The document discusses various data design strategies and challenges for microservices architectures on AWS. It recommends using polyglot persistence to allow each microservice to choose its own database technology. It also discusses using eventual consistency between services, implementing transaction rollback mechanisms, and aggregating data from multiple services for reporting and analytics purposes.
DynamoDB Accelerator (DAX) is a fully managed caching layer for DynamoDB that provides microsecond latency, millions of requests per second of throughput, and automatic hot key management to improve application performance. DAX caches both individual items and entire queries from DynamoDB to provide single-digit millisecond responses while handling all cache management and high availability.
AWS Data Services provide a suite of serverless analytics tools including Amazon Athena for interactive SQL queries, AWS Glue for ETL and data cataloging, and Amazon S3 for exabyte-scale data storage. Together these services enable building a data lake architecture for ingesting, storing, discovering, and analyzing all types of data at scale.
Building Data Warehouses and Data Lakes in the Cloud - DevDay Austin 2017 Day 2Amazon Web Services
This document discusses building data warehouses and data lakes in the cloud using AWS services. It provides an overview of AWS databases, analytics, and machine learning services that can be used to store and analyze data at scale. These services allow customers to migrate existing data warehouses to the cloud, build new data warehouses and data lakes more cost effectively, and gain insights from their data more easily.
Databases in the Cloud discusses AWS database services for moving workloads to the cloud. It describes Amazon Relational Database Service (RDS) which provides several fully managed relational database options including MySQL, PostgreSQL, MariaDB, Oracle, SQL Server, and Amazon Aurora. It also discusses non-relational database services like DynamoDB, ElastiCache, and Redshift for analytics workloads. The document provides guidance on choosing between SQL and NoSQL databases and discusses benefits of managed database services over hosting databases on-premises or in EC2 instances.
Choosing the Right Database for the Job: Relational, Cache, or NoSQL?Amazon Web Services
Developers and DBAs from a traditional relational background are spoilt for choice when looking to integrate caching and NoSQL into an application architecture to solve scaling problems and reduce costs. Even when using relational databases there are 3 managed database services on AWS for the MySQL engine alone. Trying to evaluate all the options often creates analysis paralysis, resulting in a reluctance to try something new or different. This session will guide you through a series of use cases that use different databases to solve business problems that customers face today.
AWS re:Invent 2016: Migrating a Highly Available and Scalable Database from O...Amazon Web Services
In this session, we share how an Amazon.com team that owns a document management platform that manages billions of critical customer documents for Amazon.com migrated from a relational to a non-relational database. Initially, the service was built as an Oracle database. As it grew, the team discovered the limits of the relational model and decided to migrate to a non-relational database. They chose Amazon DynamoDB for its built-in resilience, scalability, and predictability. We provide a template that you can use to migrate from a relational data store to DynamoDB. We also provide details about the entire process: design patterns for moving from a SQL schema to a NoSQL schema; mechanisms used to transition from an ACID (Atomicity, Consistency, Isolation, Durability) model to an eventually consistent model; migration alternatives considered; pitfalls in common migration strategies; and how to ensure service availability and consistency during migration.
This document discusses building a data lake on AWS. It describes using Amazon S3 for storage, Amazon Kinesis for streaming data, and AWS Lambda to populate metadata indexes in DynamoDB and search indexes. It covers using IAM for access control, AWS STS for temporary credentials, and API Gateway and Elastic Beanstalk for interfaces. The data lake provides a foundation for storing and analyzing structured, semi-structured, and unstructured data at scale from various sources in a cost-effective and secure manner.
AWS re:Invent 2016: Streaming ETL for RDS and DynamoDB (DAT315)Amazon Web Services
During this session Greg Brandt and Liyin Tang, Data Infrastructure engineers from Airbnb, will discuss the design and architecture of Airbnb's streaming ETL infrastructure, which exports data from RDS for MySQL and DynamoDB into Airbnb's data warehouse, using a system called SpinalTap. We will also discuss how we leverage Spark Streaming to compute derived data from tracking topics and/or database tables, and HBase to provide immediate data access and generate cleanly time-partitioned Hive tables.
DynamoDB Accelerator (DAX) is a fully managed caching layer for DynamoDB that provides microsecond latency, millions of requests per second of throughput, and automatic hot key management to improve application performance. DAX caches both individual items and entire queries from DynamoDB to provide single-digit millisecond responses while handling all cache management and high availability.
AWS Data Services provide a suite of serverless analytics tools including Amazon Athena for interactive SQL queries, AWS Glue for ETL and data cataloging, and Amazon S3 for exabyte-scale data storage. Together these services enable building a data lake architecture for ingesting, storing, discovering, and analyzing all types of data at scale.
