is ideal for anyone looking to gain deeper insight into their data, without the usual challenges of time, cost and effort. In this webinar, you will learn: • Understand what Amazon Redshift is and how it works • Create a data warehouse interactively through the AWS Management Console • Load some data into your new Amazon Redshift data warehouse from S3 Who Should Attend • IT professionals, developers, line-of-business managers
Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools.
This webinar will provide an overview of Redshift with an emphasis on the many changes we recently introduced. In particular, we will address the newly released DW2 instance types and what you can do with them.
This content is designed for database developers and architects interested in Amazon Redshift.
Redshift is Amazon's cloud data warehousing service that allows users to interact with S3 storage and EC2 compute. It uses a columnar data structure and zone maps to optimize analytic queries. Data is distributed across nodes using either an even or keyed approach. Sort keys and queries are optimized using statistics from ANALYZE operations while VACUUM reclaims space. Security, monitoring, and backups are managed natively with Redshift.
Amazon Redshift is a fully managed data warehouse service that makes it fast, simple and cost effective to analyze data using SQL and existing business intelligence tools. The document provides an overview of Amazon Redshift and its benefits including speed, low cost, security, scalability and ease of use. It also provides examples of how various companies use Redshift for big data analytics including analyzing social media firehoses, mobile usage and real-time IoT streaming data.
Near Real-Time Data Analysis With FlyData FlyData Inc.
This document describes our products. FlyData makes it easy to load data automatically and continuously to Amazon Redshift. You can also refer to our HP ( http://flydata.com/ ) for more information.
Take an in-depth look at data warehousing with Amazon Redshift and get answers to your technical questions. We will cover performance tuning techniques that take advantage of Amazon Redshift's columnar technology and massively parallel processing architecture. We will also discuss best practices for migrating from existing data warehouses, optimizing your schema, loading data efficiently, and using work load management and interleaved sorting.
A quick tour in 16 slides of Amazon's Redshift clustered, massively parallel database.
Find out what differentiates it from the other database products Amazon has, including SimpleDB, DynamoDB and RDS (MySQL, SQL Server and Oracle).
Learn how it stores data on disk in a columnar format and how this relates to performance and interesting compression techniques.
Contrast the difference between Redshift and a MySQL instance and discover how the clustered architecture may help to dramatically reduce query time.
Traditional data warehouses become expensive and slow down as the volume of your data grows. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it easy to analyze all of your data using existing business intelligence tools for 1/10th the traditional cost. This session will provide an introduction to Amazon Redshift and cover the essentials you need to deploy your data warehouse in the cloud so that you can achieve faster analytics and save costs. We’ll also cover the recently announced Redshift Spectrum, which allows you to query unstructured data directly from Amazon S3.
RealityMine collects digital user behavior data to help companies with marketing, product development, and analyzing user patterns. They are migrating from an on-premise SQL Server data warehouse to Amazon Redshift to handle doubling data volumes. Redshift provides better performance and scalability at lower cost compared to other options. It requires extracting raw data from SQL Server without encoding issues, loading to S3, and transforming in Redshift using a star schema with careful consideration of distribution and sort keys for query performance. Ongoing database maintenance and backups are also different in Redshift.
In this presentation, you will get a look under the covers of Amazon Redshift, a fast, fully-managed, petabyte-scale data warehouse service for less than $1,000 per TB per year. Learn how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. We'll also walk through techniques for optimizing performance and, you’ll hear from a specific customer and their use case to take advantage of fast performance on enormous datasets leveraging economies of scale on the AWS platform.
Speakers:
Ian Meyers, AWS Solutions Architect
Toby Moore, Chief Technology Officer, Space Ape
AWS July Webinar Series: Amazon Redshift Optimizing PerformanceAmazon Web Services
This document provides an overview and best practices for optimizing performance on Amazon Redshift. It discusses topics like data distribution, sort keys, compression, loading data efficiently, vacuum operations, and query processing. The webinar agenda covers architecture, distribution styles, sort keys, compression, workload management and more. Examples are provided to demonstrate how different techniques can significantly improve query performance. Administrative scripts and views are also recommended as helpful tools.
