Happy 20th Anniversary of the AWS News Blog! 🎉🥳🎊 On November 9, 2004, Jeff Barr published his first blog post. At the time, he started a personal blog site using TypePad. He wanted to speak to his readers with his personal voice, not the company or team.

On April 29, 2014, we created a new AWS blog site and migrated all posts to that page. There are currently over 4,300 posts on the AWS News Blog, with Jeff contributing over 3,200 of them.

Since December 2016, the AWS News Blog has added new writers, but we are still following Jeff’s leadership principals for AWS News Bloggers in accordance with Day One. What’s unique about the AWS News Blog is that the blog writers get to use the features of the product team in advance, following the Customer Obsession leadership principle, and focus on walk-throughs of how customers can quickly use them to save time, with the Frugality principle.

I am very grateful for Jeff’s fundamental and pivotal role over the past 20 years, and I look forward to the next 20 years!

Last week’s launches
Here are some launches that got my attention:

New Express brokers for Amazon MSK – Express brokers are a new broker type for Amazon MSK Provisioned designed to deliver up to three times more throughput per broker, scale up to 20 times faster, and reduce recovery time by 90 percent as compared to standard Apache Kafka brokers. Express brokers come preconfigured with Kafka best practices by default, support all Kafka APIs, and provide the same low-latency performance, so you can continue using existing client applications without any changes.

New Amazon Kinesis Client Library 3.0 – You can now reduce compute costs to process streaming data by up to 33 percent with Kinesis Client Library (KCL) 3.0, compared to previous KCL versions. KCL 3.0 introduces an enhanced load balancing algorithm that continuously monitors resource utilization of the stream processing workers and automatically redistributes the load from overutilized workers to other underutilized workers. To learn more, read the AWS Big Data Blog post.

Microsoft Windows Server 2025 images on Amazon EC2 – We now support Microsoft Windows Server 2025 with License Included (LI) Amazon Machine Images (AMIs), providing customers with an easy and flexible way to launch the latest version of Windows Server. By running Windows Server 2025 on Amazon EC2, customers can take advantage of the security, performance, and reliability of AWS with the latest Windows Server features. To learn more about running Windows Server 2025 on Amazon EC2, visit Windows Workloads on AWS.

Anthropic’s Claude 3.5 Haiku model in Amazon Bedrock – Claude 3.5 Haiku is the next generation of Anthropic’s fastest model, combining rapid response times with improved reasoning capabilities, making it ideal for tasks that require both speed and intelligence. Claude 3.5 Haiku improves across every skill set and surpasses even Claude 3 Opus, the largest model in Anthropic’s previous generation, on many intelligence benchmarks—including coding. To learn more, read the AWS News Blog post.

Amazon Bedrock Prompt Management GA – You can simplify the creation, testing, versioning, and sharing of prompts in Amazon Bedrock Prompt Management. At general availability, we added new features that provide enhanced options for configuring your prompts and enabling seamless integration for invoking them in your generative AI applications, such as structured prompts and Converse and InvokeModel API integration. To learn more, read the AWS Machine Learning blog post.

Six new synthetic generative voices for Amazon Polly – The generative engine is Amazon Polly’s most advanced text-to-speech (TTS) model leveraging the generative AI technology. We added six new synthetic female-sounding generative voices: Ayanda (South African English), Léa (French), Lucia (European Spanish), Lupe (American Spanish), Mía (Mexican Spanish), and Vicki (German). This extends thirteen voices and nine locales to provide you with more options of highly expressive and engaging voices.

Amazon OpenSearch Service Extended Support – We announce the end of Standard Support and Extended Support timelines for legacy Elasticsearch versions and OpenSearch Versions. Standard Support ends on Nov 7, 2025, for legacy Elasticsearch versions up to 6.7, Elasticsearch versions 7.1 through 7.8, OpenSearch versions from 1.0 through 1.2, and OpenSearch versions 2.3 through 2.9. With Extended Support, for an incremental flat fee over regular instance pricing, you continue to get critical security updates beyond the end of Standard Support. To learn more, read the AWS Big Data Blog post.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS news
Here are some additional news items that you might find interesting:

CEO’s visiting at AWS data center – Matt Garman, CEO of AWS, had a great time visiting one of our AWS data centers recently, and was able to get a look at the continuous innovation delivered by the team. Of course, it’s no surprise that Amazon’s senior executives visit fulfillment centers, contact centers, and data centers, to do real work for customers. AWS data centers are designed for customers in every aspect, for maximum resilience, performance, and energy efficiency.

AWS supports small businesses, creates jobs, sets up sustainability initiatives, and develops educational programs near AWS data centers. Get the latest updates – AWS in your community: Here’s what’s happening near data centers across the US on About Amazon News.