Building Data Warehouses and Data Lakes in the Cloud - DevDay Austin 2017 Day 2Amazon Web Services
This document discusses building data warehouses and data lakes in the cloud using AWS services. It provides an overview of AWS databases, analytics, and machine learning services that can be used to store and analyze data at scale. These services allow customers to migrate existing data warehouses to the cloud, build new data warehouses and data lakes more cost effectively, and gain insights from their data more easily.
Databases in the Cloud discusses AWS database services for moving workloads to the cloud. It describes Amazon Relational Database Service (RDS) which provides several fully managed relational database options including MySQL, PostgreSQL, MariaDB, Oracle, SQL Server, and Amazon Aurora. It also discusses non-relational database services like DynamoDB, ElastiCache, and Redshift for analytics workloads. The document provides guidance on choosing between SQL and NoSQL databases and discusses benefits of managed database services over hosting databases on-premises or in EC2 instances.
Choosing the Right Database for the Job: Relational, Cache, or NoSQL?Amazon Web Services
Developers and DBAs from a traditional relational background are spoilt for choice when looking to integrate caching and NoSQL into an application architecture to solve scaling problems and reduce costs. Even when using relational databases there are 3 managed database services on AWS for the MySQL engine alone. Trying to evaluate all the options often creates analysis paralysis, resulting in a reluctance to try something new or different. This session will guide you through a series of use cases that use different databases to solve business problems that customers face today.
AWS re:Invent 2016: Migrating a Highly Available and Scalable Database from O...Amazon Web Services
In this session, we share how an Amazon.com team that owns a document management platform that manages billions of critical customer documents for Amazon.com migrated from a relational to a non-relational database. Initially, the service was built as an Oracle database. As it grew, the team discovered the limits of the relational model and decided to migrate to a non-relational database. They chose Amazon DynamoDB for its built-in resilience, scalability, and predictability. We provide a template that you can use to migrate from a relational data store to DynamoDB. We also provide details about the entire process: design patterns for moving from a SQL schema to a NoSQL schema; mechanisms used to transition from an ACID (Atomicity, Consistency, Isolation, Durability) model to an eventually consistent model; migration alternatives considered; pitfalls in common migration strategies; and how to ensure service availability and consistency during migration.
This document discusses building a data lake on AWS. It describes using Amazon S3 for storage, Amazon Kinesis for streaming data, and AWS Lambda to populate metadata indexes in DynamoDB and search indexes. It covers using IAM for access control, AWS STS for temporary credentials, and API Gateway and Elastic Beanstalk for interfaces. The data lake provides a foundation for storing and analyzing structured, semi-structured, and unstructured data at scale from various sources in a cost-effective and secure manner.
AWS re:Invent 2016: Streaming ETL for RDS and DynamoDB (DAT315)Amazon Web Services
During this session Greg Brandt and Liyin Tang, Data Infrastructure engineers from Airbnb, will discuss the design and architecture of Airbnb's streaming ETL infrastructure, which exports data from RDS for MySQL and DynamoDB into Airbnb's data warehouse, using a system called SpinalTap. We will also discuss how we leverage Spark Streaming to compute derived data from tracking topics and/or database tables, and HBase to provide immediate data access and generate cleanly time-partitioned Hive tables.
Co 4, session 2, aws analytics servicesm vaishnavi
AWS offers several analytics services to help process and provide insights from data. These include Amazon Athena for interactive querying of data stored in S3 using SQL, Amazon EMR for processing large amounts of data using Hadoop and other open source tools, Amazon CloudSearch for setting up a search solution easily, and Amazon Kinesis for collecting, processing, and analyzing real-time data. Other services are Amazon Redshift for data warehousing, Amazon Quicksight for interactive dashboards, AWS Glue for ETL jobs, and Amazon Lake Formation for securing data lakes.
We will introduce key concepts for a data lake and present aspects related to its implementation. Also discussing critical success factors, pitfalls to avoid operational aspects, and insights on how AWS enables a server-less data lake architecture.
Speaker: Sebastien Menant, Solutions Architect, Amazon Web Services
With distributed frameworks like Hadoop and Kafka, it is essential to deploy the right environment to successfully support these workloads. Learn about the different block storage options from AWS and walk through with our experts on how to select the best option for your big data analytic workloads. We will demonstrate how to setup, select, and modify volume types to right size your environment needs.
This document discusses best practices for building a data lake architecture on AWS. It recommends using Amazon S3 as the centralized data lake storage and decoupling storage from compute. This allows for cheaper, more efficient operation and the ability to evolve to clusterless analytics tools like Amazon Athena. The document provides guidance on security, ingestion, cataloging, cost optimization, analytics tools and building a sample pipeline to analyze data in the lake.
