Top announcements at the on-going AWS re:Invent 2022

Share on facebook
Share on twitter
Share on linkedin
Share on whatsapp
AWS Reinvent

AWS re:Invent is an annual five-day conference hosted by Amazon Web Services (AWS). In its 11th consecutive year, re:Invent is being held from November 28th to December 2nd, 2022, in Las Vegas, Nevada. This massive event is one of the largest learning conferences for the cloud computing community, worldwide.

Amazon is known to encourage “cloud-ready cultures” by enabling direct learning from AWS experts through such events. The conference can be attended in person or virtually through prior registration formalities. The five-day program comprises keynote speeches from renowned leaders, peer talks, boot camps, technical & breakout sessions, chalk talks, workshops, demo booths, training & certifications, after-hour events and networking opportunities across multinationals.

Attendees at the event get introduced to new AWS features, practices, and services along with the latest innovation and trends in the cloud computing world. This year, AWS will be emphasizing on how innovation and artificial intelligence can bring actionable insights into businesses. The conference aims to provide deeper insights into AWS analytics, AWS storage, AWS Marketplace, compute, containers, database, business applications, global infrastructure, security, identity & compliance, management tools and Machine Learning to name a few.

Here are some key announcements and top launches taking place at AWS re:Invent 2022


New features in Amazon Redshift to simplify Data Ingestion

Amazon Redshift has made a number of announcements at re:Invent this year to make it easier for you to streamline data ingestion and quickly access insights in a safe and dependable environment increasing reliability and security of data warehouses.

Using Amazon Athena with Spark

With this feature, Apache Spark workloads can be run and Jupyter can be used as the interface to carry out data processing on Athena, and programmatically engage with Spark packages using Athena APIs.

New, updated engines and new data formats in AWS Glue 4.0

Python 3.10 and Apache Spark 3.3.0 are both included in this Glue release in addition to native support for the Cloud Shuffle Service Plugin for Spark. It additionally consists of Pandas guide and more.

Real-Time Analytics During Live Calls in Amazon Transcribe

Real-time call analytics offers developers with APIs to reliably record live calls while also identifying customer experience problems and sentiment in real-time.

Amazon Security Lake – a new service

With Amazon Security Lake, an organization’s cloud or on-premise security data can be automatically centralised to create a purpose-built client owned data lake service that can be stored in your account.

AWS storage

New features in AWS Backup

Protect and Restore CloudFormation Stacks with an automated solution for building and restoring applications that eliminates the need to manage custom scripts.

Amazon Redshift support in AWS Backup

Now define central backup policies with AWS Backup to manage data protection of applications as well as safeguard Amazon Redshift clusters.

Announcing Automated In-AWS Failback for AWS Elastic Disaster Recovery

New automated support provides a simple and accelerated experience to return a failed Amazon Elastic Compute Cloud (Amazon EC2) instance to its original region, providing both failover and failback processes (either locally or for recovery within AWS) that can be conveniently run from AWS Management console.

AWS Marketplace

Simplify Risk Assessments for Third-Party Software with AWS Marketplace Vendor Insights

Through the consolidation of security and compliance data, such as data privacy and residency, application security, and access control, into a single consolidated dashboard, this new capability helps guarantee that third-party software consistently complies with your industry standards.


Use the new Lambda SnapStart feature

Enabling the new Lambda SnapStart feature for Java helps speed up Lambda functions by 10x at no extra cost.

Improved Network Latency and Per-Flow Performance on EC2

The new ENA Express offers much greater per-flow bandwidth with much less variability.

Improved Amazon EC2 instances with better general purpose, compute, and memory performance

The new EC2 instance families are designed to aid data-intensive workloads with highest EBS performance, and the capacity to handle double the packets per second (PPS) as compared to earlier instances.

Provision of Microsoft Office Amazon Machine Images (AMIs) on Amazon EC2 with AWS furnished licenses

With the announcement of such an offering, clients now have the option to run Microsoft Office based applications on EC2


Fully Managed Blue/Green Deployments on Amazon Aurora and Amazon RDS

This new feature for MySQL-compatible Amazon Aurora, Amazon RDS for MySQL, and Amazon RDS for MariaDB makes database updates safer, easier, and faster.

Business Applications

AWS Wickr – A secure end-to-end encrypted communication service for enterprises with audit and regulatory requirements

Unlike many enterprises communication tools available, Wickr uses end-to-end encryption mechanisms to ensure that messages, files, and voice or video calls are only accessible to the intended recipients.

Global Infrastructure

AWS Local Zones now available in four new metropolitan areas

Announcing that AWS Local Zones are now generally available in Buenos Aires, Copenhagen, Helsinki, and Muscat.

Security, Identity & Compliance

Amazon Inspector now scans AWS Lambda functions for vulnerabilities

Prior to Amazon Inspector, customers who wanted to evaluate workloads including EC2 instances, container images, and Lambda functions against common vulnerabilities had to use AWS and third-party tools.

Amazon Macie Automatic Data Discovery

This new feature provides visibility into the location of sensitive data across Amazon Simple Storage Service (Amazon S3) buckets at a fraction of the cost as compared to running a full data inspection.

AWS Announces Amazon Verified Permissions (Preview)

This centralized, fine-grained permission management system makes it easy to change and update permission rules in one place without changing code.

Management Tools

AWS Config Rules now support Proactive Compliance

With the proactive support mode added into this release, AWS Config rules can now be used at any time prior to provisioning, thus, saving the time spent on developing unique pre-deployment validations.

Comprehensive Control Management – a new feature for AWS Control Tower (Preview)

Henceforth, by service, control objective, or compliance framework, you can apply managed preventative, detective, and proactive controls to accounts and organisational units.

Use Amazon CloudWatch Logs to Protect Sensitive Data

This new set of features for Amazon CloudWatch Logs uses pattern matching and machine learning (ML) to protect sensitive data in transit.

Observability for cross-account Amazon CloudWatch

You can now search, examine, and correlate cross-account telemetry data in CloudWatch, including metrics, logs, and traces.

Amazon CloudWatch Internet Monitor

This new feature provides insights into how an internet problem could affect the functionality and accessibility of your applications. It enables you to shorten the process of internet problem diagnosis to provide end-to-end performance visibility.

Machine Learning

Next Gen SageMaker Notebooks

With built-in data preparation, real-time collaboration and notebook automation, you can now improve data quality in minutes, edit the same notebook as your teammates in real time, and automatically convert notebook code into production-ready jobs.

Amazon SageMaker Data Wrangler, Real-time and Batch Inference Support now available

You can use data transformation flows created in SageMaker Data Wrangler as components of inference pipelines for Amazon SageMaker with the aid of this functionality.

Improve project visibility and simplify access control with mew ML Governance Tools for Amazon SageMaker

With the help of new tools, you can monitor all deployed models from a single dashboard, quickly create custom SageMaker user rights, and keep track of model specifics from conception to deployment.

Build, Train, and Deploy ML Models using Geospatial data with Amazon SageMaker

Large geographic datasets can be accessed and prepared with ease because of this feature set comprising of pre-trained deep neural network (DNN) models and geospatial operators.

Share on twitter
Share on linkedin
Share on facebook
Share on whatsapp

Leave a Comment

Your email address will not be published. Required fields are marked *