Best Practices for Migrating your Data Warehouse to Amazon Redshift Amazon Web Services
This document provides best practices for migrating a data warehouse to Amazon Redshift. It discusses why companies migrate to Redshift due to its scalability, performance and cost advantages. Example migration stories are provided from companies that achieved significant improvements after migrating large datasets from Oracle, Greenplum and SQL on Hadoop to Redshift. The document also outlines the Redshift cluster architecture, data loading best practices including file splitting and column encoding, schema design considerations and available migration tools.
In this presentation, you will get a look under the covers of Amazon Redshift, a fast, fully-managed, petabyte-scale data warehouse service for less than $1,000 per TB per year. Learn how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. We'll also walk through techniques for optimizing performance and, you’ll hear from a specific customer and their use case to take advantage of fast performance on enormous datasets leveraging economies of scale on the AWS platform.
Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. It is fast, inexpensive, and fully managed. Some key benefits include being 10x faster and cheaper than traditional data warehouses, with high availability and disaster recovery built-in. It is easy to set up and use, and has a large ecosystem of integration and business intelligence tools. Common use cases include analytics on large volumes of mobile, web, IoT and operational data. The presentation provides an overview of Amazon Redshift and how to get started, including provisioning a cluster, data modeling best practices, and loading and querying data.
Big Data Infrastructure: Introduction to Hadoop with MapReduce, Pig, and Hiveodsc
The main objective of this workshop is to give the audience hands on experience with several Hadoop technologies and jump start their hadoop journey. In this workshop, you will load data and submit queries using Hadoop! Before jumping in to the technology, the Founders of DataKitchen review Hadoop and some of its technologies (MapReduce, Hive, Pig, Impala and Spark), look at performance, and present a rubric for choosing which technology to use when.
Sensitive customer data needs to be protected throughout AWS. This session discusses the options available for encrypting data at rest in AWS. It focuses on several scenarios, including transparent AWS management of encryption keys on behalf of the customer to provide automated server-side encryption and customer key management using partner solutions or AWS CloudHSM. This session is helpful for anyone interested in protecting data stored in AWS.
This document contains slides from a presentation given at the AWS Government, Education, and Nonprofit Symposium on June 25-26, 2015 in Washington, DC. The presentation discusses how to architect applications on AWS for high availability using services like Auto Scaling, Elastic Load Balancing, Route 53, and multiple Availability Zones. It also provides an overview of AWS global infrastructure and security features.
Hybrid Infrastructure Integration is an approach to connect on-premises IT resources with AWS and bridge processes, services, and technologies used in common enterprise customer environments. This session addresses connectivity patterns, security controls, account governance, and operations monitoring approaches successfully implemented in enterprise engagements. Infrastructure architects and IT professionals can get an overview of various integration types, approaches, methodologies, and common service patterns, helping them to better understand and overcome typical challenges in hybrid enterprise environments.
Learn how you can leverage AWS Platform to tackle all Business Intelligence challenges (Real-time, Data Warehousing performance and Business Intelligence democratisation)
Get the Most Out of Amazon EC2: A Deep Dive on Reserved, On-Demand, and Spot ...Amazon Web Services
With Amazon EC2, you have the flexibility to mix-and-match purchasing models to suit your business needs. By combining pay-as-you-go (On-Demand), reserve ahead of time for discounts (Reserved), and high-discount spare capacity (Spot) purchasing models, you can optimize cost, grow your compute capacity and throughput, and enable new types of cloud computing applications. This presentation will guide you on how to achieve high performance and availability at the lowest TCO. We will explore how to best combine EC2's purchasing models across several common applications with immediately actionable takeaways.
This session is for IT pros working with compliance managers to deliver solutions that lower costs and still meet compliance demands. You will learn how to move large scale data stores to the cloud, while remaining compliant with existing regulations. Services mentioned: S3, Glacier and the Vault Lock feature, Snowball, ingestion services.
AWS March 2016 Webinar Series Getting Started with Serverless ArchitecturesAmazon Web Services
Serverless Architectures allow you to build and run applications and services without having to manage the infrastructure. With serverless architectures on AWS, your application still runs on servers, but all the server management is done by AWS.
In this webinar, you will learn how to build applications and services using a serverless architecture. We will discuss how you can use AWS Lambda to run code for any type of application or backend service; use Amazon DynamoDB to store application data with high scalability and redundancy; and use Amazon API Gateway to create and manage secure API endpoints. We will also run through a demo setting up a web application using this architecture, and discuss best practices and patterns used by our customers to run serverless applications.