(BDT308) Using Amazon Elastic MapReduce as Your Scalable Data Warehouse | AWS...Amazon Web Services
In this presentation, we will demonstrate how to use Amazon Elastic MapReduce as your scalable data warehouse. Amazon EMR supports clusters with thousands of nodes and is used to access petabyte scale data warehouses. Amazon EMR is not only fast, but it is also easy to use for rapid development and adhoc analysis. We will show you how access the large scale data warehouses with emerging tools such as Hue, Hive, low latency SQL applications like Presto, and alternative execution engines like Apache Spark. We will also show you how these tools integrate directly with other AWS big data services such as Amazon S3, Amazon DynamoDB, and Amazon Kinesis.
This document summarizes key aspects of full stack analytics on AWS, including foundational services like storage, data ingestion, processing and analytics, machine learning, and security. It discusses AWS services like S3, Athena, Glue, Kinesis, Rekognition, and how they can be used together for cost-effective analytics from ingestion to machine learning to building smarter applications. Security is addressed at both the service and data levels using tools like IAM, encryption, and third party integration.
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
Today organizations find themselves in a data rich world with a growing need for increased agility and accessibility of all this data for analysis and deriving keen insights to drive strategic decisions. Creating a data lake helps you to manage all the disparate sources of data you are collecting, in its original format and extract value. In this session learn how to architect and implement an Analytics Data Lake. Hear customer examples of best practices and learn from their architectural blueprints.
Data collection and storage is a primary challenge for any big data architecture. In this session, we will describe the different types of data that customers are handling to drive high-scale workloads on AWS, and help you choose the best approach for your workload. We will cover optimization techniques that improve performance and reduce the cost of data ingestion.AWS services to be covered include: Amazon S3, DynamoDB, and Kinesis.
re:Invent Round-up, Time Stream, Quantum and Managed Blockchain Amazon Web Services
This document provides an overview of Amazon Web Services database services, including Amazon DynamoDB, Amazon RDS, Amazon ElastiCache, Amazon Neptune, and Amazon QLDB. It discusses the different types of databases, common use cases, and new features like Amazon Managed Blockchain and Amazon Timestream. The pricing examples show how Amazon QLDB and Amazon Managed Blockchain can provide ledger databases and blockchain networks at lower costs than traditional options.
By using a Data Lake, you no longer need to worry about structuring or transforming data before storing it. A Data Lake on AWS enables your organization to more rapidly analyze data, helping you quickly discover new business insights. Join us for our webinar to learn about the benefits of building a Data Lake on AWS and how your organization can begin reaping their rewards. In this session, we will share methodology for implementing a Data Lake on AWS and best practices for getting the most from your Data Lake.
Speaker: Russell Nash,
APAC Solution Architect, DW, AWS APAC
This document provides an agenda and overview for a workshop on building a data lake on AWS. The agenda includes reviewing data lakes, modernizing data warehouses with Amazon Redshift, data processing with Amazon EMR, and event-driven processing with AWS Lambda. It discusses how data lakes extend traditional data warehousing approaches and how services like Redshift, EMR, and Lambda can be used for analytics in a data lake on AWS.
The benefits of running databases in the cloud are compelling but how do you get the data there? In this session we will explore how to use the AWS Database Migration Service and the AWS Schema Conversion Tool to help you to migrate, or continuously replicate, your on-premise databases to AWS.
Speaker: Jarrod Spiga, Solutions Architect, Amazon Web Services
Building Serverless Web Applications - DevDay Los Angeles 2017Amazon Web Services
The document provides information about building serverless web applications using AWS Lambda and other AWS services. It begins with an overview of serverless computing using AWS Lambda and how it avoids the need to provision and manage servers. It then discusses various AWS compute offerings and when to use EC2, ECS, or Lambda. The rest of the document discusses serverless design patterns, demonstrates building a serverless web application using services like API Gateway and DynamoDB, and how to define and manage serverless applications using the AWS Serverless Application Model (SAM).
Logging infrastructure for Microservices using StreamSets Data CollectorCask Data
This document discusses using StreamSets Data Collector (SDC) to build a logging infrastructure for microservices. SDC can ingest logs from microservices running in containers and handle issues like schema changes and new log formats. It processes and transforms the logs, sending them to destinations like Kafka. SDC pipelines can run on Spark clusters on Yarn and Mesos to handle large volumes of log data and load it into systems like HDFS, HBase and Elasticsearch for analysis.
The document provides an overview of Amazon Web Services (AWS) databases and analytics services. It summarizes that AWS has significantly expanded its database and analytics offerings between 2015-2018, with over 750 new features and 10 new services launched. It highlights several core AWS database and analytics services, including Amazon DynamoDB, Amazon RDS, Amazon Aurora, Amazon Neptune, and Amazon ElastiCache. It also discusses how customers are migrating workloads from on-premises databases to AWS databases and analytics services.
