by Jeff Duffy, Database Specialist Solutions Architect, AWS
Database Week at the AWS Loft is an opportunity to learn about Amazon’s broad and deep family of managed database 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 RDS and Amazon Aurora relational databases, Amazon DynamoDB non-relational databases, Amazon Neptune graph databases, and Amazon ElastiCache managed Redis, along with options for database migration, caching, search and more. You'll will learn how to get started, how to support applications, and how to scale.
by Karthik Kumar Odapally, Solutions Architect, AWS
Database Week at the AWS Loft is an opportunity to learn about Amazon’s broad and deep family of managed database 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 RDS and Amazon Aurora relational databases, Amazon DynamoDB non-relational databases, Amazon Neptune graph databases, and Amazon ElastiCache managed Redis, along with options for database migration, caching, search and more. You'll will learn how to get started, how to support applications, and how to scale.
by Joseph Idziorek, Sr. Product Manager, AWS
Database Week at the AWS Loft is an opportunity to learn about Amazon’s broad and deep family of managed database 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 RDS and Amazon Aurora relational databases, Amazon DynamoDB non-relational databases, Amazon Neptune graph databases, and Amazon ElastiCache managed Redis, along with options for database migration, caching, search and more. You'll will learn how to get started, how to support applications, and how to scale.
by Rajeev Srinivasan, Strategic Solutions Architect, AWS
Database Week at the AWS Loft is an opportunity to learn about Amazon’s broad and deep family of managed database 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 RDS and Amazon Aurora relational databases, Amazon DynamoDB non-relational databases, Amazon Neptune graph databases, and Amazon ElastiCache managed Redis, along with options for database migration, caching, search and more. You'll will learn how to get started, how to support applications, and how to scale.
by Rich Alberth, Solutions Architect, AWS
If you need to query relationships between data, you need a graph database. We’ll take a close look at Amazon Neptune, explore the differences between property graphs and RDF, then do graph data queries using Apache Tinkerpop. You’ll need a laptop with a Firefox or Chrome browser.
Amazon Elasticsearch Service Deep Dive - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Learn how to configure a secure, petabyte-scale Amazon ES cluster and ingest data into it
- Learn how to build Kibana dashboards to analyze and visualize your data in Amazon ES
- Take away best practices to make your cluster reliable, take backups, and debug slow-running queries and indexing operations
by Mikhail Prudnikov, Sr. Solutions Architect, AWS
Elasticsearch is a popular open-source distributed search and analytics engine, widely used for log analytics and text search – and increasingly used as a primary data store. Amazon Elasticsearch Service makes it easy to deploy, secure, operate, and scale Elasticsearch. We’ll take a look at how to use Elasticsearch Service to manage these different use cases.
by Mikhail Prudnikov, Sr. Solutions Architect, AWS
In-memory data stores, such as ElastiCache for Redis, enable applications where response times are measured in microseconds. We’ll look at how to design and deploy high-performance applications using ElastiCache, Aurora, DynamoDB, DAX, and Lambda, then we’ll do a hands-on lab to do it ourselves. You’ll need a laptop with a Firefox or Chrome browser.
by J. Bako, Solutions Architect, AWS
Graph databases are purpose-built to store and navigate relationships. They have advantages for many use cases: social networking, recommendation engines, fraud detection, and others where you need to create relationships between data and quickly query these relationships. Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. We’ll discuss when you should use a graph database and look at how to use Neptune.
by Karthik Kumar Odapally, Solutions Architect, AWS
Database Week at the AWS Loft is an opportunity to learn about Amazon’s broad and deep family of managed database 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 RDS and Amazon Aurora relational databases, Amazon DynamoDB non-relational databases, Amazon Neptune graph databases, and Amazon ElastiCache managed Redis, along with options for database migration, caching, search and more. You'll will learn how to get started, how to support applications, and how to scale.
by Joseph Idziorek, Sr. Product Manager, AWS
Database Week at the AWS Loft is an opportunity to learn about Amazon’s broad and deep family of managed database 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 RDS and Amazon Aurora relational databases, Amazon DynamoDB non-relational databases, Amazon Neptune graph databases, and Amazon ElastiCache managed Redis, along with options for database migration, caching, search and more. You'll will learn how to get started, how to support applications, and how to scale.
by Rajeev Srinivasan, Strategic Solutions Architect, AWS
Database Week at the AWS Loft is an opportunity to learn about Amazon’s broad and deep family of managed database 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 RDS and Amazon Aurora relational databases, Amazon DynamoDB non-relational databases, Amazon Neptune graph databases, and Amazon ElastiCache managed Redis, along with options for database migration, caching, search and more. You'll will learn how to get started, how to support applications, and how to scale.
by Rich Alberth, Solutions Architect, AWS
If you need to query relationships between data, you need a graph database. We’ll take a close look at Amazon Neptune, explore the differences between property graphs and RDF, then do graph data queries using Apache Tinkerpop. You’ll need a laptop with a Firefox or Chrome browser.