Learning Objectives:
• Understand the basics of serverless architectures
• Learn how to use Lambda, API Gateway, and DynamoDB to run web applications
Who Should Attend:
• Developers, web developers
With AWS, you can choose the right storage service like including Amazon Simple Storage Service (Amazon S3) and Amazon Elastic Block Storage (Amazon EBS) for the right use case. This session shows the range of AWS choices—from object storage to block storage—that are available to you. The sessions will also include specifics about real-world deployments from customers who are using Amazon S3, Amazon EBS, Amazon Glacier, and AWS Storage Gateway.
In this session, learn how you evaluate, design, build, and manage distributed applications over hybrid infrastructures using Amazon Web Services. This session follows the evolution of a simple legacy data center expansion with basic connectivity into managing complex hybrid applications. Along the way, we investigate best practice designs in use by AWS customers. Topics covered include interconnectivity, availability, security, and hybrid networks with Amazon VPC and AWS Direct Connect, as well as automated provisioning with AWS CloudFormation and configuration management with AWS OpsWorks.
Create mobile apps quickly and easily. We manage the back end, so you don’t have to provision, scale, or monitor servers – just upload code and you’re done. Onboard new users and synchronize their data, such as app preferences, across multiple devices. Engage users by sending push notifications, track usage patterns and optimize your business with in-app analytics. Deliver high quality apps by testing them against a large collection of real phones and tablets. Start simple and add more services at any time.
An insider view of some of the innovations that help make the AWS cloud unique. We will show examples of innovative service offerings and will continue to discuss data center, power, and networking innovations used across the AWS platform. Join this session and walk away with a deeper understanding of the underlying innovations powering the cloud.
Pinterest is rolling out a phased platform migration from EC2-Classic to EC2-VPC. We used ClassicLink to link our EC2-Classic instances to VPCs, and we applied AWS best practices to configure VPC subnets and security groups. In this session, we share the lessons we learned along the way, and we also show you how to create a migration strategy and track migration costs.
Compute Without Servers – Building Applications with AWS Lambda - Technical 301Amazon Web Services
AWS Lambda enables developers to build scalable applications without managing servers. Come learn how Lambda's event driven approach helps build backend ingestion systems, real time stream processing, and scalable API backends. We will deep dive into the different approaches that customers have taken to building applications with Lambda, typical architectures that customers use Lambda for, and best practices for authoring, deploying, and managing Lambda functions.
Speaker: Ajay Nair, Sr Product Manager Lambda, Amazon Web Services
(DEV204) Building High-Performance Native Cloud Apps In C++Amazon Web Services
The document provides an overview of the AWS SDK for C++, including its core features such as credential management, asynchronous requests, rate limiting, error handling, and memory allocation. It also discusses how to override the HTTP/TLS stack and integrate high-level APIs. The presentation encourages attendees to contribute high-level APIs and send pull requests to the SDK's GitHub repository.
This document provides an overview of a serverless workshop on building microservices for a zombie apocalypse chat application. The workshop will introduce AWS Lambda, Amazon API Gateway, DynamoDB and other AWS serverless services. Attendees will work in teams to implement features like user typing indicators, SMS integration, message search and sensors for the chat app. The goal is to experience building event-driven architectures without having to manage servers. Special challenges will provide extra credit opportunities. The total estimated cost for running the 3 hour workshop on AWS serverless services is less than $1.
The document provides best practices for using AWS Identity and Access Management (IAM) to control access to AWS resources. It recommends 10 steps for basic user and permission management including creating individual users, granting least privilege, using groups, and restricting privileged access. It also recommends steps for credential management like rotating credentials regularly and enabling multi-factor authentication. The document discusses using IAM roles to delegate access and share permissions across accounts or with EC2 instances. It provides examples of when to use IAM users versus federated users and AWS access keys versus passwords.
How to use Ansible to automate your applications in AWS. What is Ansible and why is it different? How to control cloud deployments securely and how to control AWS resources using dynamic inventory and tags.
This document provides instructions for setting up a big data application on AWS using various AWS services. It describes using Amazon Kinesis Firehose to collect web server logs from an EC2 instance into an S3 bucket. It then describes using Amazon EMR with Spark and Hive to process the data, Amazon Redshift for data analysis, and Amazon QuickSight for visualization. The document contains detailed steps for setting up IAM roles, security groups, launching the EC2 instance and EMR cluster, and ingesting and exploring the log data with Spark SQL and Zeppelin notebooks.
