All posts by Glenn Gore

Glenn’s Take on re:Invent 2017 – Part 3

Post Syndicated from Glenn Gore original https://aws.amazon.com/blogs/architecture/glenns-take-on-reinvent-2017-part-3/

Glenn Gore here, Chief Architect for AWS. I was in Las Vegas last week — with 43K others — for re:Invent 2017. I checked in to the Architecture blog here and here with my take on what was interesting about some of the bigger announcements from a cloud-architecture perspective.

In the excitement of so many new services being launched, we sometimes overlook feature updates that, while perhaps not as exciting as Amazon DeepLens, have significant impact on how you architect and develop solutions on AWS.

Amazon DynamoDB is used by more than 100,000 customers around the world, handling over a trillion requests every day. From the start, DynamoDB has offered high availability by natively spanning multiple Availability Zones within an AWS Region. As more customers started building and deploying truly-global applications, there was a need to replicate a DynamoDB table to multiple AWS Regions, allowing for read/write operations to occur in any region where the table was replicated. This update is important for providing a globally-consistent view of information — as users may transition from one region to another — or for providing additional levels of availability, allowing for failover between AWS Regions without loss of information.

There are some interesting concurrency-design aspects you need to be aware of and ensure you can handle correctly. For example, we support the “last writer wins” reconciliation where eventual consistency is being used and an application updates the same item in different AWS Regions at the same time. If you require strongly-consistent read/writes then you must perform all of your read/writes in the same AWS Region. The details behind this can be found in the DynamoDB documentation. Providing a globally-distributed, replicated DynamoDB table simplifies many different use cases and allows for the logic of replication, which may have been pushed up into the application layers to be simplified back down into the data layer.

The other big update for DynamoDB is that you can now back up your DynamoDB table on demand with no impact to performance. One of the features I really like is that when you trigger a backup, it is available instantly, regardless of the size of the table. Behind the scenes, we use snapshots and change logs to ensure a consistent backup. While backup is instant, restoring the table could take some time depending on its size and ranges — from minutes to hours for very large tables.

This feature is super important for those of you who work in regulated industries that often have strict requirements around data retention and backups of data, which sometimes limited the use of DynamoDB or required complex workarounds to implement some sort of backup feature in the past. This often incurred significant, additional costs due to increased read transactions on their DynamoDB tables.

Amazon Simple Storage Service (Amazon S3) was our first-released AWS service over 11 years ago, and it proved the simplicity and scalability of true API-driven architectures in the cloud. Today, Amazon S3 stores trillions of objects, with transactional requests per second reaching into the millions! Dealing with data as objects opened up an incredibly diverse array of use cases ranging from libraries of static images, game binary downloads, and application log data, to massive data lakes used for big data analytics and business intelligence. With Amazon S3, when you accessed your data in an object, you effectively had to write/read the object as a whole or use the range feature to retrieve a part of the object — if possible — in your individual use case.

Now, with Amazon S3 Select, an SQL-like query language is used that can work with delimited text and JSON files, as well as work with GZIP compressed files. We don’t support encryption during the preview of Amazon S3 Select.

Amazon S3 Select provides two major benefits:

  • Faster access
  • Lower running costs

Serverless Lambda functions, where every millisecond matters when you are being charged, will benefit greatly from Amazon S3 Select as data retrieval and processing of your Lambda function will experience significant speedups and cost reductions. For example, we have seen 2x speed improvement and 80% cost reduction with the Serverless MapReduce code.

Other AWS services such as Amazon Athena, Amazon Redshift, and Amazon EMR will support Amazon S3 Select as well as partner offerings including Cloudera and Hortonworks. If you are using Amazon Glacier for longer-term data archival, you will be able to use Amazon Glacier Select to retrieve a subset of your content from within Amazon Glacier.

As the volume of data that can be stored within Amazon S3 and Amazon Glacier continues to scale on a daily basis, we will continue to innovate and develop improved and optimized services that will allow you to work with these magnificently-large data sets while reducing your costs (retrieval and processing). I believe this will also allow you to simplify the transformation and storage of incoming data into Amazon S3 in basic, semi-structured formats as a single copy vs. some of the duplication and reformatting of data sometimes required to do upfront optimizations for downstream processing. Amazon S3 Select largely removes the need for this upfront optimization and instead allows you to store data once and process it based on your individual Amazon S3 Select query per application or transaction need.