Modernize Legacy and Enterprise Application Through Implementation of Cloud N...Amazon Web Services
Many Federal agencies are taking on initiatives to consolidate datacenters, modernize legacy and enterprise applications, and transform the digital portfolio to enable agility and other advantageous through cloud based delivery models. Deloitte provides a breadth of services to help federal government agencies select the right cloud solutions to accelerate their missions and derive value while helping agencies to be at the forefront of technology and innovation. Join us as we demonstrate specific client use cases where we have successfully assisted clients in selecting a cloud service model for migrating and transforming current data center to the cloud, enabling agility by taking advantage of Agile, DevOps, and continuous delivery, and modernized legacy and enterprise application through the implementation of cloud native solutions on AWS infrastructure to deliver value at the speed of your mission. Learn More: https://aws.amazon.com/government-education/
This document provides an overview of Amazon Web Services storage options for big data and analytics workloads. It discusses Amazon S3, Amazon EBS volume types, use cases for different storage solutions, examples of customers optimizing storage, and a new feature called EBS Elastic Volumes that allows modifying the configuration of live EBS volumes non-disruptively.
AWS March 2016 Webinar Series Building Your Data Lake on AWS Amazon Web Services
Uncovering new, valuable insights from big data requires organizations to collect, store, and analyze increasing volumes of data from multiple, often disparate sources at disparate points in time. This makes it difficult to handle big data with data warehouses or relational database management systems alone.
A Data Lake allows you to store massive amounts of data in its original form, without the need to enforce a predefined schema, enabling a far more agile and flexible architecture, which makes it easier to gain new types of analytical insights from your data
In this webinar, we will introduce key concepts of a Data Lake and present aspects related to its implementation. We will discuss critical success factors, pitfalls to avoid as well as operational aspects such as security, governance, search, indexing and metadata management.
Learning Objectives:
• Learn how AWS can help enable a Data Lake architecture
• Understand some of the key architectural considerations when building a Data Lake
• Hear some of the important Data Lake implementation considerations
Who Should Attend:
• Data architects, data scientists, advanced AWS developers
The document discusses challenges and best practices for data architecture in microservices environments. It covers issues like distributed transactions, eventual consistency, and error handling. It also provides recommendations for choosing data stores and keeping data consistent across services through master data management. The key aspects are using correlation IDs, designing services to handle their own rollbacks, leveraging streams for async operations, and classifying requirements to select the right database technologies.
Database Week at the San Francisco Loft: Microservices and Data Design
Microservices are an architectural and organizational approach to software development where software is composed of small independent services that communicate over well-defined APIs. Microservices architectures make applications easier to scale and faster to develop, enabling innovation and accelerating time-to-market for new features. We’ll explore best practices for microservice design and the data design needed to support microservices, using Aurora, RDS, DynamoDB, DAX, ElastiCache, and Lambda and we’ll do a design exercise in converting a monolithic solution to a microservices design.
Co 4, session 2, aws analytics servicesm vaishnavi
AWS offers several analytics services to help process and provide insights from data. These include Amazon Athena for interactive querying of data stored in S3 using SQL, Amazon EMR for processing large amounts of data using Hadoop and other open source tools, Amazon CloudSearch for setting up a search solution easily, and Amazon Kinesis for collecting, processing, and analyzing real-time data. Other services are Amazon Redshift for data warehousing, Amazon Quicksight for interactive dashboards, AWS Glue for ETL jobs, and Amazon Lake Formation for securing data lakes.
We will introduce key concepts for a data lake and present aspects related to its implementation. Also discussing critical success factors, pitfalls to avoid operational aspects, and insights on how AWS enables a server-less data lake architecture.
Speaker: Sebastien Menant, Solutions Architect, Amazon Web Services
With distributed frameworks like Hadoop and Kafka, it is essential to deploy the right environment to successfully support these workloads. Learn about the different block storage options from AWS and walk through with our experts on how to select the best option for your big data analytic workloads. We will demonstrate how to setup, select, and modify volume types to right size your environment needs.
This document discusses best practices for building a data lake architecture on AWS. It recommends using Amazon S3 as the centralized data lake storage and decoupling storage from compute. This allows for cheaper, more efficient operation and the ability to evolve to clusterless analytics tools like Amazon Athena. The document provides guidance on security, ingestion, cataloging, cost optimization, analytics tools and building a sample pipeline to analyze data in the lake.
(BDT308) Using Amazon Elastic MapReduce as Your Scalable Data Warehouse | AWS...Amazon Web Services
In this presentation, we will demonstrate how to use Amazon Elastic MapReduce as your scalable data warehouse. Amazon EMR supports clusters with thousands of nodes and is used to access petabyte scale data warehouses. Amazon EMR is not only fast, but it is also easy to use for rapid development and adhoc analysis. We will show you how access the large scale data warehouses with emerging tools such as Hue, Hive, low latency SQL applications like Presto, and alternative execution engines like Apache Spark. We will also show you how these tools integrate directly with other AWS big data services such as Amazon S3, Amazon DynamoDB, and Amazon Kinesis.
