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
A closer look at the fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. We'll show how to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution.
Level: Beginner
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
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
Data Warehousing with Amazon Redshift: Data Analytics Week SFAmazon Web Services
Data Analytics Week at the San Francisco Loft
Data Warehousing with Amazon Redshift
Asser Moustafa - Data Warehouse Specialist Solutions Architect, AWSA closer look at the fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. We'll show how to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution.
Speakers:
Jay Formosa - Solutions Architect, AWS
Asser Moustafa - Data Warehouse Specialist 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.
by Andre Hass, Specialist Technical Account Manager, AWS
A closer look at the fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. We'll show how to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution.
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. You can start small for just $0.25 per hour with no commitment or upfront costs and scale to a petabyte or more for $1,000 per terabyte per year, less than a tenth of most other data warehousing solutions.
See a recording of the webinar based on this presentation here on YouTube: https://youtu.be/GgLKodmL5xE
Masterclass series webinars, including on-demand access to all of this years recorded webinars: http://aws.amazon.com/campaigns/emea/masterclass/
Journey Through the Cloud webinar series, including on-demand access to all webinars so far this year: http://aws.amazon.com/campaigns/emea/journey/
by Rajeev Srinivasan, Sr. Solutions Architect, AWS
Amazon DynamoDB is a fast and flexible NoSQL database service for all applications that need consistent, single-digit millisecond latency at any scale. It is a fully managed cloud database and supports both document and key-value store models. Its flexible data model, reliable performance, and automatic scaling of throughput capacity, makes it a great fit for mobile, web, gaming, ad tech, IoT, and many other applications. We’ll take a look at how DynamoDB works and how it can be accelerated by DAX, the DynamoDB Accelerator.
A closer look at the fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. We'll show how to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution.
Level: Beginner
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
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
Data Warehousing with Amazon Redshift: Data Analytics Week SFAmazon Web Services
Data Analytics Week at the San Francisco Loft
Data Warehousing with Amazon Redshift
Asser Moustafa - Data Warehouse Specialist Solutions Architect, AWSA closer look at the fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. We'll show how to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution.
Speakers:
Jay Formosa - Solutions Architect, AWS
Asser Moustafa - Data Warehouse Specialist 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.
by Andre Hass, Specialist Technical Account Manager, AWS
A closer look at the fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. We'll show how to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution.
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. You can start small for just $0.25 per hour with no commitment or upfront costs and scale to a petabyte or more for $1,000 per terabyte per year, less than a tenth of most other data warehousing solutions.
See a recording of the webinar based on this presentation here on YouTube: https://youtu.be/GgLKodmL5xE
Masterclass series webinars, including on-demand access to all of this years recorded webinars: http://aws.amazon.com/campaigns/emea/masterclass/
Journey Through the Cloud webinar series, including on-demand access to all webinars so far this year: http://aws.amazon.com/campaigns/emea/journey/
by Rajeev Srinivasan, Sr. Solutions Architect, AWS
Amazon DynamoDB is a fast and flexible NoSQL database service for all applications that need consistent, single-digit millisecond latency at any scale. It is a fully managed cloud database and supports both document and key-value store models. Its flexible data model, reliable performance, and automatic scaling of throughput capacity, makes it a great fit for mobile, web, gaming, ad tech, IoT, and many other applications. We’ll take a look at how DynamoDB works and how it can be accelerated by DAX, the DynamoDB Accelerator.
by Marie Yap, Enterprise Solutions Architect, AWS
A closer look at the fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. We'll show how to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution.
In this talk, Ian will table about Amazon Redshift, a managed petabyte scale data warehouse, give an overview of integration with Amazon Elastic MapReduce, a managed Hadoop environment, and cover some exciting new developments in the analytics space.
Amazon Redshift é um serviço gerenciado que lhe dá um Data Warehouse, pronto para usar. Você se preocupa com carregar dados e utilizá-lo. Os detalhes de infraestrutura, servidores, replicação, backup são administrados pela AWS.
by Maor Kleider, Sr. Technical Product Manager, AWS
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. Level: 200
A closer look at the fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. We'll show how to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution.
Speakers:
Karan Desai - Solutions Architect, AWS
Neel Mitra - Solutions Architect, AWS
Building a Modern Data Warehouse: Deep Dive on Amazon Redshift - SRV337 - Chi...Amazon Web Services
In this chalk talk, we take a deep dive on Amazon Redshift architecture and the latest performance enhancements that give you faster insights into your data. We also cover Amazon Redshift Spectrum, a feature of Amazon Redshift that enables you to analyze data across Amazon Redshift and your Amazon S3 data lake to deliver unique insights not possible by analyzing independent data silos.
