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Structured Query Language (SQL)

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Snowflake: The Definitive Guide, 2nd Edition

Snowflake is reshaping data management by integrating AI, analytics, and enterprise workloads into a single cloud platform. Snowflake: The Definitive Guide is a comprehensive resource for data architects, engineers, and business professionals looking to harness Snowflake's evolving capabilities, including Cortex AI, Snowpark, and Polaris Catalog for Apache Iceberg. This updated edition provides real-world strategies and hands-on activities for optimizing performance, securing data, and building AI-driven applications. With hands-on SQL examples and best practices, this book helps readers process structured and unstructured data, implement scalable architectures, and integrate Snowflake's AI tools seamlessly. Whether you're setting up accounts, managing access controls, or leveraging generative AI, this guide equips you with the expertise to maximize Snowflake's potential. Implement AI-powered workloads with Snowflake Cortex Explore Snowsight and Streamlit for no-code development Ensure security with access control and data governance Optimize storage, queries, and computing costs Design scalable data architectures for analytics and machine learning

The Data Engineer's Guide to Microsoft Fabric

Modern data engineering is evolving; and with Microsoft Fabric, the entire data platform experience is being redefined. This essential book offers a fresh, hands-on approach to navigating this shift. Rather than being an introduction to features, this guide explains how Fabric's key components—Lakehouse, Warehouse, and Real-Time Intelligence—work under the hood and how to put them to use in realistic workflows. Written by Christian Henrik Reich, a data engineering expert with experience that extends from Databricks to Fabric, this book is a blend of foundational theory and practical implementation of lakehouse solutions in Fabric. You'll explore how engines like Apache Spark and Fabric Warehouse collaborate with Fabric's Real-Time Intelligence solution in an integrated platform, and how to build ETL/ELT pipelines that deliver on speed, accuracy, and scale. Ideal for both new and practicing data engineers, this is your entry point into the fabric of the modern data platform. Acquire a working knowledge of lakehouses, warehouses, and streaming in Fabric Build resilient data pipelines across real-time and batch workloads Apply Python, Spark SQL, T-SQL, and KQL within a unified platform Gain insight into architectural decisions that scale with data needs Learn actionable best practices for engineering clean, efficient, governed solutions

PostgreSQL: Up and Running, 4th Edition

Thinking of migrating to PostgreSQL? This concise introduction helps you understand and use this open source database system. Not only will you learn about the new enterprise class features in versions 16 to 18, but you'll also discover all that PostgreSQL has to offer—much more than a relational database system. As an open source product, it has hundreds of plug-ins, expanding the capability of PostgreSQL beyond all other database systems. With examples throughout, this book shows you how to perform tasks that are difficult or impossible in other databases. The revised fourth edition covers the latest features of Postgres, such as ISO-SQL constructs rarely found in other databases, foreign data wrapper (FDW) enhancements, JSON constructs, multirange data types, query parallelization, and replication. If you're an experienced PostgreSQL user, you'll pick up gems you may have missed before. Learn basic administration tasks such as role management, database creation, backup, and restore Use psql command-line utility and the pgAdmin graphical administration tool Explore PostgreSQL tables, constraints, and indexes Learn powerful SQL constructs not generally found in other databases Use several different languages to write database functions and stored procedures Tune your queries to run as fast as your hardware will allow Query external and variegated data sources with foreign data wrappers Learn how to use built-in replication to replicate data

Advanced SQL

SQL is no longer just a querying language for relational databases—it's a foundational tool for building scalable, modern data solutions across real-time analytics, machine learning workflows, and even generative AI applications. Advanced SQL shows data professionals how to move beyond conventional SELECT statements and tap into the full power of SQL as a programming interface for today's most advanced data platforms. Written by seasoned data experts Rui Pedro Machado, Hélder Russa, and Pedro Esmeriz, this practical guide explores the role of SQL in streaming architectures (like Apache Kafka and Flink), data lake ecosystems, cloud data warehouses, and ML pipelines. Geared toward data engineers, analysts, scientists, and analytics engineers, the book combines hands-on guidance with architectural best practices to help you extend your SQL skills into emerging workloads and real-world production systems. Use SQL to design and deploy modern, end-to-end data architectures Integrate SQL with data lakes, stream processing, and cloud platforms Apply SQL in feature engineering and ML model deployment Master pipe syntax and other advanced features for scalable, efficient queries Leverage SQL to build GenAI-ready data applications and pipelines

Head First SQL, 2nd Edition

What will you learn from this book? Do you have an abundance of data but don't know how to make sense of it? Do you want to gain useful insights from your data, but you're not sure where to begin? Mining data is a vital, well-paying skill, and SQL provides the most fundamental way to query and manage data. But learning SQL can be intimidating. This thoroughly revised book teaches you SQL fundamentals in a painless and enjoyable manner. With the Head First series' hands-on, conversational style, you'll quickly grasp SQL concepts, then move to intermediate topics, including stored procedures and cloud databases. You'll gain the knowledge, skills, and confidence necessary to get the most out of your data with SQL. Why does this book look so different? If you've read a Head First book, you know what to expect: a visually rich format designed for the way your brain works. If you haven't, you're in for a treat. With this book, you'll learn about SQL through a multisensory experience that engages your mind—rather than a text-heavy approach that puts you to sleep.