Amazon Q Business at Amazon – I introduced an Amazon story to use Code transformation in Amazon Q Developer to migrate more than old 30,000 Java applications to Java 17 version. It saved over 4,500 developer years of effort compared to previous manual jobs and saved the company $260 million in annual by moving to the latest Java version.

Here is another dogfooding story of Amazon Q Business at Amazon. Amazon built an internal chatbot with Amazon Q Business and it has resolved over 1 million internal Amazon developer questions, reducing time spent churning on manual technical investigations by more than 450,000 hours.

Our team onboarded Amazon Q Business with millions of internal documents and integrated Q Business into the tools our team use every day. Now, instead of waiting hours for responses to complex technical questions on Q&A boards or Slack channels, developers can get answers in seconds.

TOURCast at PGA TOUR – If you enjoy golf, this news will be of interest to you. The PGA TOUR debuted TOURCast in Japan at the 2024 ZOZO Championship to capture and disseminate better statistical data and bring fans closer to the game based on new scoring system called ShotLink, powered by CDW. This marks the first time the PGA TOUR has been able to bring this technology to Asia, leveraging the flexibility and scalability of AWS to overcome unique challenges.

PGA TOUR volunteer setting up GPS equipment on the fairway at ZOZO championship that will input specific shot data and feed back to Shotlink Select Plus. [IMAGE: PGA TOUR]

They’ve completely rebuilt their scoring system over the past two years on a new cloud stack. With AWS cloud, whether data comes from high-tech radar systems, cameras, or manual input, the system processes it all seamlessly.

Upcoming AWS events
Check your calendars and sign up for these AWS events:

AWS GenAI Lofts – AWS GenAI Lofts are about more than just the tech, they bring together startups, developers, investors, and industry experts. Whether you’re looking to gain deep insights, or get your questions answered by generative AI pros, our GenAI Lofts have you covered, and provide everything you need to start building your next innovation. Join events in São Paulo (through November 20), and Paris (through November 25).

AWS Community Days – Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Jakarta, Indonesia (November 23), Kochi, India (December 14).

AWS re:Invent – You can still register for the annual learning event, taking place December 2–6 in Las Vegas. Surprisingly Andy Jassy, CEO of Amazon said he will come back and participate in AWS re:Invent this year. He said “As always, the priority is to make this a learning event so customers can take nuggets back and change their own customer experiences and businesses. We’ll also have a bunch of goodies for you that we’ll announce and that we think folks will like.” Let’s meet there!

You can browse all upcoming in-person and virtual events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Channy

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

The post AWS Weekly Roundup: 20 years of AWS News Blog, Express brokers for Amazon MSK, Windows Server 2025 images on EC2, and more (Nov 11, 2024) appeared first on The NOKIA Blog.

Data engineers play a crucial role in the modern data-driven landscape, managing essential tasks from data ingestion and processing to transformation and serving. Their expertise is particularly valuable in the era of generative AI, where harnessing the value of vast datasets is paramount.

To empower aspiring and experienced data professionals, DeepLearning.AI and Amazon Web Services (AWS) have partnered to launch the Data Engineering Specialization, an advanced professional certificate on Coursera. This comprehensive program covers a wide range of data engineering concepts, tools, and techniques relevant to modern organizations. It’s designed for learners with some experience working with data who are interested in learning the fundamentals of data engineering. The specialization comprises four hands-on courses, each culminating in a Coursera course certificate upon completion.

Specialization overview

This Data Engineering Specialization is a joint initiative by AWS and DeepLearning.AI, a leading provider of world-class AI education founded by renowned machine learning (ML) pioneer Andrew Ng.

Joe Reis, a prominent figure in data engineering and coauthor of the bestselling book Fundamentals of Data Engineering, leads the program as a primary instructor. By providing a foundational framework, the curriculum ensures learners gain a holistic understanding of the data engineering lifecycle, while covering key aspect such as data architecture, orchestration, DataOps, and data management.

Further enhancing the learning experience, the program features hands-on labs and technical assessments hosted on the AWS Cloud. These practical, cloud-based exercises were designed in partnership with AWS technical experts, including Gal Heyne, Navnit Shukla, and Morgan Willis. Learners will apply theoretical concepts using AWS services and tools, such as Amazon Kinesis, AWS Glue, Amazon Simple Storage Service (Amazon S3), and Amazon Redshift, equipping them with hands-on skill and experience.

Specialization highlights

Participants will be introduced to several key learning opportunities.

Acquisition of core skills and strategies

The specialization equips data engineers with the ability to design data engineering solutions for various use cases, select the right technologies for their data architecture, and circumvent potential pitfalls. The skills gained universally apply across various platforms and technologies, offering learners a program that is versatile.