Amazon Elasticsearch Service Deep Dive - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Learn how to configure a secure, petabyte-scale Amazon ES cluster and ingest data into it
- Learn how to build Kibana dashboards to analyze and visualize your data in Amazon ES
- Take away best practices to make your cluster reliable, take backups, and debug slow-running queries and indexing operations
by Mikhail Prudnikov, Sr. Solutions Architect, AWS
Elasticsearch is a popular open-source distributed search and analytics engine, widely used for log analytics and text search – and increasingly used as a primary data store. Amazon Elasticsearch Service makes it easy to deploy, secure, operate, and scale Elasticsearch. We’ll take a look at how to use Elasticsearch Service to manage these different use cases.
by Mikhail Prudnikov, Sr. Solutions Architect, AWS
In-memory data stores, such as ElastiCache for Redis, enable applications where response times are measured in microseconds. We’ll look at how to design and deploy high-performance applications using ElastiCache, Aurora, DynamoDB, DAX, and Lambda, then we’ll do a hands-on lab to do it ourselves. You’ll need a laptop with a Firefox or Chrome browser.
by J. Bako, Solutions Architect, AWS
Graph databases are purpose-built to store and navigate relationships. They have advantages for many use cases: social networking, recommendation engines, fraud detection, and others where you need to create relationships between data and quickly query these relationships. Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. We’ll discuss when you should use a graph database and look at how to use Neptune.
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.
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
Search Your DynamoDB Data with Amazon Elasticsearch Service (ANT302) - AWS re...Amazon Web Services
Both Amazon DynamoDB and Amazon ES are database technologies. Their strengths are different and complementary. DynamoDB is an excellent, durable store, providing high throughput at reliable latencies with nearly infinite scale. Elasticsearch provides a rich query API, supporting high throughput, low-latency search across numeric and string data and with a built-in capability of bringing relevant results for your queries. In this lab, we explore the joint power of these technologies. You deploy a DynamoDB table, bootstrap it with data, then using Dynamo Streams, replicate that bootstrapped data to Amazon ES. You use Elasticsearch's query language to query your data directly. Finally, you send updates to your DynamoDB table and use Elasticsearch analytics capabilities to monitor changes occurring in your table.
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.
by Joe Idziorek, Sr. Product Manager, AWS
Database Week at the AWS Loft is an opportunity to learn about Amazon’s broad and deep family of managed database 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 RDS and Amazon Aurora relational databases, Amazon DynamoDB non-relational databases, Amazon Neptune graph databases, and Amazon ElastiCache managed Redis, along with options for database migration, caching, search and more. You'll will learn how to get started, how to support applications, and how to scale.
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.
Working with Scalable Machine Learning Algorithms in Amazon SageMaker - AWS O...Amazon Web Services
Learning Objectives:
- Become aquainted with the popular algorithms provided with Amazon SageMaker
- Learn how to use algorithms for training in Amazon SageMaker
- Learn how the algorithms in Amazon SageMaker were architected to be faster and more efficient by design
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
by Rajeev Srinivasan, Sr. Solutions Architect and Gautam Srinivasan, Solutions Architect, AWS
While a Data Lake can support completely unstructured data, getting performant analytics at scale requires some data preparation. We'll look at how to use Amazon Kinesis, AWS Glue, and Amazon EMR to make raw data ready to high-performance analytics.
by Rich Alberth, Solutions Architect, AWS
Modernizing your database environment can bring many benefits, from avoiding technical debt to reducing expenses. AWS Database Migration Service enables easy modernization, enabling you to easily change database versions (and even database engines) and schema topologies while avoiding downtimes. We’ll look at some models for modernization, then do a hands-on exercise to migrate and consolidate MySQL databases to Amazon Aurora. You’ll need a laptop with a Firefox or Chrome browser.