Best Practices for Migrating Your Data Warehouse to Amazon RedshiftAmazon Web Services
by Darin Briskman, Technical Evangelist, AWS
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. Level: 200
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
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.
by Mike Labib, In-Memory NoSQL Solutions Architect, AWS
Redis has exploded in popularity to become the de-facto standard for in-memory key-value store used by customers for fast data storage to accelerate databases and applications. In this talk, we will discuss how to leverage Redis to achieve blazing fast performance in a variety of use cases – from database caching, to messaging, queuing, IoT and more. Both high-level architecture considerations and implementation (with code snippets) will be covered. We will also see how using Amazon ElastiCache makes it easy to power your Redis workloads in a robust, secure and fully managed way. Level: 200 (requires understanding of database services)
by Mikhail Prudnikov, Sr. Solutions Architect, AWS
Redis is an open source, in-memory data store that delivers sub-millisecond response times enabling millions of requests per second to power real-time applications. It can be used as a fast database, cache, message broker, and queue. Amazon ElastiCache delivers the ease-of-use and power of Redis along with the availability, reliability, scalability, security, and performance suitable for the most demanding applications. We’ll take a close look at Redis and how to use it to power different use cases.
Redshift is a petabyte-scale data warehouse that is a lot faster, a lot less expensive and a whole lot simpler to use. How can you get your data into Amazon Redshift? In this webinar, hear from representatives of Attunity (Amazon Redshift Partner), and AWS as they present many of the options available for data integration. Whether your data is in an on premise platform or a cloud based database like DynamoDB, we will show you how you can easily load your data in to Re
dshift.
Reasons to attend: - Learn about best practices to efficiently integrate data into Redshift. - Attend Q&A session with Redshift experts
by Darin Briskman, Technical Evangelist, AWS
Microsoft SQL Server is a commonly-used commercial relational database, especially for organizations that use Microsoft development tools. We’ll look at how to run SQL Server on the AWS Cloud, with examples of organizations using it.
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 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. You can start small for just $0.25 per hour with no commitment or upfront costs and scale to a petabyte or more for $1,000 per terabyte per year, less than a tenth of most other data warehousing solutions.
In this Masterclass presentation we will:
• Explore the architecture and fundamental characteristics of Amazon Redshift
• Show you how to launch Redshift clusters and to load data into them
• Explain out how to use the AWS Console to monitor and manage Redshift clusters
• Help you to discover best practices and other resources to help you get the most from Redshift
Watch the recording here: http://youtu.be/-FmCWcxRvXY
by Brian Mitchell, Principal Data Architect, AWS
An inside look at how a global e-commerce firm uses AWS technologies to build a scalable environment for data and analytics. We'll look at how Amazon is evolving the world of data warehousing with a combination of a data lake and parallel scalable compute engines including Amazon EMR and Amazon Redshift.
Integrating Amazon Elasticsearch with your DevOps Tooling - AWS Online Tech T...Amazon Web Services
Learning Objectives:
- Learn how to stream Amazon CloudWatch Logs data into Amazon Elasticsearch Service
- Learn how to configure Kibana to visualize your data
- Learn how to get started with Amazon Elasticsearch Service
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
by Marie Yap, Enterprise Solutions Architect, AWS
A closer look at the fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. We'll show how to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution.
In this talk, Ian will table about Amazon Redshift, a managed petabyte scale data warehouse, give an overview of integration with Amazon Elastic MapReduce, a managed Hadoop environment, and cover some exciting new developments in the analytics space.
Amazon Redshift é um serviço gerenciado que lhe dá um Data Warehouse, pronto para usar. Você se preocupa com carregar dados e utilizá-lo. Os detalhes de infraestrutura, servidores, replicação, backup são administrados pela AWS.
by Maor Kleider, Sr. Technical Product Manager, AWS
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. Level: 200
A closer look at the fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. We'll show how to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution.
Speakers:
Karan Desai - Solutions Architect, AWS
Neel Mitra - Solutions Architect, AWS
Building a Modern Data Warehouse: Deep Dive on Amazon Redshift - SRV337 - Chi...Amazon Web Services
In this chalk talk, we take a deep dive on Amazon Redshift architecture and the latest performance enhancements that give you faster insights into your data. We also cover Amazon Redshift Spectrum, a feature of Amazon Redshift that enables you to analyze data across Amazon Redshift and your Amazon S3 data lake to deliver unique insights not possible by analyzing independent data silos.