Migrate your Data Warehouse to Amazon Redshift - September Webinar SeriesAmazon Web Services
- TrueCar migrated their data warehouse from an on-premises Hadoop cluster to Amazon Redshift. They load clickstream, transactions, inventory, and lead data into Redshift for analytics and reporting.
- They use ETL tools like Talend and Hive to process data and load it into HDFS and S3, then load it into Redshift using a custom utility. The data is organized into schemas separating raw, user, and reporting data.
- Best practices for Redshift include designing tables for compression, sort keys, and distribution, managing cluster size and workloads over time, and vacuuming and analyzing tables regularly. TrueCar's migration to Redshift improved performance and reduced costs.
(BDT303) Construct Your ETL Pipeline with AWS Data Pipeline, Amazon EMR, and ...Amazon Web Services
This document discusses Coursera's use of AWS services like Amazon Redshift, EMR, and Data Pipeline to consolidate their data from various sources, make the data easier for analysts and users to access, and increase the reliability of their data infrastructure. It describes how Coursera programmatically defined ETL pipelines using these services to extract, transform, and load data between sources like MySQL, Cassandra, S3, and Redshift. It also discusses how they built reporting and visualization tools to provide self-service access to the data and ensure high data quality and availability.
A quick overview of Redshift and common use-cases. Followed by tools and links to performance tuning. How Redshift fits in the AWS data services. A list of key new features since last meetup in September 2016, including Redshift Spectrum that allows one to run SQL directly on your data sitting on Amazon S3. It also includes Redshift echosystem with data integration, bi, consultancy and data modelling partners.
This document provides an overview and best practices for using Amazon Redshift as a data warehouse. It discusses ingestion best practices like using multiple files for COPY and primary keys. It also covers data hygiene practices like analyzing tables and vacuuming regularly. Recent features like automatic compression, table restore, UDFs and interleaved sort keys are described. The document provides guidance on migrating workloads and tuning queries, including using WLM queues and the performance monitor in the console.
by Peter Dalton, Principal Consultant AWS and Taz Sayed, Sr Technical Account Manager AWS
AWS Data & Analytics Week is an opportunity to learn about Amazon’s family of managed analytics services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon Redshift data warehouse; Data Lake services including Amazon EMR, Amazon Athena, & Amazon Redshift Spectrum; Log Analytics with Amazon Elasticsearch Service; and data preparation and placement services with AWS Glue and Amazon Kinesis. You'll will learn how to get started, how to support applications, and how to scale.
Loading Data into Redshift: Data Analytics Week at the SF LoftAmazon Web Services
Loading Data into Redshift: Data Analytics Week at the San Francisco Loft
How do you get data from your sources into your Redshift data warehouse? We'll show how to use AWS Glue and Amazon Kinesis Firehose to make it easy to automate the work to get data loaded.
Level: Intermediate
Speakers:
Aser Moustafa - Data Warehouse Specialist Solutions Architect, AWS
Vikram Gangulavoipalyam - Enterprise Solutions Architect, AWS
Learn tuning best practices for taking advantage of Amazon Redshift's columnar technology and parallel processing capabilities to improve your delivery of queries and improve overall database performance. This session explains how to migrate from existing data warehouses, create an optimized schema, efficiently load data, use work load management, tune your queries, and use Amazon Redshift's interleaved sorting features. Finally, learn how to use these best practices to give their entire organization access to analytic insights at scale.
Presented by: Alex Sinner, Solutions Architecture PMO, Amazon Web Services
Customer Guest: Luuk Linssen, Product Manager, Bannerconnect
How do you get data from your sources into your Redshift data warehouse? We'll show how to use AWS Glue and Amazon Kinesis Firehose to make it easy to automate the work to get data loaded.
Level: Intermediate
Speakers:
Jay Formosa - Solutions Architect, AWS
Aser Moustafa - Data Warehouse Specialist Solutions Architect, AWS
Data Analytics Week at the San Francisco Loft
Loading Data Into Redshift
How do you get data from your sources into your Redshift data warehouse? We'll show how to use AWS Glue and Amazon Kinesis Firehose to make it easy to automate the work to get data loaded.