AWS provides a wide set of services to manage your data, which allow our customers to choose the right tool to the right workload. Learn how to make your databases up to 10x faster and less expensive with Amazon ElastiCache for Redis and utilize DynamoDB Accelerator (DAX) to access your data on DynamoDB faster with no additional development efforts. If you need fast access to your data, these services might be the right services for your workload.
by Ben Willett, Solutions Architect, AWS
Organizations use reports, dashboards, and analytics tools to extract insights from their data, monitor performance, and support decision making. To support these tools, data must be collected and prepared for use. We'll look at two approaches: a structured centralized data repository as a Data Warehouse the less-structured repository of a Data Lake. We'll compare these approaches, examine the services that support each, and explore how they work together.
Building a Messaging Application with Redis Streams (DAT353) - AWS re:Invent ...Amazon Web Services
Redis Streams is a new data type introduced in Redis 5.0 for message publishing and subscription. It differs from existing pub/sub components as it provides message persistence and primary/secondary data replication. Attend this session to learn about Redis Streams and discover how you can use it to build better messaging applications, such as chat, queues, and IoT.
Organizations need to gain insight and knowledge from a growing number of Internet of Things (IoT), APIs, clickstreams, unstructured and log data sources. However, organizations are also often limited by legacy data warehouses and ETL processes that were designed for transactional data. In this session, we introduce key ETL features of AWS Glue, cover common use cases ranging from scheduled nightly data warehouse loads to near real-time, event-driven ETL flows for your data lake. We discuss how to build scalable, efficient, and serverless ETL pipelines using AWS Glue. Additionally, Merck will share how they built an end-to-end ETL pipeline for their application release management system, and launched it in production in less than a week using AWS Glue.
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.
by Sid Chauhan, Solutions architect, AWS
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
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
How to build a data lake with aws glue data catalog (ABD213-R) re:Invent 2017Amazon Web Services
As data volumes grow and customers store more data on AWS, they often have valuable data that is not easily discoverable and available for analytics. The AWS Glue Data Catalog provides a central view of your data lake, making data readily available for analytics. We introduce key features of the AWS Glue Data Catalog and its use cases. Learn how crawlers can automatically discover your data, extract relevant metadata, and add it as table definitions to the AWS Glue Data Catalog. We will also explore the integration between AWS Glue Data Catalog and Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum.
by Andre Hass, Specialist Technical Account Manager, AWS
Organizations use reports, dashboards, and analytics tools to extract insights from their data, monitor performance, and support decision making. To support these tools, data must be collected and prepared for use. We'll look at two approaches: a structured centralized data repository as a Data Warehouse the less-structured repository of a Data Lake. We'll compare these approaches, examine the services that support each, and explore how they work together.
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.
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.
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
Search Your DynamoDB Data with Amazon Elasticsearch Service (ANT302) - AWS re...Amazon Web Services
Both Amazon DynamoDB and Amazon ES are database technologies. Their strengths are different and complementary. DynamoDB is an excellent, durable store, providing high throughput at reliable latencies with nearly infinite scale. Elasticsearch provides a rich query API, supporting high throughput, low-latency search across numeric and string data and with a built-in capability of bringing relevant results for your queries. In this lab, we explore the joint power of these technologies. You deploy a DynamoDB table, bootstrap it with data, then using Dynamo Streams, replicate that bootstrapped data to Amazon ES. You use Elasticsearch's query language to query your data directly. Finally, you send updates to your DynamoDB table and use Elasticsearch analytics capabilities to monitor changes occurring in your table.
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.
by Joe Idziorek, Sr. Product Manager, AWS
Database Week at the AWS Loft is an opportunity to learn about Amazon’s broad and deep family of managed database 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 RDS and Amazon Aurora relational databases, Amazon DynamoDB non-relational databases, Amazon Neptune graph databases, and Amazon ElastiCache managed Redis, along with options for database migration, caching, search and more. You'll will learn how to get started, how to support applications, and how to scale.
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.
