AWS re:Invent 2022: The Key Announcements and Takeaways
There was no shortage of news at AWS re:Invent 2022 in Las Vegas.
As with most huge tech conferences, this year’s re:Invent saw the cloud giant introduce a slew of new products and services, including new data governance and sharing, supply chain management services, and spatial simulation tools and capabilities, among others. Throughout re:Invent, AWS also showcased a number of new AI and machine learning offerings.
While the event largely focused on partners and business solutions, the developer community has mixed reviews. Let’s look at the biggest announcements from this mega event.
This year saw a slew of announcements about analytics and data processing. Some of the highlights include:
- Amazon security lake (a customer-owned data lake service)
- New Amazon QuickSight API capabilities for efficient BI
- Amazon Athena for Apache Spark
- A new version of AWS Glue
- Amazon OpenSearch Serverless for analytics workloads without managing clusters
The most interesting announcements regarding analytics, however, were the announcements regarding Amazon Redshift and Amazon Quicksight.
Amazon Redshift can now automatically load files in an Amazon Simple Storage Service (Amazon S3) location. For copy command-enabled data types such as CSV, JSON, Parquet, and Avro, you may avoid manual copy processing. Amazon Aurora zero-ETL connection with Amazon Redshift is another improvement that streamlines data ingestion. Other security enhancements include multi-AZ deployments and dynamic data masking.
This year’s re:Invent also saw the general availability of Amazon Redshift integration for Apache Spark and AWS Backup support. Amazon Redshift received several intriguing enhancements, including the general availability of streaming ingestion for Kinesis Data Streams and managed streaming for Apache Kafka, as well as a Spark Connector.
At Simform, we have used Amazon Redshift in a lot of solutions. For instance, a renowned education firm desired to fully overhaul and replace its existing solution. This necessitated the redesign of their entire platform and migration to a modern cloud-based stack. We needed to integrate 50+ systems and bring all of their data to a single source of truth for performing meaningful BI and Analytics operations for correct insights.
The challenge was to migrate the program to the AWS Cloud and redesign it without impacting performance.
We created a database architecture to support a cloud-based data warehouse powered by Amazon Redshift. To initiate independent synchronization processes for multiple system integrations, a microservices architecture was deployed.
Another issue was that users couldn’t run dynamic queries or apply complex filters to reports and data to build more effective analytics. The information was out of date and stagnant.
We built a solution in which AWS Glue initiates ETL operations to get everything into Amazon Redshift clusters, which process data at breakneck speed for insight generation.
How we built a transformative big data and analytics solution for school districts
As we can see, Amazon Redshift was an important part of the solution. We are excited about the new Amazon Redshift updates unveiled at AWS re:Invent 2022. Our future solutions will definitely benefit from these updates.
Using the recently introduced paginated reporting feature, customers can easily build and share critical operational reports. They can even ask questions using the automated data preparation feature of Amazon QuickSight Q.
Developers, on the contrary, can build, edit and manage the Amazon QuickSight dashboards and reports.
The in-memory engine of Amazon QuickSight now supports 1 billion rows of data. Thus, making it easier and faster to analyze and visualize huge datasets.
Amazon QuickSight is an integral part of a lot of solutions that Simform has delivered. For example, we designed a 360-degree data analytics platform for a multi-million dollar sports eCommerce firm. AWS QuickSight was one of the tools used to build the solution.
The client desired to comprehend the customer journey by tracking event attendees, how they engaged, and when and how they purchased. The goal was to develop a highly tailored digital buying experience while leveraging the appropriate balance of marketing channels and dollars spent.
To build the solution, we started by linking numerous data sources, including customer data, analytics data, customer engagement/funnel data, events data, loyalty, reviews, marketing automation, and eCommerce plugins.
Using AWS Glue and Data Lake, we created a standardized data enrichment solution that enabled us to categorize data. This data was then used to build BI and Analytics reports in AWS Quicksight, as well as run advertising, loyalty, and offer campaigns based on different segments.
We built multi-channel customer journeys by linking Shopify, Klaviyo, Ticketsocket, Mozu, Facebook advertising, Clicky, Ruckclub, Bazaarvoice, and Mailchimp.
Result? The client was able to increase ROI on marketing initiatives by 40%.
How we built a data analytics platform for a multi-million dollar sports eCommerce brand
One of the challenges in this solution was migrating the data from legacy systems. Through the new updates in Amazon QuickSight, we hope that we will now be able to achieve migration in a faster and simpler manner in our future solutions.
AWS announced AWS Glue for Ray at the event. The tool can quickly process large python datasets and supports popular Python libraries.
The AWS Glue Data Quality is a tool that can analyze the tables and automatically recommend a set of rules based on its findings.