Speakers:
Jay Formosa - Solutions Architect, AWS
Asser Moustafa - Data Warehouse Specialist Solutions Architect, AWS
SQL Server 2008 Development for ProgrammersAdam Hutson
The document outlines a presentation by Adam Hutson on SQL Server 2008 development for programmers, including an overview of CRUD and JOIN basics, dynamic versus compiled statements, indexes and execution plans, performance issues, scaling databases, and Adam's personal toolbox of SQL scripts and templates. Adam has 11 years of database development experience and maintains a blog with resources for SQL topics.
(BDT404) Large-Scale ETL Data Flows w/AWS Data Pipeline & DataductAmazon Web Services
"As data volumes grow, managing and scaling data pipelines for ETL and batch processing can be daunting. With more than 13.5 million learners worldwide, hundreds of courses, and thousands of instructors, Coursera manages over a hundred data pipelines for ETL, batch processing, and new product development.
In this session, we dive deep into AWS Data Pipeline and Dataduct, an open source framework built at Coursera to manage pipelines and create reusable patterns to expedite developer productivity. We share the lessons learned during our journey: from basic ETL processes, such as loading data from Amazon RDS to Amazon Redshift, to more sophisticated pipelines to power recommendation engines and search services.
Attendees learn:
Do's and don’ts of Data Pipeline
Using Dataduct to streamline your data pipelines
How to use Data Pipeline to power other data products, such as recommendation systems
What’s next for Dataduct"
by Manish Mohite, Solutions Architect, AWS
How do you get data from your sources into your Redshift data warehouse? We'll show how to use AWS Glue and Amazon Kinesis Firehose to make it easy to automate the work to get data loaded.
AWS Glue is a fully managed, serverless extract, transform, and load (ETL) service that makes it easy to move data between data stores. AWS Glue simplifies and automates the difficult and time consuming tasks of data discovery, conversion mapping, and job scheduling so you can focus more of your time querying and analyzing your data using Amazon Redshift Spectrum and Amazon Athena. In this session, we introduce AWS Glue, provide an overview of its components, and share how you can use AWS Glue to automate discovering your data, cataloging it, and preparing it for analysis.
Best Practices for Migrating your Data Warehouse to Amazon RedshiftAmazon Web Services
You can gain substantially more business insights and save costs by migrating your existing data warehouse to Amazon Redshift. This session will cover the key benefits of migrating to Amazon Redshift, migration strategies, and tools and resources that can help you in the process. We’ll learn about AWS Database Migration Service and AWS Schema Migration Tool, which were recently enhanced to import data from six common data warehouse platforms.
How do you get data from your sources into your Redshift data warehouse? We'll show how to use AWS Glue and Amazon Kinesis Firehose to make it easy to automate the work to get data loaded.
Speakers:
Natalie Rabinovich- Solutions Architect, AWS
Gareth Eagar - Solutions Architect, AWS
by Ben Willett, Solutions Architect, AWS
How do you get data from your sources into your Redshift data warehouse? We'll show how to use AWS Glue and Amazon Kinesis Firehose to make it easy to automate the work to get data loaded.
Accelerate Oracle to Aurora PostgreSQL Migration (GPSTEC313) - AWS re:Invent ...Amazon Web Services
There is a lot of interest these days in migrating data from commercial relational databases to open-source relational databases. PostgreSQL is a great choice for migration, offering advanced features, high performance, rock-solid data integrity, and a flexible open-source license. PostgreSQL is compliant with ANSI SQL. It supports drivers for nearly all development languages, and it has a strong community of active committers and companies to provide support. In this talk, we demonstrate an overall approach for migrating an application from your current Oracle database to an Amazon Aurora PostgreSQL database.
Data processing and analysis is where big data is most often consumed - driving business intelligence (BI) use cases that discover and report on meaningful patterns in the data. In this session, we will discuss options for processing, analyzing and visualizing data. We will also look at partner solutions and BI-enabling services from AWS. Attendees will learn about optimal approaches for stream processing, batch processing and Interactive analytics. AWS services to be covered include: Amazon Machine Learning, Elastic MapReduce (EMR), and Redshift.
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
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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.
1) The document discusses building a minimum viable product (MVP) using Amazon Web Services (AWS).
