Keeping up with innovations is essential if your organization depends on the AWS tech stack. Knowing about new releases beforehand allows you to get ready and utilize them to their fullest potential once they are released. Additionally, AWS re:Invent 2022 offers the chance to learn about the cloud computing behemoth's future intentions.
AWS's annual conference, re:Invent 2022, was held in Las Vegas over four days in late November and early December. It included over 50,000 participants in person and 300,000 virtual guests. We noted several keynotes and product announcements during the event to support our clients who use AWS's tools and services. There were many sessions, speakers, and strategic announcements, just like at every significant tech event. Here are some major conclusions that are incredibly crucial.
Numerous announcements relating to analytics and data processing were made this year. Among the highlights are:
However, the news surrounding Amazon Redshift and Amazon QuickSight was the most intriguing regarding analytics.
Now, files stored in an Amazon Simple Storage Service (Amazon S3) location can be loaded automatically by Amazon Redshift. You can skip manual copy processing for data formats like CSV, JSON, Parquet, and Avro, including copy commands. Another improvement in simplifying data intake is the link between Amazon Aurora zero-ETL and Amazon Redshift. Dynamic data masking and multi-AZ deployments are additional security improvements.
The general availability of Amazon Redshift integration for Apache Spark and AWS Backup support was also announced at this year's re:Invent. A Spark Connector, controlled streaming for Apache Kafka, and streaming ingestion for Kinesis Data Streams are just some noteworthy improvements made to Amazon Redshift.
Customers can quickly create and distribute important operating reports using the recently launched paginated reporting capability. They can even ask questions using Amazon QuickSight Q's automatic data preparation tool. On the other hand, the developers can create, edit, and manage the dashboards and reports for Amazon QuickSight. Amazon QuickSight's in-memory engine can now handle 1 billion rows of data. Hence, making the analysis and visualization of large datasets simpler and faster.
AWS made a statement about AWS Glue for Ray there. This tool can process large Python datasets efficiently. Also, it supports well-known Python modules. The AWS Glue Data Quality tool can explore the tables and automatically provide a set of criteria based on its results.
The actual world is so complicated that simulating it on a computer requires much processing. With the help of SimSpace Weaver, you can scale, orchestrate, and coordinate a fleet of EC2 instances to execute your simulations on a massive scale from inside popular engines like Unreal Engine 5 and Unity. This service is specifically aimed at a group of users that require large-scale simulation of millions of entities, such as modeling traffic patterns over an entire city, catastrophe simulation, or the movement of millions of individuals.
AWS Elastic Compute Cloud has long held the top position in the market for virtual computing instances. Additionally, the 2022 edition of re:Invent offers several enhancements for Amazon EC2 users.
AWS emphasizes the benefits of its bespoke silicon and several instance types. It highlighted its ability to deliver affordable pricing and excellent performance in the same offering. The launch of Nitro v5, C7gn (powered by Nitro v5), HPC7g, Inf2 for Amazon EC2, and SimSpace Weaver shows AWS's commitment to addressing HPC workloads, concentrating on more extensive and faster computation and keeping up with AI innovation from Microsoft Azure and, in particular, Google Cloud Platform (GCP).
Amazon DataZone has been introduced under Private Preview. It enables your data producers to share what they have and link them with your data consumers. So they can access it in a way relevant to your business and within the parameters of your data governance policies.
Businesses typically want to complement their data with their partners to acquire crucial insights. At the same time, they must protect private customer information and restrict or stop the exchange of raw data. Data clean rooms enable several parties to integrate and analyze their data in a safe environment without participants being able to see each other's raw data, which can help with this problem. Clean rooms can be challenging to build, requiring intricate privacy safeguards and specialized data transmission technology. The service AWS Clean Rooms claims to simplify the procedure. In the AWS Cloud, businesses can rapidly build secure data clean rooms and collaborate with other companies.
It's a new feature for Amazon Aurora, including Amazon RDS for MariaDB compatibility and Amazon RDS for MySQL. This new functionality makes database modifications easier, quicker, and safer.
AWS offers An outstanding feature for multi-region applications: Failover Controls for Amazon S3 Multi-Region Access Points. For end users, this functionality will reduce delay. As a result, the disaster recovery system for multi-region applications that use multi-region databases will be improved. It will assist them in achieving greater resilience and availability. Using AWS Global Accelerator to monitor network congestion and connection, the function also directs traffic to the nearest image of your data.
Amazon EFS Elastic Throughput is a new mode that AWS also introduced. With this option, customers can only use and pay for their apps' throughput. Additionally, this mode helps customers streamline workload control on AWS. It operates without provisioning management as it uses shared file storage.
Users can add an AWS CloudFormation stack to their data protection policies using AWS backup. As a result, users may restore their application stack from a single recovery point. Further, AWS Backup now supports Amazon Redshift backups as well. Users may now define a centralized backup policy to control how the program protects user data. The service improves user experience and gives consumers more excellent data protection tools.
Automated in-AWS Failback for AWS Elastic Disaster Recovery makes it quick and easy to fail back Amazon Elastic Compute Cloud (Amazon EC2) instances to the original region. The AWS Management Console also makes it simple for customers to initiate failover and failback procedures (for on-premises or within AWS recovery).
It is an excellent development for clients who are lawfully required to keep and utilize encryption keys on-premises or outside the AWS Cloud. Customers can store AWS KMS customer-controlled keys using this new capability on a hardware security module (HSM) that can be managed remotely.
Amazon Security Lake makes it simpler for businesses to standardize security data from AWS automatically. To assist enterprises in combining security data with various pre-integrated third-party enterprise security data sources, it translates security data into the Apache Parquet format and conforms it to the Open Cybersecurity Schema Framework.
Amazon SageMaker now offers 8 new features. Including Amazon SageMaker JumpStart, it is a significant improvement over previous versions.
With this capability's introduction, Amazon SageMaker JumpStart customers who share an AWS account may now exchange ML artifacts with one another.
Customers can now create, practice, and distribute machine learning models utilizing geographic data using Amazon SageMaker. Pre-trained deep neural network (DNN) models and geospatial operators are among the elements that make it simple to gather and analyze vast volumes of geographical data. Amazon SageMaker now supports shadow testing. It enables users to run tests in shadow mode while fully considering real-world scenarios.
AWS Machine Learning University has unveiled a new educator support program to cultivate a broad talent pool for AI and ML employment. They also enhanced the Amazon Comprehend functionality used for IDP (intelligent document processing). It can now categorize and extract entities from MS Word, PDF, and image files without initially extracting the text.
For worker identification issues, there is a service called Amazon CodeWhisperer. AWS administrators can quickly grant single sign-on authentication to groups or people and establish organization-wide settings using the CodeWhisperer. It is done to decrease human error when collecting mortgage and loan data. Amazon Textract, a machine learning (ML) service, can perform signature detection, Social Security Number extraction, Tax ID extraction, and other data extraction activities to enhance mortgage and loan data extraction speed and accuracy.
AWS being one of the industry leaders in the cloud offers solutions for companies with diverse IT systems. Many of their announcements are compatible with on-premise and third-party cloud products. With its portfolio of new tools, features, and solution improvements, AWS aims to address security, data processing, and storage and improve business insights, among other critical issues on most engineers' concerns. We'll see how these announcements affect AWS' financial performance as the year goes on and which will benefit cloud computing in the long run.