Unparalleled approach to data engineering education

Unlike conventional courses focused on specific technologies, this specialization provides a comprehensive understanding of data engineering fundamentals. It emphasizes the importance of aligning data engineering strategies with broader business goals, fostering a more integrated and effective approach to building and maintaining data solutions.

Holistic understanding of data engineering

By using the insights from the Fundamentals of Data Engineering book, the curriculum offers a well-rounded education that prepares professionals for success in the data-driven focused industries.

Practical skills through AWS cloud labs

The hands-on labs hosted by AWS Partner Vocareum let learners apply the techniques directly in an AWS environment provided with the course. This practical experience is crucial for mastering the intricacies of data engineering and developing the skills needed to excel in the industry.

Why choose this specialization?

• Structured learning path–The specification is thoughtfully structured to provide a step-by-step learning journey, from foundational concepts to advanced applications.
• Expert insights–Gain insights from the authors of Fundamentals of Data Engineering and other industry experts. Learn how to apply practical knowledge to build modern data architecture on the cloud, using cloud services for data engineering.
• Hands-on experience–Engage in hands-on labs in the AWS Cloud, where you not only learn but also apply the knowledge in real-world scenarios.
• Comprehensive curriculum–This program encompasses all aspects of the data engineering lifecycle, including data generation in source systems, ingestion, transformation, storage, and serving. It also addresses key undercurrents of data engineering, such as security, data management, and orchestration.

At the end of this specialization, learners will be well-equipped with the necessary skills and expertise to embark on a career in data engineering, an in-demand role at the core of any organization that is looking to use data to create value. Data-centric ML and analytics would not be possible without the foundation of data engineering.

Course modules

The Data Engineering Specialization comprises four courses:

• Course 1–Introduction to Data Engineering–This foundational module explores the collaborative nature of data engineering, identifying key stakeholders and understanding their requirements. The course delves into a mental framework for building data engineering solutions, emphasizing holistic ecosystem understanding, critical factors like data quality and scalability, and effective requirements gathering. The course then examines the data engineering lifecycle, illustrating interconnections between stages. By showcasing the AWS data engineering stack, the course teaches how to use the right technologies. By the end of this course, learners will have the skills and mindset to tackle data engineering challenges and make informed decisions.
• Course 2–Source Systems, Data Ingestion, and Pipelines–In this course, data engineers dive deep into the practical aspects of working with diverse data sources, ingestion patterns, and pipeline construction. Learners explore the characteristics of different data formats and the appropriate source systems for generating each type of data, equipping them with the knowledge to design effective data pipelines. The course covers the fundamentals of relational and NoSQL databases, including ACID compliance and CRUD operations, so that engineers learn to interact with a wide range of data source systems. The course covers the significance of cloud networking, resolving database connection issues, and using message queues and streaming platforms—crucial skills for creating strong and scalable data architectures. By mastering the concepts in this course, data engineers will be able to automate data ingestion processes, optimize connectivity, and establish the foundation for successful data engineering projects.
• Course 3–Data Storage and Queries–This course equips data engineers with principles and best practices for designing robust, efficient data storage and querying solutions. Learners explore the data lake house concept, implementing a medallion-like architecture and using open table formats to build transactional data lakes. The course enhances SQL proficiency by teaching advanced queries, such as aggregations and joins on streaming data, while also exploring data warehouse and data lake capabilities. Learners compare storage performance and discover optimization strategies, like indexing. Data engineers can achieve high performance and scalability in data services by comprehending query execution and processing.
• Course 4–Data Modeling, Transformation, and Serving–In this capstone course, data engineers explore advanced data modeling techniques, including data vault and star schemas. Learners differentiate between modeling approaches like Inmon and Kimball, gaining the ability to transform data and structure it for optimal analytical and ML use cases. The course equips data engineers with preprocessing skills for textual, image, and tabular data. Learners understand the distinctions between supervised and unsupervised learning, as well as classification and regression tasks, empowering them to design data solutions supporting a range of predictive applications. By mastering these data modeling, transformation, and serving concepts, data engineers can build robust, scalable, and business-aligned data architectures to deliver maximum value.

Enrollment

Whether you’re new to data engineering or looking to enhance your skills, this specialization provides a balanced mix of theory and hands-on experience through 4 courses, each culminating in a Coursera course certificate.

Embark on your data engineering journey from here:

By enrolling in these courses, you’ll also earn the DeepLearning.AI Data Engineering Professional Certificate upon completing all four courses.

Enroll now and take the first step towards mastering data engineering with this comprehensive and practical program, built on the foundation of Fundamentals of Data Engineering and powered by AWS.

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