This document summarizes key aspects of full stack analytics on AWS, including foundational services like storage, data ingestion, processing and analytics, machine learning, and security. It discusses AWS services like S3, Athena, Glue, Kinesis, Rekognition, and how they can be used together for cost-effective analytics from ingestion to machine learning to building smarter applications. Security is addressed at both the service and data levels using tools like IAM, encryption, and third party integration.
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
Today organizations find themselves in a data rich world with a growing need for increased agility and accessibility of all this data for analysis and deriving keen insights to drive strategic decisions. Creating a data lake helps you to manage all the disparate sources of data you are collecting, in its original format and extract value. In this session learn how to architect and implement an Analytics Data Lake. Hear customer examples of best practices and learn from their architectural blueprints.
Data collection and storage is a primary challenge for any big data architecture. In this session, we will describe the different types of data that customers are handling to drive high-scale workloads on AWS, and help you choose the best approach for your workload. We will cover optimization techniques that improve performance and reduce the cost of data ingestion.AWS services to be covered include: Amazon S3, DynamoDB, and Kinesis.
re:Invent Round-up, Time Stream, Quantum and Managed Blockchain Amazon Web Services
This document provides an overview of Amazon Web Services database services, including Amazon DynamoDB, Amazon RDS, Amazon ElastiCache, Amazon Neptune, and Amazon QLDB. It discusses the different types of databases, common use cases, and new features like Amazon Managed Blockchain and Amazon Timestream. The pricing examples show how Amazon QLDB and Amazon Managed Blockchain can provide ledger databases and blockchain networks at lower costs than traditional options.
By using a Data Lake, you no longer need to worry about structuring or transforming data before storing it. A Data Lake on AWS enables your organization to more rapidly analyze data, helping you quickly discover new business insights. Join us for our webinar to learn about the benefits of building a Data Lake on AWS and how your organization can begin reaping their rewards. In this session, we will share methodology for implementing a Data Lake on AWS and best practices for getting the most from your Data Lake.
Speaker: Russell Nash,
APAC Solution Architect, DW, AWS APAC
This document provides an agenda and overview for a workshop on building a data lake on AWS. The agenda includes reviewing data lakes, modernizing data warehouses with Amazon Redshift, data processing with Amazon EMR, and event-driven processing with AWS Lambda. It discusses how data lakes extend traditional data warehousing approaches and how services like Redshift, EMR, and Lambda can be used for analytics in a data lake on AWS.
The benefits of running databases in the cloud are compelling but how do you get the data there? In this session we will explore how to use the AWS Database Migration Service and the AWS Schema Conversion Tool to help you to migrate, or continuously replicate, your on-premise databases to AWS.
Speaker: Jarrod Spiga, Solutions Architect, Amazon Web Services
Building Serverless Web Applications - DevDay Los Angeles 2017Amazon Web Services
The document provides information about building serverless web applications using AWS Lambda and other AWS services. It begins with an overview of serverless computing using AWS Lambda and how it avoids the need to provision and manage servers. It then discusses various AWS compute offerings and when to use EC2, ECS, or Lambda. The rest of the document discusses serverless design patterns, demonstrates building a serverless web application using services like API Gateway and DynamoDB, and how to define and manage serverless applications using the AWS Serverless Application Model (SAM).
Logging infrastructure for Microservices using StreamSets Data CollectorCask Data
This document discusses using StreamSets Data Collector (SDC) to build a logging infrastructure for microservices. SDC can ingest logs from microservices running in containers and handle issues like schema changes and new log formats. It processes and transforms the logs, sending them to destinations like Kafka. SDC pipelines can run on Spark clusters on Yarn and Mesos to handle large volumes of log data and load it into systems like HDFS, HBase and Elasticsearch for analysis.
The document provides an overview of Amazon Web Services (AWS) databases and analytics services. It summarizes that AWS has significantly expanded its database and analytics offerings between 2015-2018, with over 750 new features and 10 new services launched. It highlights several core AWS database and analytics services, including Amazon DynamoDB, Amazon RDS, Amazon Aurora, Amazon Neptune, and Amazon ElastiCache. It also discusses how customers are migrating workloads from on-premises databases to AWS databases and analytics services.
Modernize Legacy and Enterprise Application Through Implementation of Cloud N...Amazon Web Services
Many Federal agencies are taking on initiatives to consolidate datacenters, modernize legacy and enterprise applications, and transform the digital portfolio to enable agility and other advantageous through cloud based delivery models. Deloitte provides a breadth of services to help federal government agencies select the right cloud solutions to accelerate their missions and derive value while helping agencies to be at the forefront of technology and innovation. Join us as we demonstrate specific client use cases where we have successfully assisted clients in selecting a cloud service model for migrating and transforming current data center to the cloud, enabling agility by taking advantage of Agile, DevOps, and continuous delivery, and modernized legacy and enterprise application through the implementation of cloud native solutions on AWS infrastructure to deliver value at the speed of your mission. Learn More: https://aws.amazon.com/government-education/
This document provides an overview of Amazon Web Services storage options for big data and analytics workloads. It discusses Amazon S3, Amazon EBS volume types, use cases for different storage solutions, examples of customers optimizing storage, and a new feature called EBS Elastic Volumes that allows modifying the configuration of live EBS volumes non-disruptively.