Best Practices for Migrating Your Data Warehouse to Amazon RedshiftAmazon Web Services
by Darin Briskman, Technical Evangelist, AWS
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. Level: 200
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
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.
by Mike Labib, In-Memory NoSQL Solutions Architect, AWS
Redis has exploded in popularity to become the de-facto standard for in-memory key-value store used by customers for fast data storage to accelerate databases and applications. In this talk, we will discuss how to leverage Redis to achieve blazing fast performance in a variety of use cases – from database caching, to messaging, queuing, IoT and more. Both high-level architecture considerations and implementation (with code snippets) will be covered. We will also see how using Amazon ElastiCache makes it easy to power your Redis workloads in a robust, secure and fully managed way. Level: 200 (requires understanding of database services)
by Mikhail Prudnikov, Sr. Solutions Architect, AWS
Redis is an open source, in-memory data store that delivers sub-millisecond response times enabling millions of requests per second to power real-time applications. It can be used as a fast database, cache, message broker, and queue. Amazon ElastiCache delivers the ease-of-use and power of Redis along with the availability, reliability, scalability, security, and performance suitable for the most demanding applications. We’ll take a close look at Redis and how to use it to power different use cases.
Redshift is a petabyte-scale data warehouse that is a lot faster, a lot less expensive and a whole lot simpler to use. How can you get your data into Amazon Redshift? In this webinar, hear from representatives of Attunity (Amazon Redshift Partner), and AWS as they present many of the options available for data integration. Whether your data is in an on premise platform or a cloud based database like DynamoDB, we will show you how you can easily load your data in to Re
dshift.
Reasons to attend: - Learn about best practices to efficiently integrate data into Redshift. - Attend Q&A session with Redshift experts
by Darin Briskman, Technical Evangelist, AWS
Microsoft SQL Server is a commonly-used commercial relational database, especially for organizations that use Microsoft development tools. We’ll look at how to run SQL Server on the AWS Cloud, with examples of organizations using it.
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 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. You can start small for just $0.25 per hour with no commitment or upfront costs and scale to a petabyte or more for $1,000 per terabyte per year, less than a tenth of most other data warehousing solutions.
In this Masterclass presentation we will:
• Explore the architecture and fundamental characteristics of Amazon Redshift
• Show you how to launch Redshift clusters and to load data into them
• Explain out how to use the AWS Console to monitor and manage Redshift clusters
• Help you to discover best practices and other resources to help you get the most from Redshift
Watch the recording here: http://youtu.be/-FmCWcxRvXY
by Brian Mitchell, Principal Data Architect, AWS
An inside look at how a global e-commerce firm uses AWS technologies to build a scalable environment for data and analytics. We'll look at how Amazon is evolving the world of data warehousing with a combination of a data lake and parallel scalable compute engines including Amazon EMR and Amazon Redshift.
Integrating Amazon Elasticsearch with your DevOps Tooling - AWS Online Tech T...Amazon Web Services
Learning Objectives:
- Learn how to stream Amazon CloudWatch Logs data into Amazon Elasticsearch Service
- Learn how to configure Kibana to visualize your data
- Learn how to get started with Amazon Elasticsearch Service
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
Using data lakes to quench your analytics fire - AWS Summit Cape Town 2018Amazon Web Services
Speaker: Shafreen Sayyed, AWS
Level: 200
Traditional data storage and analytic tools no longer provide the agility and flexibility required to deliver relevant business insights. We are seeing more and more organisations shift to a data lake solution. This approach allows you to store massive amounts of data in a central location so its readily available to be categorized, processed, analyzed, and consumed by diverse organizational groups. In this session, we’ll assemble a data lake using services such as Amazon S3, Amazon Kinesis, Amazon Athena, Amazon EMR, AWS Glue and integration with Amazon Redshift Spectrum.
Building Your First Serverless Data Lake (ANT356-R1) - AWS re:Invent 2018Amazon Web Services
In this session, you have the opportunity to learn the fundamental building blocks of a data lake on AWS. You design and build a serverless pipeline to ingest, process, optimize and query data in your very own data lake. We discuss different optimizations and best practices to tune your architecture for future growth.
Optimising your Amazon Redshift Cluster for Peak PerformanceAmazon Web Services
In this session, we take an in-depth look at the latest features in Amazon Redshift. Analyze data stored in and outside of your cluster with Amazon Redshift Spectrum, accelerate all your analytics workloads, and modernize your on-premises data warehouse. We will focus on best practices for designing optimal schemas, load data efficiently, and optimise queries to deliver high throughput an performance.