At Simform, we have used AWS Glue as a critical component in a lot of solutions that we have delivered. For instance, one of North America’s top hospitality recruitment and career platforms approached us about modernizing its recruitment platform with a contemporary tech stack. They also wanted us to assist them in gradually migrating to new systems and smoothly onboarding old and new clients.
In this application, we used ETL using AWS Glue to parse enormous amounts of data. For client performance analytics, we integrated Salesforce and other ATLs, which is also utilized by the client’s sales staff to upsell and uncover new prospects.
At the end of the project, the customer realized a 10X increase in sales, and the platform was able to easily handle 600,000 users per day.
Know more on how we engineered a bespoke recruitment platform for hospitality domain
The new AWS Glue tools announced at AWS re:Invent 2022 will help us explore new possibilities with the existing as well as future solutions that we build using AWS Glue.
AWS SimSpace Weaver
Dynamic 3D trials assist businesses in a variety of industries — transportation, robotics, and public safety — in understanding, and training for potential real-world outcomes.
For example, defining new factory floor procedures, testing multiple natural disaster response scenarios, or accounting for various road closure combinations.
However, sophisticated spatial simulations necessitate significant compute resources, and integrating and scaling simulations with millions of interacting objects across compute instances can be a challenging and costly operation.
AWS SimSpace Weaver has been released to assist users in building, operating, and running large-scale spatial simulations. Users can utilize the fully-managed compute service to deploy spatial simulations to model systems with many data points, such as traffic patterns throughout a metropolis, the crowd flow in a venue, or manufacturing floor layouts.
SimSpace Weaver automatically sets up the environment, links up to 10 Amazon EC2 instances into a networked cluster, and distributes the simulation across instances when a customer is ready to deploy. According to AWS, the service then controls network and memory configurations, duplicating and synchronizing data across instances to produce a single, unified simulation with numerous users interacting and manipulating the simulation in real-time.
The capacity to handle spiking workloads is one of AWS Lambda’s biggest advantages, and what keeps developers coming back for more. In layman’s words, it is the ability to deal with sudden and continuous spikes in task volume. The most difficult obstacle for developers who wish to use AWS Lambda is the occasional cold start. The extended startup duration make AWS Lambda cold starts much more uncomfortable for Java users. Particularly in Java contexts.
During his Monday night speech at AWS re:Invent 2022, Peter DeSantis revealed a new AWS Lambda functionality called AWS Lambda SnapStart. By producing a snapshot of your AWS Lambda function and bypassing the standard initialization process, AWS Lambda SnapStart virtually eliminates the AWS Lambda cold starts. AWS has made SnapStart available for Java 11 functions.
Updates to Amazon EC2
In the virtual computing instances area, AWS Elastic Compute Cloud has been the market leader. And, as with previous re:Invents, the 2022 version includes numerous upgrades for Amazon EC2 users.
- Enhanced Network Enablers have been a huge help in terms of more bandwidth. ENA Express employs the Scalable Reliable Datagram (SRD) protocol to minimize P99 latency by up to 50% and P99.9 latency by up to 85%. Note that these numbers are in comparison to TCP.
- AWS also introduced many new Amazon EC2 instances, including M6in/M6idn general-purpose compute instances, C6in with improved packet-processing speed, and memory-optimized instances R6in/R6idn. These instances will be available on-demand and on-spot and may be limited to select regions at launch.
AWS is emphasizing its advantage in bespoke silicon and a variety of instance kinds. It stressed its capacity to give low prices while also providing great performance in the same offering. The announcements of Nitro v5, C7gn (powered by Nitro v5), HPC7g, Inf2 for Amazon EC2, and SimSpace Weaver demonstrate AWS’s push to address HPC workloads, focus on bigger and faster compute, and keep up with AI innovation coming from Microsoft Azure and, in particular, Google Cloud Platform (GCP).
Petabytes — even exabytes — of data are collected by today’s enterprises, which are scattered over many departments, services, on-premises databases, and third-party sources.
However, before they can realize the full value of this data, administrators and data stewards must make it available. Simultaneously, they must retain control and governance to ensure that data is only accessible by the appropriate person and in the appropriate context.
The new Amazon DataZone service is introduced to assist enterprises in cataloging, discovering, sharing, and governing data from AWS, on-premises, and third-party sources.
Organizations can use the new data management service’s web portal to create their own business data catalog by defining their data taxonomy, configuring governance policies, and connecting to a variety of AWS services (such as Amazon S3 or Amazon Redshift), partner solutions (such as Salesforce and ServiceNow), and on-premises systems.
AWS Clean Rooms
In order to gain vital insights, businesses frequently wish to supplement their own data with that of their partners. At the same time, they must safeguard sensitive consumer data and limit or eliminate raw data sharing.
Data clean rooms can assist in addressing this issue since they allow numerous parties to combine and analyze their data in a secure setting where participants cannot access each other’s raw data. However, clean rooms can be challenging to construct since they require complicated privacy measures and specific data transportation technologies.