AWS March 2016 Webinar Series Building Your Data Lake on AWS Amazon Web Services
Uncovering new, valuable insights from big data requires organizations to collect, store, and analyze increasing volumes of data from multiple, often disparate sources at disparate points in time. This makes it difficult to handle big data with data warehouses or relational database management systems alone.
A Data Lake allows you to store massive amounts of data in its original form, without the need to enforce a predefined schema, enabling a far more agile and flexible architecture, which makes it easier to gain new types of analytical insights from your data
In this webinar, we will introduce key concepts of a Data Lake and present aspects related to its implementation. We will discuss critical success factors, pitfalls to avoid as well as operational aspects such as security, governance, search, indexing and metadata management.
Learning Objectives:
• Learn how AWS can help enable a Data Lake architecture
• Understand some of the key architectural considerations when building a Data Lake
• Hear some of the important Data Lake implementation considerations
Who Should Attend:
• Data architects, data scientists, advanced AWS developers
The document discusses challenges and best practices for data architecture in microservices environments. It covers issues like distributed transactions, eventual consistency, and error handling. It also provides recommendations for choosing data stores and keeping data consistent across services through master data management. The key aspects are using correlation IDs, designing services to handle their own rollbacks, leveraging streams for async operations, and classifying requirements to select the right database technologies.
Database Week at the San Francisco Loft: Microservices and Data Design
Microservices are an architectural and organizational approach to software development where software is composed of small independent services that communicate over well-defined APIs. Microservices architectures make applications easier to scale and faster to develop, enabling innovation and accelerating time-to-market for new features. We’ll explore best practices for microservice design and the data design needed to support microservices, using Aurora, RDS, DynamoDB, DAX, ElastiCache, and Lambda and we’ll do a design exercise in converting a monolithic solution to a microservices design.
Database Week at the San Francisco Loft
Microservices & Data Design
Microservices are an architectural and organizational approach to software development where software is composed of small independent services that communicate over well-defined APIs. Microservices architectures make applications easier to scale and faster to develop, enabling innovation and accelerating time-to-market for new features. We’ll explore best practices for microservice design and the data design needed to support microservices, using Aurora, RDS, DynamoDB, DAX, ElastiCache, and Lambda and we’ll do a design exercise in converting a monolithic solution to a microservices design.
Speakers:
Sachin Holla - Sr. Solutions Architect, AWS
The document discusses data architecture challenges and best practices for microservices. It covers challenges like distributed transactions, eventual consistency, and choosing appropriate data stores. It provides recommendations for handling errors and rollbacks in a distributed system using techniques like correlation IDs, transaction managers, and event-driven architectures with DynamoDB streams. The document also provides a framework for classifying non-functional requirements and mapping them to suitable AWS data services.
Microservices are an architectural and organizational approach to software development where software is composed of small independent services that communicate over well-defined APIs. Microservices architectures make applications easier to scale and faster to develop, enabling innovation and accelerating time-to-market for new features. We’ll explore best practices for microservice design and the data design needed to support microservices, using Aurora, RDS, DynamoDB, DAX, ElastiCache, and Lambda and we’ll do a design exercise in converting a monolithic solution to a microservices design.
Speaker: Rajanikanth Bhargava Chilakapati - Solutions Architect, AWS
This document discusses data design considerations for microservices architectures. It begins by comparing monolithic, SOA, and microservice approaches. It then covers some common challenges with microservices like distributed transactions and coordination. Key elements of microservices like service discovery, state management, and deployment are presented. The benefits of microservices like scalability and alignment with business domains are also summarized. The document dives into data architecture challenges and provides best practices around topics like decentralized data stores, transactional integrity, error handling, and choosing appropriate data storage technologies.
Data Design and Modeling for Microservices I AWS Dev Day 2018AWS Germany
Microservices architectures make applications easier to scale and faster to develop, enabling innovation and accelerating time-to-market for new features In this session, we used Aurora, RDS, DynamoDB, DAX, ElasticCache, and Lambda to explore best practices for microservice design and the data design needed to support microservices. We also did a design exercise by converting a monolithic solution to a microservices design. You can learn more about Microservices here: https://aws.amazon.com/microservices/.