2) It provides an example of an MVP for an omni-channel messenger platform that was built from 2017 to connect ecommerce stores to customers via web chat, Facebook Messenger, WhatsApp, and other channels.
3) The founder discusses how they started with an MVP in 2017 with 200 ecommerce stores in Hong Kong and Taiwan, and have since expanded to over 5000 clients across Southeast Asia using AWS for scaling.
This document discusses pitch decks and fundraising materials. It explains that venture capitalists will typically spend only 3 minutes and 44 seconds reviewing a pitch deck. Therefore, the deck needs to tell a compelling story to grab their attention. It also provides tips on tailoring different types of decks for different purposes, such as creating a concise 1-2 page teaser, a presentation deck for pitching in-person, and a more detailed read-only or fundraising deck. The document stresses the importance of including key information like the problem, solution, product, traction, market size, plans, team, and ask.
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AWS_HK_StartupDay_Building Interactive websites while automating for efficien...Amazon Web Services
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The Zaitechno Handheld Raman Spectrometer is a powerful and portable tool for rapid, non-destructive chemical analysis. It utilizes Raman spectroscopy, a technique that analyzes the vibrational fingerprint of molecules to identify their chemical composition. This handheld instrument allows for on-site analysis of materials, making it ideal for a variety of applications, including:
Material identification: Identify unknown materials, minerals, and contaminants.
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Pharmaceutical analysis: Verify the identity and purity of pharmaceutical compounds.
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Discovery Series - Zero to Hero - Task Mining Session 1DianaGray10
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2. Amazon Redshift – Resources
Getting Started – June Webinar Series:
https://www.youtube.com/watch?v=biqBjWqJi-Q
Best Practices – July Webinar Series:
Optimizing Performance – July 21, 2015
Migration and Data Loading – July 22,2015
Reporting and Advanced Analytics – July 23, 2015
5. Common Migration Patterns
Data from a variety of relational OLTP systems
structure lends itself to SQL schemas
Data from logs, devices, sensors…
data is less structured
6. Structured Data Loading
Data is often being loaded into another warehouse
existing ETL process
Temptation is to ‘lift & shift’ workload.
Resist temptation. Instead consider:
What do I really want to do?
What do I need?
7. Ingesting Less Structured Data
Some data does not lend itself to a relational schema
Common pattern is to use EMR:
impose structure
import into Redshift
Other solutions are often home grown scripting
applications.
8. Loading Data
Load to an empty Redshift database.
Load changes captured in the source system to Redshift
9. Truncate and Load
This is by far the easiest option:
Move the data to Amazon Simple Storage Service
multi-part upload
import/export service
direct connect
COPY the data into Redshift, a table at a time.
10. Load Changes
Identify changes in source systems
Move data to Amazon S3
Load changes
‘Upsert process’
Partner ETL tools
11. Partner ETL
Amazon Redshift is supported by a variety of ETL vendors
Many simplify the process of data loading
Visit http://aws.amazon.com/redshift/partners
There are a variety of vendors offering a free trial of their
products, allowing you to evaluate and choose the one that
suits your needs.
12. Upsert
The goal is to insert new rows and update changed rows in
Redshift.
Load data into a temporary staging table
Join the staging with production and delete the common
rows.
Copy the new data into the production table.
See Updating and Inserting New Data in the developer’s
guide
13. Checkpoint
We’ve talked about common migration patterns
Sources of data and data structure
Methods of getting data to AWS
Options for loading data
15. Amazon Redshift Architecture
Leader Node
• SQL endpoint, JDBC/ODBC
• Stores metadata
• Coordinates query execution
Compute Nodes
• Local, columnar storage
• Execute queries in parallel
• Load, backup, restore via Amazon S3
• Load from Amazon DynamoDB or SSH
Two hardware platforms
• Optimized for data processing
• DS2: HDD; scale from 2TB to 2PB
• DC1: SSD; scale from 160GB to 326TB
10 GigE
(HPC)
Ingestion
Backup
Restore
JDBC/ODBC
16. A Closer Look
Each node is split into slices
• One slice per core
Each slice is allocated
memory, CPU, and disk space
Each slice processes a piece
of the workload in parallel
17. COPY command
COMPUPDATE ON when running on an empty table
Use the COPY command.
Each slice can load one file at a time.
Partition input files so every slice can load in parallel.