AWS Clean Rooms is a service that promises to make the process easier. Organizations can now instantly construct secure data clean rooms in the AWS Cloud and work with any other enterprise.
Customers, according to AWS, pick the partners with whom they want to engage, specify their datasets, and configure participant limits. They have access to data access constraints that can be configured, such as query controls, query output restrictions, and query logging, while modern cryptographic computing tools keep data secured.
Blue/Green Deployments for MySQL on Amazon Aurora and Amazon RDS
It’s a new feature for Amazon Aurora, and it comes with MySQL compatibility along with Amazon RDS for MariaDB and Amazon RDS for MySQL. This new feature essentially helps you make database updates simpler, faster, and safer.
Failover Controls for Amazon S3 Multi-Region Access Points is a great feature added by AWS for multi-region applications. This feature will improve the latency for end users. It will help them achieve higher resilience and availability, thereby, improving the disaster recovery mechanism for multi-region applications that operate using multi-region databases. The feature also routes traffic to the closest image of your data by monitoring network congestion and connectivity using AWS Global Accelerator.
AWS also launched a new mode named Amazon EFS Elastic Throughput. This mode lets the users use and pay only for the throughput consumed by their applications. The new throughput mode also aids the users in simplifying the workload management on AWS. This is because the new mode works on shared file storage and does not require provisioning management.
AWS backup allows the users to attach an AWS CloudFormation stack to their data protection policies. Thus, allowing the users to have a single recovery point for restoring the application stack. Moreover, AWS Backup now also provides backup support for Amazon Redshift. The users can now manage the application’s data protection by defining a centralized backup policy. The service enhances user experience and provides better data protection mechanisms to the users.
The Automated in-AWS Failback for AWS Elastic Disaster Recovery feature provides a fast and simple experience of failing back Amazon Elastic Compute Cloud (Amazon EC2) instances to the original region. Moreover, the users can conveniently start both failover and failback processes (for on-premises or in-AWS recovery) from the AWS Management Console.
AWS KMS External Key Store
This is a fantastic announcement for customers who must store and use encryption keys on-premises or outside of the AWS Cloud due to regulatory requirements. This new feature will allow customers to store AWS KMS customer-controlled keys on a hardware security module (HSM) that is operated remotely.
AWS KMS is an integral part of many solutions that Simform has provided. For example, one of our clients, a global leader in semiconductor manufacturing, sought to digitize and automate the order fulfillment process. In this scenario, the client’s primary concern was security.
Our team performed Application Vulnerability Tests on a regular basis to guarantee that there were no potential flaws in the application, server, and backend services that attackers could exploit.
We used the AWS Key Management Service (KMS) to manage the cryptographic keys used by the cross-functional team to access AWS workloads and enterprise applications.
Check out how we digitized & automated order fulfillment lifecycle for a semiconductors manufacturer
By using the new functionality added in AWS KMS, we would be able to help customers who need to store AWS KMS keys on-premise or outside the AWS cloud.
AWS has expanded the reach of Amazon Inspector. Now the Amazon Inspector can scan for vulnerabilities in AWS Lambda functions.
AWS has also included an automated data discovery tool for Amazon Macie. With this new feature, users can now see the location of their private data on the Amazon S3 for a much lesser cost. They do not have to perform a comprehensive data examination of all the Amazon S3 buckets.
Amazon Security Lake
Amazon Security Lake is a service that makes it easier for organizations to automatically normalize the security data from AWS. It converts the security data into the Apache Parquet format and conforms it to the Open Cybersecurity Schema Framework to help organizations combine security data with a plethora of pre-integrated third-party enterprise security data sources.
The Amazon Security Lake allows users to act on security-sensitive data faster.
Amazon SageMaker updates
Amazon SageMaker has 8 new capabilities; Amazon SageMaker JumpStart is a fantastic addition to Amazon SageMaker’s pedigree!
With this feature’s debut, the users can now share ML artifacts with other users that share their AWS account using Amazon SageMaker JumpStart.
Customers may now use Amazon SageMaker to build, train, and deploy machine learning models using geospatial data. This set of features includes pre-trained deep neural network (DNN) models and geospatial operators that make it easy to acquire and process large amounts of geospatial data.
The Amazon SageMaker now supports shadow testing. This lets the users conduct tests in a shadow mode while accounting for real-world situations in a holistic manner.
AWS Machine Learning University has announced a new educator enablement initiative in order to develop diverse talent for AI and ML careers. In addition, they improved the Amazon Comprehend capability, which is utilized for intelligent document processing (IDP). It can now classify and extract entities from MS Word, PDF, and picture files without first extracting the text.