AWS FSI Symposium 2017 NYC - Moving at the Speed of Serverless ft BroadridgeAmazon Web Services
This document discusses Broadridge's use of AWS serverless technologies like AWS Lambda and Amazon API Gateway to build their Experience Manager application. It provides an overview of Broadridge, describes the problem of migrating essential communications from physical to digital, and how the Experience Manager solution leverages AWS services. It then covers benefits Broadridge realized by moving to AWS, including faster deployments, usage-based pricing, and automated key rotation.
Navigating Microservice Architecture with AWS - AWS Public Sector Summit Sing...Amazon Web Services
Running and managing large-scale applications with microservice architecture is hard and often requires operating complex container management infrastructure. Amazon EC2 Container Service (Amazon ECS) is a highly scalable, high-performance container management service that supports Docker containers and makes it easy to run applications on a managed cluster of Amazon EC2 instances. In this session, we walk through a number of patterns used by our customers to run their microservice platforms. We dive deep into some of the challenges of running microservices – such as load balancing, service discovery, and secrets management – and see how Amazon ECS can help address them.
The document provides information about serverless computing on AWS Lambda. It discusses that serverless applications have no servers to provision or manage, scale automatically based on usage, and have built-in availability and fault tolerance. Various AWS services that can be used as event sources or functions for serverless applications are listed. Common use cases for serverless include web applications, data processing, chatbots, backends, and IT automation.
Leadership Session: Using DevOps, Microservices, and Serverless to Accelerate...Amazon Web Services
In this session, learn how AWS can help you innovate faster with DevOps, microservices, and serverless. Join us for a rare and intimate discussion with AWS senior leaders: David Richardson, VP of Serverless, Ken Exner, director of AWS Developer Tools, and Deepak Singh, director of Compute Services, Containers, and Linux. Hear them share development best practices and discuss key learnings from building modern applications at Amazon.com. Also, learn how developers can leverage containers, AWS Lambda, and developer tools to build and run production applications in the cloud.
Monitoring and Troubleshooting in a Serverless World - SRV303 - re:Invent 2017Amazon Web Services
How do you monitor and troubleshoot an application made up of many ephemeral, stateless functions? How do you debug a distributed application in production? In this talk, we walk you through best practices, tools, and conventions using common troubleshooting scenarios. We'll discuss how you can use AWS services to address these scenarios, such as using Amazon CloudWatch for alarms and using AWS X-Ray to detect cross service calls.
You will also learn how Financial Engines leverages AWS X-Ray to debug, monitor, and analyze latency data for its serverless applications. It will also share some best practices for debugging and reporting.
"When designing microservices there are a number of things to think about. Just for starters, the bounds of their functionality, how they communicate with their dependencies, and how they provide an interface for their own consumers. Serverless technologies such as AWS Lambda change paradigms around code structure, usage of libraries, and how you deploy and manage your applications. In this session, we show you how by combining microservices and serverless technologies, you can achieve the ultimate flexibility and agility that microservices aim for, while providing business value in how serverless greatly reduces operational overhead and cost.
In addition, National Geographic will share how it built its NG1 platform using a serverless, microservices architecture. The NG1 platform provides National Geographic consumers with content personalized to their preferences and behaviors in an intuitive, easy-to-use way on smartphones."
As serverless architectures become more popular, customers need a framework of patterns to help them identify how they can leverage AWS to deploy their workloads without managing servers or operating systems. This session describes re-usable serverless patterns while considering costs. For each pattern, we provide operational and security best practices and discuss potential pitfalls and nuances. We also discuss the considerations for moving an existing server-based workload to a serverless architecture. The patterns use services like AWS Lambda, Amazon API Gateway, Amazon Kinesis Streams, Amazon Kinesis Analytics, Amazon DynamoDB, Amazon S3, AWS Step Functions, AWS Config, AWS X-Ray, and Amazon Athena. This session can help you recognize candidates for serverless architectures in your own organizations and understand areas of potential savings and increased agility. What’s new in 2017: using X-Ray in Lambda for tracing and operational insight; a pattern on high performance computing (HPC) using Lambda at scale; how a query can be achieved using Athena; Step Functions as a way to handle orchestration for both the Automation and Batch patterns; a pattern for Security Automation using AWS Config rules to detect and automatically remediate violations of security standards; how to validate API parameters in API Gateway to protect your API back-ends; and a solid focus on CI/CD development pipelines for serverless –that includes testing, deploying, and versioning (SAM tools).
Join us to learn what's new in serverless computing and AWS Lambda. Dr. Tim Wagner, General Manager of AWS Lambda and Amazon API Gateway, will share the latest developments in serverless computing and how companies are benefiting from serverless applications. You'll learn about the latest feature releases from AWS Lambda, Amazon API Gateway, and more. You will also hear from FICO about how it is using serverless computing for its predictive analytics and data science platform.
AWS Application Service Workshop - Serverless ArchitectureJohn Yeung
Demonstrate how severless architecture can benefits enterprise to build API platforms, using Lambda, DynamoDB and API Gateway etc. Real-life use cases are also included.