Speaker: Ganesh Raja, Solutions Architect, AWS
Optimizing Your Amazon Redshift Cluster for Peak Performance - AWS Summit Syd...Amazon Web Services
Optimising Your Amazon Redshift Cluster for Peak Performance
In this session we take an in-depth look at the latest features in Amazon Redshift, including analysing data store in and outside of your cluster with Amazon Redshift Spectrum, query and platform enhancements, and more. We will dive deep into best practices on how to design optimal schemas, load data efficiently, and optimise your queries to deliver high throughput and performance.
Eric Ferreira , Principal Database Engineer, Amazon Web Services
Migrating Your Data Warehouse to Amazon Redshift (DAT337) - AWS re:Invent 2018Amazon Web Services
Gain in-depth knowledge and best practices for migrating commercial data warehouses to Amazon Redshift using AWS Database Migration Service (AWS DMS) and AWS Schema Conversion Tool (AWS SCT). We use an example based on an Oracle data warehouse, and we discuss approaches to migrate it to Amazon Redshift. We also discuss some of the common challenges, limitations, and workarounds, as well as the option of using AWS Snowball to migrate very large data warehouses to Amazon Redshift.
Data Warehousing with Amazon Redshift: Data Analytics Week at the SF LoftAmazon Web Services
Data Warehousing with Amazon Redshift: Data Analytics Week at the San Francisco Loft
A closer look at the fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. We'll show how to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution.
Level: Beginner
Speakers:
Jay Formosa - Solutions Architect, AWS
Sudhir Gupta - Partner Solutions Architect, Redshift Specialist, AWS
The AWS Big Data services are inherently built to run at @scale. In this session, you will learn how to develop an enterprise scale big data application using AWS services such as Amazon EMR, Amazon Redshift & Redshift Spectrum, Amazon Athena, Amazon Elasticsearch Service, Amazon Kinesis, Amazon QuickSight and AWS Glue. This session will also cover different architectural patterns and customer use cases.
Customers are migrating their analytics, data processing (ETL), and data science workloads running on Apache Hadoop, Spark, and data warehouse appliances from on-premise deployments to AWS in order to save costs, increase availability, and improve performance. AWS offers a broad set of analytics services, including solutions for batch processing, stream processing, machine learning, data workflow orchestration, and data warehousing. This session will focus on identifying the components and workflows in your current environment; and providing the best practices to migrate these workloads to the right AWS data analytics product. We will cover services such as Amazon EMR, Amazon Athena, Amazon Redshift, Amazon Kinesis, and more. We will also feature Vanguard, an American investment management company based in Malvern, Pennsylvania with over $4.4 trillion in assets under management. Ritesh Shah, Sr. Program Manager for Cloud Analytics Program at Vanguard, will describe how they orchestrated their migration to AWS analytics services, including Hadoop and Spark workloads to Amazon EMR. Ritesh will highlight the technical challenges they faced and overcame along the way, as well as share common recommendations and tuning tips to accelerate the time to production.
by Taz Sayed, Sr Technical Account Manager AWS and Marie Yap, Enterprise Solutions Architect 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.
Data warehousing in the era of Big Data: Deep Dive into Amazon RedshiftAmazon 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 all of your data for a fraction of the cost of traditional data warehouses. In this session, we take an in-depth look at data warehousing with Amazon Redshift for big data analytics. We cover best practices to take advantage of Amazon Redshift's columnar technology and parallel processing capabilities to deliver high throughput and query performance. We also discuss how to design optimal schemas, load data efficiently, and use work load management.
Data Warehousing and Data Lake Analytics, Together - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Learn how to discover and prepare your data lake for analytics
- See how you can query across your data warehouse and data lake without moving data
- Understand use cases that give you freedom to store data where you want and analyze it when you need it
Modernise your Data Warehouse with Amazon Redshift and Amazon Redshift SpectrumAmazon Web Services
We will walk through how to migrate and modernise your legacy data warehouse, moving from an on-premises server or application, to the cloud. You will learn how to easily migrate your data by leveraging serverless ETL, data cataloging as well as the techniques needed to successfully modernise your data warehouse, reduce costs, and increase performance and scalability.
Speaker: Paul Macey, Specialist Solutions Architect, AWS
AWS March 2016 Webinar Series - Building Big Data Solutions with Amazon EMR a...Amazon Web Services
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 organizations.
Using a combination of Amazon EMR, a managed Hadoop framework, and Amazon Redshift, a managed petabyte-scale data warehouse, organizations can effectively address many of these requirements.