Amazon CodeWhisperer is a service for worker identity solutions. AWS administrators may now use the CodeWhisperer to define organization-wide settings and rapidly give Single sign-on authentication to groups or individuals. This is intended to reduce human mistakes while obtaining mortgage and loan data. To improve the speed and accuracy of mortgage and loan data extraction, Amazon Textract, a machine learning (ML) service, can conduct signature detection, Social Security Number extraction, Tax ID detection, and other data extraction functions.
This year’s AWS re:Invent was focused on simplifying the existing products and services and making them more relevant to the end users. Although a section of developers who were expecting big-ticket announcements on the tech front was disappointed. Overall, the event can be deemed a success for partners and customers. Let’s have a look at the key takeaways from AWS re:Invent 2022.
Change in tone
Many of this year’s AWS re:Invent announcements emphasized “simplification” and “user friendliness”. This is markedly different from prior years, when the majority of AWS announcements were focused on features and functions, with a heavy emphasis on technology.
This year, rather than wowing delegates with new cutting-edge technology, announcement after announcement focused on improving ease of use.
AWS focused on educating clients on how they can leverage existing AWS capabilities.
The AWS partnership with global systems integrators took a new turn with the announcement of a deal with Atos that will shift information technology outsourcing contracts toward cloud migration while skirting contractual hurdles that have hitherto stymied such initiatives.
More broadly, re:Invent demonstrates that AWS remains the backbone for the majority of SaaS and Internet enterprises in general. Many of the capacity expansions and innovations that AWS provides to its clients are likely to be driven by the needs of AWS’s tech customers.
This year’s re:Invent highlighted how cost-conscious AWS customers could save money by including their purchases in their committed cloud budget.
At this year’s re:Invent, we witnessed clients from a variety of industries reinventing their business functions with purpose-built solutions and a philosophy of transformation and innovation through services. To optimize the delivery and management of AWS services, AWS will rely on its partner ecosystem.
AWS is evolving into a SaaS company
AWS has long been regarded as a pioneer in Infrastructure-as-a-Service, and it is now discreetly offering Software-as-a-Service applications.
Amazon Connect was created a few years ago to penetrate contact centers. When it was first released, it was a simple device with telephony, basic interactive voice response, and not much else.
AWS has since included all digital channels as well as a range of AI capabilities. It unveiled a number of Amazon Connect enhancements at re:Invent, including Workspace, an artificial intelligence-based agent training platform, machine learning-based forecasting, and a no-code interface for IVR.
AWS Supply Chain is also now in preview mode, according to the company. The solution, as the name implies, promises to assist businesses in increasing supply chain visibility in order to make faster, more informed decisions that can save money, decrease risk, and improve the customer experience.
The most intriguing aspect of these disclosures is that Amazon Connect and AWS Supply Chain are both proprietary tools used by Amazon to manage its own business.
The company chose to construct them because they believed it would give them a competitive advantage over pre-built tools, and they are now transforming those capabilities into customer-centric ones.
We believe that taking internal software and offering them as SaaS services will become typical AWS practice. Procurement, human resources, expenditure management, and other areas where AWS sees an advantage could be future uses.
When most people think of AWS, they see a corporation that manufactures Lego-style building bricks. Developers take them, put the pieces together, and create something. While this method works for many, it is not suitable for all organizations because it requires a significant amount of effort and talent to fit the building blocks together.
AWS now pre-integrates the items to make deployment easier. It highlighted the combination of its Amazon Aurora relational database with Amazon Redshift for cloud data warehousing as an example. Customers rarely buy one without the other. Thus close connection makes sense.
I anticipate more of this in the future, as well as easier integration into its large ecosystem.
Democratization of AI and machine learning
There is a lot of buzz surrounding AI, and machine learning as a lot of industries are slowly waking up to the potential of these two technologies. The problem, however, with AI and machine learning is that they are difficult to operate. In order for the technologies to become ubiquitous, they should be simplified.
At the AWS re:Invent 2022, AWS announced eight updates to Amazon SageMaker. Role Manager is one of the key new features included in the Amazon SageMaker. Through the Role Manager, the admins can now easily accomplish the following tasks.
- Control access
- Simplify documentation
- Track performance through a centralized dashboard
These updates have helped simplify AI and Machine Learning. We expect AWS to continue the trend of simplifying AI and Machine Learning for its customers in the future re:Invent events.
Following a week of high-profile announcements, the major lesson is that AWS wants to make it as simple as possible for businesses to shift to the cloud and use their products. Many of their announcements are interoperable with on-premise and third-party cloud products, with the goal of providing solutions for businesses with heterogeneous IT systems.
The company is targeting security, data processing and storage, and increased business insights, among other crucial problems on most engineers’ minds, with its portfolio of new tools, features, and solution updates.
As the year proceeds, we’ll see how these announcements influence AWS’ bottom line and which of these announcements will prove to be the most valuable for cloud computing in the future.