Serverless introduction - AWS IL Beer Sheva meetupBoaz Ziniman
- The document discusses serverless computing, which allows building and running applications without having to manage servers. It describes how serverless is an evolution from physical servers, to virtual servers in datacenters, to virtual servers in the cloud. With serverless, there is no server to manage, which eliminates responsibilities around provisioning, scaling, operations, availability and fault tolerance. Serverless applications are event-driven and pay only for usage, with continuous scaling. Common use cases provided include a 3-tier web app and image thumbnail creation.
Database Freedom. Database migration approaches to get to the Cloud - Marcus ...Amazon Web Services
The document discusses database migration approaches to move to the cloud using AWS services. It covers how the database market and application architectures are changing, as well as how AWS offers database freedom and services for various database and analytics workloads. These include managed relational databases like RDS and Aurora, NoSQL databases and caches, data warehouses, analytics services, and the Database Migration Service for migrating databases to AWS.
SRV301-Optimizing Serverless Application Data Tiers with Amazon DynamoDBAmazon Web Services
"As a fully managed database service, Amazon DynamoDB is a natural fit for serverless architectures. In this session, we dive deep into why and how to use DynamoDB in serverless applications, followed by a real-world use case from CapitalOne.
First, we dive into the relevant DynamoDB features, and how you can use it effectively with AWS Lambda in solutions ranging from web applications to real-time data processing. We show how some of the new features in DynamoDB, such as Auto Scaling and Time to Live (TTL), are particularly useful in serverless architectures, and distill the best practices to help you create effective serverless applications. In the second part, we talk about how CapitalOne migrated billions of transactions to a completely serverless architecture and built a scalable, resilient and fast transaction platform by leveraging DynamoDB, AWS Lambda and other services within the serverless ecosystem."
Similar to Data Design for Microservices - DevDay Austin 2017 Day 2 (20)
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.
1) The document discusses building a minimum viable product (MVP) using Amazon Web Services (AWS).
2) It provides an example of an MVP for an omni-channel messenger platform that was built from 2017 to connect ecommerce stores to customers via web chat, Facebook Messenger, WhatsApp, and other channels.
3) The founder discusses how they started with an MVP in 2017 with 200 ecommerce stores in Hong Kong and Taiwan, and have since expanded to over 5000 clients across Southeast Asia using AWS for scaling.
This document discusses pitch decks and fundraising materials. It explains that venture capitalists will typically spend only 3 minutes and 44 seconds reviewing a pitch deck. Therefore, the deck needs to tell a compelling story to grab their attention. It also provides tips on tailoring different types of decks for different purposes, such as creating a concise 1-2 page teaser, a presentation deck for pitching in-person, and a more detailed read-only or fundraising deck. The document stresses the importance of including key information like the problem, solution, product, traction, market size, plans, team, and ask.
This document discusses building serverless web applications using AWS services like API Gateway, Lambda, DynamoDB, S3 and Amplify. It provides an overview of each service and how they can work together to create a scalable, secure and cost-effective serverless application stack without having to manage servers or infrastructure. Key services covered include API Gateway for hosting APIs, Lambda for backend logic, DynamoDB for database needs, S3 for static content, and Amplify for frontend hosting and continuous deployment.
This document provides tips for fundraising from startup founders Roland Yau and Sze Lok Chan. It discusses generating competition to create urgency for investors, fundraising in parallel rather than sequentially, having a clear fundraising narrative focused on what you do and why it's compelling, and prioritizing relationships with people over firms. It also notes how the pandemic has changed fundraising, with examples of deals done virtually during this time. The tips emphasize being fully prepared before fundraising and cultivating connections with investors in advance.
AWS_HK_StartupDay_Building Interactive websites while automating for efficien...Amazon Web Services
This document discusses Amazon's machine learning services for building conversational interfaces and extracting insights from unstructured text and audio. It describes Amazon Lex for creating chatbots, Amazon Comprehend for natural language processing tasks like entity extraction and sentiment analysis, and how they can be used together for applications like intelligent call centers and content analysis. Pre-trained APIs simplify adding machine learning to apps without requiring ML expertise.
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.
2. AWS Data Services to Accelerate Your Move to the Cloud
RDS
Open
Source
RDS
Commercial
Aurora
Migration for DB Freedom
DynamoDB
& DAX
ElastiCache EMR Amazon
Redshift
Redshift
Spectrum
AthenaElasticsearch
Service
QuickSightGlue
Databases to Elevate your Apps
Relational Non-Relational
& In-Memory
Analytics to Engage your Data
Inline Data Warehousing Reporting
Data Lake
Amazon AI to Drive the Future
Lex
Polly
Rekognition Machine
Learning
Deep Learning, MXNet
Database Migration
Schema Conversion