In this webinar, we will show how organizations 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 EMR, to address multiple data processing requirements. We will also share best practices and common use cases to integrate EMR and Redshift.
Learning Objectives:
• Best practices for building a big data architecture that includes Amazon EMR and Amazon Redshift
• Understand how to use technologies such as 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
Building a Modern Data Warehouse - Deep Dive on Amazon RedshiftAmazon Web Services
Osemeke Isibor, Solutions Architect, AWS
In this session, we take a deep dive on Amazon Redshift architecture and the latest performance enhancements that give you faster insights into your data. We also cover Redshift Spectrum, a feature of Redshift that enables you to analyze data across Redshift and your Amazon S3 data lake to deliver unique insights not possible by analyzing independent data silos.
Learn how Amazon Redshift, our fully managed, petabyte-scale data warehouse, can help you quickly and cost-effectively analyze all of your data using your existing business intelligence tools. Get an introduction to how Amazon Redshift uses massively parallel processing, scale-out architecture, and columnar direct-attached storage to minimize I/O time and maximize performance. Learn how you can gain deeper business insights and save money and time by migrating to Amazon Redshift. Take away strategies for migrating from on-premises data warehousing solutions, tuning schema and queries, and utilizing third party solutions.
Data Transformation Patterns in AWS - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Learn how to accelerate common data transformations from a variety of data
- Learn how to efficiently orchestrate transformation jobs
- Learn best practices and methodologies in data preparation for analytics
Saiba como Amazon Redshift, o nosso dataware house totalmente gerenciados, pode ajudá-lo de forma rápida e rentável analisar todos os seus dados utilizando suas ferramentas de BI. Também será abordado introdução ao serviço, o qual utiliza MPP, arquitetura scale-out e armazenamento de forma colunar.
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.
17. Amazon Kinesis: Streaming Data Done the AWS Way
Makes it easy to capture, deliver, and process real-time data streams
Pay as you go, no up-front costs
Elastically scalable
Right services for your specific use cases
Real-time latencies
Easy to provision, deploy, and manage
18. Streaming Data Scenarios Across Verticals
Scenarios/
Verticals
Accelerated Ingest-
Transform-Load
Continuous Metrics
Generation
Responsive Data Analysis
Digital Ad
Tech/Marketing
Publisher, bidder data
aggregation
Advertising metrics like
coverage, yield, and
conversion
User engagement with ads,
optimized bid/buy engines
IoT Sensor, device telemetry
data ingestion
Operational metrics and
dashboards
Device operational
intelligence and alerts
Gaming Online data aggregation,
e.g., top 10 players
Massively multiplayer online
game (MMOG) live
dashboard
Leader board generation,
player-skill match
Consumer
Online
Clickstream analytics Metrics like impressions and
page views
Recommendation engines,
proactive care
19. Amazon Kinesis Firehose
Load massive volumes of streaming data into Amazon S3, Redshift and Elasticsearch
• Zero administration: Capture and deliver streaming data into Amazon S3, Amazon Redshift, and other destinations
without writing an application or managing infrastructure.
• Direct-to-data store integration: Batch, compress, and encrypt streaming data for delivery into data destinations in
as little as 60 secs using simple configurations.
• Seamless elasticity: Seamlessly scales to match data throughput w/o intervention
• Serverless ETL using AWS Lambda - Firehose can invoke your Lambda function to transform incoming source data.
Capture and submit streaming
data
Analyze streaming data using your
favorite BI tools
Firehose loads streaming data continuously
into Amazon S3, Redshift and Elasticsearch
20. AWS Platform SDKs Mobile SDKs Kinesis Agent AWS IoT
Amazon S3 Amazon Redshift
• Send data from IT infra, mobile devices, sensors
• Integrated with AWS SDK, Agents, and AWS IoT
• Fully-managed service to capture streaming data
• Elastic w/o resource provisioning
• Pay-as-you-go: 3.5 cents/ GB transferred
• Batch, compress, and encrypt data before loads
• Loads data into Redshift tables base on COPY
command
Amazon Kinesis Firehose
Capture IT & App Logs, Device & Sensor Data, and more
Enable near-real time analytics using existing tools
24. Amazon Kinesis Data Firehose vs.
Amazon Kinesis Data Streams
Amazon Kinesis Data Streams is for use cases that require custom
processing, per incoming record, with sub-1 second processing latency, and
a choice of stream processing frameworks.
Amazon Kinesis Data Firehose is for use cases that require zero
administration, ability to use existing analytics tools based on Amazon S3,
Amazon Redshift and Amazon Elasticsearch, and a data latency of 60
seconds or higher.