Thanks for reading!

Glenn contemplating why CSV format is still relevant in 2017 (Italy).

Glenn’s Take on re:Invent Part 2

Post Syndicated from Glenn Gore original https://aws.amazon.com/blogs/architecture/glenns-take-on-reinvent-part-2/

Glenn Gore here, Chief Architect for AWS. I’m in Las Vegas this week — with 43K others — for re:Invent 2017. We’ve got a lot of exciting announcements this week. I’m going to check in to the Architecture blog with my take on what’s interesting about some of the announcements from an cloud architectural perspective. My first post can be found here.

The Media and Entertainment industry has been a rapid adopter of AWS due to the scale, reliability, and low costs of our services. This has enabled customers to create new, online, digital experiences for their viewers ranging from broadcast to streaming to Over-the-Top (OTT) services that can be a combination of live, scheduled, or ad-hoc viewing, while supporting devices ranging from high-def TVs to mobile devices. Creating an end-to-end video service requires many different components often sourced from different vendors with different licensing models, which creates a complex architecture and a complex environment to support operationally.

AWS Media Services
Based on customer feedback, we have developed AWS Media Services to help simplify distribution of video content. AWS Media Services is comprised of five individual services that can either be used together to provide an end-to-end service or individually to work within existing deployments: AWS Elemental MediaConvert, AWS Elemental MediaLive, AWS Elemental MediaPackage, AWS Elemental MediaStore and AWS Elemental MediaTailor. These services can help you with everything from storing content safely and durably to setting up a live-streaming event in minutes without having to be concerned about the underlying infrastructure and scalability of the stream itself.

In my role, I participate in many AWS and industry events and often work with the production and event teams that put these shows together. With all the logistical tasks they have to deal with, the biggest question is often: “Will the live stream work?” Compounding this fear is the reality that, as users, we are also quick to jump on social media and make noise when a live stream drops while we are following along remotely. Worse is when I see event organizers actively selecting not to live stream content because of the risk of failure and and exposure — leading them to decide to take the safe option and not stream at all.

With AWS Media Services addressing many of the issues around putting together a high-quality media service, live streaming, and providing access to a library of content through a variety of mechanisms, I can’t wait to see more event teams use live streaming without the concern and worry I’ve seen in the past. I am excited for what this also means for non-media companies, as video becomes an increasingly common way of sharing information and adding a more personalized touch to internally- and externally-facing content.

AWS Media Services will allow you to focus more on the content and not worry about the platform. Awesome!

Amazon Neptune
As a civilization, we have been developing new ways to record and store information and model the relationships between sets of information for more than a thousand years. Government census data, tax records, births, deaths, and marriages were all recorded on medium ranging from knotted cords in the Inca civilization, clay tablets in ancient Babylon, to written texts in Western Europe during the late Middle Ages.

One of the first challenges of computing was figuring out how to store and work with vast amounts of information in a programmatic way, especially as the volume of information was increasing at a faster rate than ever before. We have seen different generations of how to organize this information in some form of database, ranging from flat files to the Information Management System (IMS) used in the 1960s for the Apollo space program, to the rise of the relational database management system (RDBMS) in the 1970s. These innovations drove a lot of subsequent innovations in information management and application development as we were able to move from thousands of records to millions and billions.

Today, as architects and developers, we have a vast variety of database technologies to select from, which have different characteristics that are optimized for different use cases:

  • Relational databases are well understood after decades of use in the majority of companies who required a database to store information. Amazon Relational Database (Amazon RDS) supports many popular relational database engines such as MySQL, Microsoft SQL Server, PostgreSQL, MariaDB, and Oracle. We have even brought the traditional RDBMS into the cloud world through Amazon Aurora, which provides MySQL and PostgreSQL support with the performance and reliability of commercial-grade databases at 1/10th the cost.
  • Non-relational databases (NoSQL) provided a simpler method of storing and retrieving information that was often faster and more scalable than traditional RDBMS technology. The concept of non-relational databases has existed since the 1960s but really took off in the early 2000s with the rise of web-based applications that required performance and scalability that relational databases struggled with at the time. AWS published this Dynamo whitepaper in 2007, with DynamoDB launching as a service in 2012. DynamoDB has quickly become one of the critical design elements for many of our customers who are building highly-scalable applications on AWS. We continue to innovate with DynamoDB, and this week launched global tables and on-demand backup at re:Invent 2017. DynamoDB excels in a variety of use cases, such as tracking of session information for popular websites, shopping cart information on e-commerce sites, and keeping track of gamers’ high scores in mobile gaming applications, for example.
  • Graph databases focus on the relationship between data items in the store. With a graph database, we work with nodes, edges, and properties to represent data, relationships, and information. Graph databases are designed to make it easy and fast to traverse and retrieve complex hierarchical data models. Graph databases share some concepts from the NoSQL family of databases such as key-value pairs (properties) and the use of a non-SQL query language such as Gremlin. Graph databases are commonly used for social networking, recommendation engines, fraud detection, and knowledge graphs. We released Amazon Neptune to help simplify the provisioning and management of graph databases as we believe that graph databases are going to enable the next generation of smart applications.

A common use case I am hearing every week as I talk to customers is how to incorporate chatbots within their organizations. Amazon Lex and Amazon Polly have made it easy for customers to experiment and build chatbots for a wide range of scenarios, but one of the missing pieces of the puzzle was how to model decision trees and and knowledge graphs so the chatbot could guide the conversation in an intelligent manner.

Graph databases are ideal for this particular use case, and having Amazon Neptune simplifies the deployment of a graph database while providing high performance, scalability, availability, and durability as a managed service. Security of your graph database is critical. To help ensure this, you can store your encrypted data by running AWS in Amazon Neptune within your Amazon Virtual Private Cloud (Amazon VPC) and using encryption at rest integrated with AWS Key Management Service (AWS KMS). Neptune also supports Amazon VPC and AWS Identity and Access Management (AWS IAM) to help further protect and restrict access.

Our customers now have the choice of many different database technologies to ensure that they can optimize each application and service for their specific needs. Just as DynamoDB has unlocked and enabled many new workloads that weren’t possible in relational databases, I can’t wait to see what new innovations and capabilities are enabled from graph databases as they become easier to use through Amazon Neptune.

Look for more on DynamoDB and Amazon S3 from me on Monday.

 

Glenn at Tour de Mont Blanc

 

 

Glenn’s Take on re:Invent 2017 Part 1

Post Syndicated from Glenn Gore original https://aws.amazon.com/blogs/architecture/glenns-take-on-reinvent-2017-part-1/

GREETINGS FROM LAS VEGAS

Glenn Gore here, Chief Architect for AWS. I’m in Las Vegas this week — with 43K others — for re:Invent 2017. We have a lot of exciting announcements this week. I’m going to post to the AWS Architecture blog each day with my take on what’s interesting about some of the announcements from a cloud architectural perspective.

Why not start at the beginning? At the Midnight Madness launch on Sunday night, we announced Amazon Sumerian, our platform for VR, AR, and mixed reality. The hype around VR/AR has existed for many years, though for me, it is a perfect example of how a working end-to-end solution often requires innovation from multiple sources. For AR/VR to be successful, we need many components to come together in a coherent manner to provide a great experience.

First, we need lightweight, high-definition goggles with motion tracking that are comfortable to wear. Second, we need to track movement of our body and hands in a 3-D space so that we can interact with virtual objects in the virtual world. Third, we need to build the virtual world itself and populate it with assets and define how the interactions will work and connect with various other systems.

There has been rapid development of the physical devices for AR/VR, ranging from iOS devices to Oculus Rift and HTC Vive, which provide excellent capabilities for the first and second components defined above. With the launch of Amazon Sumerian we are solving for the third area, which will help developers easily build their own virtual worlds and start experimenting and innovating with how to apply AR/VR in new ways.

Already, within 48 hours of Amazon Sumerian being announced, I have had multiple discussions with customers and partners around some cool use cases where VR can help in training simulations, remote-operator controls, or with new ideas around interacting with complex visual data sets, which starts bringing concepts straight out of sci-fi movies into the real (virtual) world. I am really excited to see how Sumerian will unlock the creative potential of developers and where this will lead.