Working with Scalable Machine Learning Algorithms in Amazon SageMaker - AWS O...Amazon Web Services
Learning Objectives:
- Become aquainted with the popular algorithms provided with Amazon SageMaker
- Learn how to use algorithms for training in Amazon SageMaker
- Learn how the algorithms in Amazon SageMaker were architected to be faster and more efficient by design
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
by Rajeev Srinivasan, Sr. Solutions Architect and Gautam Srinivasan, Solutions Architect, AWS
While a Data Lake can support completely unstructured data, getting performant analytics at scale requires some data preparation. We'll look at how to use Amazon Kinesis, AWS Glue, and Amazon EMR to make raw data ready to high-performance analytics.
by Rich Alberth, Solutions Architect, AWS
Modernizing your database environment can bring many benefits, from avoiding technical debt to reducing expenses. AWS Database Migration Service enables easy modernization, enabling you to easily change database versions (and even database engines) and schema topologies while avoiding downtimes. We’ll look at some models for modernization, then do a hands-on exercise to migrate and consolidate MySQL databases to Amazon Aurora. You’ll need a laptop with a Firefox or Chrome browser.
AWS provides a wide set of services to manage your data, which allow our customers to choose the right tool to the right workload. Learn how to make your databases up to 10x faster and less expensive with Amazon ElastiCache for Redis and utilize DynamoDB Accelerator (DAX) to access your data on DynamoDB faster with no additional development efforts. If you need fast access to your data, these services might be the right services for your workload.
by Ben Willett, Solutions Architect, AWS
Organizations use reports, dashboards, and analytics tools to extract insights from their data, monitor performance, and support decision making. To support these tools, data must be collected and prepared for use. We'll look at two approaches: a structured centralized data repository as a Data Warehouse the less-structured repository of a Data Lake. We'll compare these approaches, examine the services that support each, and explore how they work together.
Building a Messaging Application with Redis Streams (DAT353) - AWS re:Invent ...Amazon Web Services
Redis Streams is a new data type introduced in Redis 5.0 for message publishing and subscription. It differs from existing pub/sub components as it provides message persistence and primary/secondary data replication. Attend this session to learn about Redis Streams and discover how you can use it to build better messaging applications, such as chat, queues, and IoT.
Organizations need to gain insight and knowledge from a growing number of Internet of Things (IoT), APIs, clickstreams, unstructured and log data sources. However, organizations are also often limited by legacy data warehouses and ETL processes that were designed for transactional data. In this session, we introduce key ETL features of AWS Glue, cover common use cases ranging from scheduled nightly data warehouse loads to near real-time, event-driven ETL flows for your data lake. We discuss how to build scalable, efficient, and serverless ETL pipelines using AWS Glue. Additionally, Merck will share how they built an end-to-end ETL pipeline for their application release management system, and launched it in production in less than a week using AWS Glue.
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.
by Sid Chauhan, Solutions architect, AWS
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
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
How to build a data lake with aws glue data catalog (ABD213-R) re:Invent 2017Amazon Web Services
As data volumes grow and customers store more data on AWS, they often have valuable data that is not easily discoverable and available for analytics. The AWS Glue Data Catalog provides a central view of your data lake, making data readily available for analytics. We introduce key features of the AWS Glue Data Catalog and its use cases. Learn how crawlers can automatically discover your data, extract relevant metadata, and add it as table definitions to the AWS Glue Data Catalog. We will also explore the integration between AWS Glue Data Catalog and Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum.
by Andre Hass, Specialist Technical Account Manager, AWS
Organizations use reports, dashboards, and analytics tools to extract insights from their data, monitor performance, and support decision making. To support these tools, data must be collected and prepared for use. We'll look at two approaches: a structured centralized data repository as a Data Warehouse the less-structured repository of a Data Lake. We'll compare these approaches, examine the services that support each, and explore how they work together.
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.
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.
Scaling Redis Workloads with Amazon ElastiCache - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Learn how to horizontally scale Redis clusters within ElastiCache
- Learn about features to secure data in ElastiCache for Redis
- Learn about ElastiCache for Redis use cases to speed up real-time applications in web, gaming, ad-tech, media
In-memory services, such as Amazon ElastiCache for Redis, provide a number of tools to accelerate application and database performance. In this workshop, we explore these tools and features and dive deep on Redis, the ElastiCache API, and visit the common architecture patterns for which we see customers using in-memory services. Participants gain hands-on experience launching a Redis cluster through AWS CloudFormation, updating the cluster through the AWS Command Line Interface, and working with Redis data structures.
In this session, we provide a peek behind the scenes to learn about Amazon ElastiCache's design and architecture. See common design patterns with our Redis and Memcached offerings and how customers have used them for in-memory operations to reduce latency and improve application throughput. During this session, we review ElastiCache best practices, design patterns, and anti-patterns.