Use a Manifest file.
18. Use multiple input files to maximize throughput
Use the COPY command
Each slice can load one file at a
time
A single input file means only one
slice is ingesting data
Instead of 100MB/s, you’re only
getting 6.25MB/s
19. Use multiple input files to maximize throughput
Use the COPY command
You need at least as many input
files as you have slices
With 16 input files, all slices are
working so you maximize
throughput
Get 100MB/s per node; scale
linearly as you add nodes
20. Primary keys and manifest files
Amazon Redshift doesn’t enforce primary key constraints
• If you load data multiple times, Amazon Redshift won’t complain
• If you declare primary keys in your DML, the optimizer will
expect the data to be unique
Use manifest files to control exactly what is loaded and
how to respond if input files are missing
• Define a JSON manifest on Amazon S3
• Ensures the cluster loads exactly what you want
21. Analyze sort/dist key columns after every load
Amazon Redshift’s query
optimizer relies on up-to-date
statistics
Maximize performance by
updating stats on sort/dist key
columns after every load
22. Automatic compression
Better performance, lower costs
COPY samples data automatically when loading into an empty
table
• Samples up to 100,000 rows and picks optimal encoding
If you have a regular ETL process and you use temp tables or
staging tables, turn off automatic compression
• Use analyze compression to determine the right encodings
• Bake those encodings into your DML
23. Checking STL_LOAD_COMMITS
SELECT query, trim(filename) as filename, curtime, status
FROM stl_load_commits
WHERE filename LIKE ’%table name%'
ORDER BY query;
After the load operation is complete, query the
STL_LOAD_COMMITS system table to verify that the
expected files were loaded.
24. COPY and 18 inserts
COPY country FROM
's3://…country.txt' CREDENTIALS …
1.57s then
.
insert into country (country_name)
values ('Slovakia'),('Slovenia'),('South
Africa'),('South Korea'),('Spain'); 5.44s
‘
Insert vs Copy
Commit info
25. COPY best practice
Use it.
Avoid inserts, which will not run in parallel.
If you are moving data from table to another, use the
deep copy features:
1. Use the original CREATE TABLE ddl and then
INSERT INTO … SELECT
2. CREATE TABLE AS
3. CREATE TABLE LIKE
4. Create a temporary table and truncate the
original.
27. Automating Data Ingestion
Many customers run custom scripts on EC2 instances to
load data into Redshift.
Another option is to use the Amazon Data Pipeline
automation tool.
AWS Lambda-based Amazon Redshift Loader
32. Using the Lambda based Redshift Loader
Offers the ability to drop files
into S3 and load them into any
number of database tables in
multiple Amazon Redshift
clusters automatically, with no
servers to maintain.
33. Configure the sample loader
johnlou$ ./configureSample.sh more.ohno.us-east-1.redshift.amazonaws.com 8192 mydb
johnlou us-east-1
Password for user johnlou:
create user test_lambda_load_user password 'Change-me1!';
CREATE USER
create table lambda_redshift_sample(
column_a int,
column_b int,
column_c int
);
CREATE TABLE
Enter the Region for the Redshift Load Configuration > us-east-1
Enter the S3 Bucket to use for the Sample Input > johnlou-ohno/loader-demo-data
Enter the Access Key used by Redshift to get data from S3 > nope
Enter the Secret Key used by Redshift to get data from S3 > nope
Creating Tables in Dynamo DB if Required
Configuration for johnlou-ohno/loader-demo-data/input successfully written in us-east-1
36. Micro-batch loading
Ideal for time series data
Balance input files
Pre-configure column encoding
Reduce frequency of statistics calculation
Load in sort key order
Use SSD instances
Consider using the ‘Load Stream’ architecture HasOffers
developed.
38. Data Loading Options
Parallel upload to Amazon S3
AWS Direct Connect
AWS Import/Export
Amazon Kinesis
Systems integrators
Data Integration Systems Integrators
39. Resources on the AWS Big Data Blog
Best Practices for Micro-Batch Loading on Amazon
Redshift
Using Attunity Cloudbeam at UMUC to Replicate Data
to Amazon RDS and Amazon Redshift
A Zero-Administration Amazon Redshift Database
Loader
40. Best Practices References
Best Practices for Designing Tables
Best Practices for Designing Queries
Best Practices for Loading Data