Amazon MQ
I am a huge fan of distributed architectures where asynchronous messaging is the backbone of connecting the discrete components together. Amazon Simple Queue Service (Amazon SQS) is one of my favorite services due to its simplicity, scalability, performance, and the incredible flexibility of how you can use Amazon SQS in so many different ways to solve complex queuing scenarios.

While Amazon SQS is easy to use when building cloud-native applications on AWS, many of our customers running existing applications on-premises required support for different messaging protocols such as: Java Message Service (JMS), .Net Messaging Service (NMS), Advanced Message Queuing Protocol (AMQP), MQ Telemetry Transport (MQTT), Simple (or Streaming) Text Orientated Messaging Protocol (STOMP), and WebSockets. One of the most popular applications for on-premise message brokers is Apache ActiveMQ. With the release of Amazon MQ, you can now run Apache ActiveMQ on AWS as a managed service similar to what we did with Amazon ElastiCache back in 2012. For me, there are two compelling, major benefits that Amazon MQ provides:

  • Integrate existing applications with cloud-native applications without having to change a line of application code if using one of the supported messaging protocols. This removes one of the biggest blockers for integration between the old and the new.
  • Remove the complexity of configuring Multi-AZ resilient message broker services as Amazon MQ provides out-of-the-box redundancy by always storing messages redundantly across Availability Zones. Protection is provided against failure of a broker through to complete failure of an Availability Zone.

I believe that Amazon MQ is a major component in the tools required to help you migrate your existing applications to AWS. Having set up cross-data center Apache ActiveMQ clusters in the past myself and then testing to ensure they work as expected during critical failure scenarios, technical staff working on migrations to AWS benefit from the ease of deploying a fully redundant, managed Apache ActiveMQ cluster within minutes.

Who would have thought I would have been so excited to revisit Apache ActiveMQ in 2017 after using SQS for many, many years? Choice is a wonderful thing.

Amazon GuardDuty
Maintaining application and information security in the modern world is increasingly complex and is constantly evolving and changing as new threats emerge. This is due to the scale, variety, and distribution of services required in a competitive online world.

At Amazon, security is our number one priority. Thus, we are always looking at how we can increase security detection and protection while simplifying the implementation of advanced security practices for our customers. As a result, we released Amazon GuardDuty, which provides intelligent threat detection by using a combination of multiple information sources, transactional telemetry, and the application of machine learning models developed by AWS. One of the biggest benefits of Amazon GuardDuty that I appreciate is that enabling this service requires zero software, agents, sensors, or network choke points. which can all impact performance or reliability of the service you are trying to protect. Amazon GuardDuty works by monitoring your VPC flow logs, AWS CloudTrail events, DNS logs, as well as combing other sources of security threats that AWS is aggregating from our own internal and external sources.

The use of machine learning in Amazon GuardDuty allows it to identify changes in behavior, which could be suspicious and require additional investigation. Amazon GuardDuty works across all of your AWS accounts allowing for an aggregated analysis and ensuring centralized management of detected threats across accounts. This is important for our larger customers who can be running many hundreds of AWS accounts across their organization, as providing a single common threat detection of their organizational use of AWS is critical to ensuring they are protecting themselves.

Detection, though, is only the beginning of what Amazon GuardDuty enables. When a threat is identified in Amazon GuardDuty, you can configure remediation scripts or trigger Lambda functions where you have custom responses that enable you to start building automated responses to a variety of different common threats. Speed of response is required when a security incident may be taking place. For example, Amazon GuardDuty detects that an Amazon Elastic Compute Cloud (Amazon EC2) instance might be compromised due to traffic from a known set of malicious IP addresses. Upon detection of a compromised EC2 instance, we could apply an access control entry restricting outbound traffic for that instance, which stops loss of data until a security engineer can assess what has occurred.

Whether you are a customer running a single service in a single account, or a global customer with hundreds of accounts with thousands of applications, or a startup with hundreds of micro-services with hourly release cycle in a devops world, I recommend enabling Amazon GuardDuty. We have a 30-day free trial available for all new customers of this service. As it is a monitor of events, there is no change required to your architecture within AWS.

Stay tuned for tomorrow’s post on AWS Media Services and Amazon Neptune.

 

Glenn during the Tour du Mont Blanc