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.
ElastiCache: Deep Dive Best Practices and Usage Patterns - AWS Online Tech TalksAmazon Web Services
"Learning Objectives:
- What is Redis and why do you need it
- Get an inside look at Amazon ElastiCache for Redis design and architecture
- Hear about common usage patterns"
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.
Speaker: Samir Karande - Sr. Manager, Solutions Architecture, AWS
Database Week at the San Francisco Loft: ElastiCache & Redis
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.
Speaker: Ben Willett - Sr. Solutions Architect, AWS
Database Week at the San Francisco Loft
ElastiCache & Redis
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.
Speakers:
Smitty Weygant - Solutions Architect, AWS
ElastiCache Deep Dive: Best Practices and Usage Patterns - March 2017 AWS Onl...Amazon Web Services
Amazon ElastiCache is a web service that makes it easy to deploy, operate, and scale an in-memory data store or cache in the cloud. The service improves the performance of web applications by allowing you to retrieve information from fast, managed, in-memory data stores, instead of relying entirely on slower disk-based databases. In this tech talk, we’ll provide a peek behind the scenes to learn about Amazon ElastiCache's design and architecture. You’ll see common design patterns with our Redis and Memcached offerings and how customers have used them for in-memory operations to reduce latency and improve application throughput. During this session, we review ElastiCache best practices, design patterns, and anti-patterns.
Learning Objectives:
- Learn how to integrate Amazon ElastiCache in your workloads
- Understand the benefits of an In-Memory data store
- Learn how to apply various caching strategies in your applications
- Hands on demonstration using Amazon ElastiCache
Amazon Elasticache Deep Dive - March 2017 AWS Online Tech TalksAmazon Web Services
Amazon ElastiCache is a web service that makes it easy to deploy, operate, and scale an in-memory data store or cache in the cloud. The service improves the performance of web applications by allowing you to retrieve information from fast, managed, in-memory data stores, instead of relying entirely on slower disk-based databases. In this tech talk, we’ll provide a peek behind the scenes to learn about Amazon ElastiCache's design and architecture. You’ll see common design patterns with our Redis and Memcached offerings and how customers have used them for in-memory operations to reduce latency and improve application throughput. During this session, we review ElastiCache best practices, design patterns, and anti-patterns.
Learning Objectives:
- Learn how to integrate Amazon ElastiCache in your workloads
- Understand the benefits of an In-Memory data store
- Learn how to apply various caching strategies in your applications
- Hands on demonstration using Amazon ElastiCache
Migrate Your Hadoop/Spark Workload to Amazon EMR and Architect It for Securit...Amazon Web Services
"Customers are migrating their analytics, data processing (ETL), and data science workloads running on Apache Hadoop/Spark to AWS in order to save costs, increase availability, and improve performance. In this session, AWS customers Airbnb and Guardian Life discuss how they migrated their workload to Amazon EMR. This session focuses on key motivations to move to the cloud. It details key architectural changes and the benefits of migrating Hadoop/Spark workloads to the cloud.
"
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)
Black Belt Tips for IT Operations - AWS Summit Sydney 2018Amazon Web Services
Black Belt Tips for IT Operations
Black Belt tips makes a comeback for the fifth consecutive year. How you used the AWS platform last year should be different to how you utilise it today. In this advanced technical session, learn about latest changes in compute, networking, and security. Understand how new architecture patterns will allow you to maximise performance efficiency, reliability, and security on AWS today.
Evgeny Vaganov, Solutions Architect, Amazon Web Services
ElastiCache Deep Dive: Design Patterns for In-Memory Data Stores (DAT302-R1) ...Amazon Web Services
In this session, we provide a behind the scenes peek to learn about the design and architecture of Amazon ElastiCache. See common design patterns with our Redis and Memcached offerings and how customers use them for in-memory data processing to reduce latency and improve application throughput. We review ElastiCache best practices, design patterns, and anti-patterns.
Join this session for a deep dive into cloudscale network patterns with Nicolas Meessen, Senior Principal Network Engineer, Atlassian. Learn how to build any-to-any connectivity between over 40 VPCs in a region, deploy any-casted private services inside of AWS, and create private connectivity between DX-connected regions. If you are a network administrator or systems architect then this is the perfect session for you.
Speaker: Nicolas Meessen, Senior Principal Network Engineer, Atlassian
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.