PostgreSQL 18 for Developers

Developing intelligent applications that integrate AI, analytics, and transactional capabilities using the latest release of the world's most popular open-source database Key Features Practical examples demonstrating how to use Postgres to develop intelligent applications Best practices for developers of intelligent data management applications Includes the latest PostgreSQL 18 features for AI, analytics, and transactions ures for AI, analytics, and transactions Book Description In today’s data-first world, businesses need applications that blend transactions, analytics, and AI to power real-time insights at scale. Mastering PostgreSQL 18 for AI-Powered Enterprise Apps is your essential guide to building intelligent, high-performance systems with the latest features of PostgreSQL 18. Through hands-on examples and expert guidance, you’ll learn to design architectures that unite OLTP and OLAP, embed AI directly into apps, and optimize for speed, scalability, and reliability. Discover how to apply cutting-edge PostgreSQL tools for real-time decisions, predictive analytics, and automation. Go beyond basics with advanced strategies trusted by industry leaders. Whether you’re building data-rich applications, internal analytics platforms, or AI-driven services, this book equips you with the patterns and insights to deliver enterprise-grade innovation. Ideal for developers, architects, and tech leads driving digital transformation, this book empowers you to lead the future of intelligent applications. Harness the power of PostgreSQL 18—and unlock the full potential of your data. What you will learn How to leverage PostgreSQL 18 for building intelligent data-driven applications for the modern enterprise Data management principles and best practices for managing transactions, analytics, and AI use cases How to utilize Postgres capabilities to address architectural challenges and attain optimal performance for each use case Methods for utilizing the latest Postgres innovation to create integrated data management applications Guidelines on when to use Postgres and when to opt for specialized data management solutions Who this book is for This book is intended for developers creating intelligent, data-driven applications for the modern enterprise. It features hands-on examples that demonstrate how to use PostgreSQL as the database for business applications that integrate transactions, analytics, and AI. We explore the fundamental architectural principles of data management and detail how developers utilize PostgreSQL 18's latest capabilities to build AI-enabled applications. The book assumes a working knowledge of SQL and does not address the needs of data analysts or those looking to master SQL.

Real-Time Intelligence with Microsoft Fabric

In today's hyper-connected world, many organizations are overwhelmed by the volume of data generated every second. Making timely decisions using this information remains a challenge for many. Real-time intelligence has transformed from a luxury to a necessity for businesses striving to stay ahead in a rapidly evolving marketplace. Enter Microsoft Fabric's Real-Time Intelligence: a new tool that not only analyzes data but also acts upon the results. If you're ready to unlock the power of immediate insights, this comprehensive primer offers an exploration into the capabilities of Real-Time Intelligence with Microsoft Fabric. Authors Johan Ludvig Brattås and Frank Geisler explain AI-driven insights and how to use them to drive business success. Whether you're a seasoned professional or an enthusiast, this guide is the key to understanding an exciting new platform. You'll discover: The core concepts of Real-Time Intelligence within Microsoft Fabric Challenges that can be solved with Real-Time Intelligence, enhancing efficiency Techniques for using KQL queries, including SQL knowledge to optimize these queries Practical applications including data analytic solutions, event streams, and more How to automatically trigger actions based on data conditions

Microsoft Fabric Analytics Engineer Associate Study Guide

Data is the heartbeat of business. Microsoft's Fabric Analytics Engineer Associate (DP-600) certification proves you understand the tools and technologies to make use of it. This comprehensive guide covers everything you need to know to design and implement enterprise-grade analytics solutions—and ace your certification exam. Drawing on their extensive experience working with Microsoft Fabric and Power BI, Brian Bønk and Valerie Junk take you through preparing and transforming data, securing and managing analytics assets, and building and optimizing semantic models. You'll learn to work with data warehouses and lakehouses, ensuring data is structured and ready for analysis. You will also discover how to query and analyze data using SQL, KQL, and DAX, which are essential skills for anyone working with Fabric. Whether you're preparing for the exam or just looking to expand your Fabric expertise, this book gives you the foundation to succeed. Prepare and enrich data for analysis Work with, secure, and maintain analytics assets Implement and manage semantic models Utilize data warehouses and lakehouses Handle workspace access control and item-level access control Optimize enterprise-scale semantic models

Google Cloud Certified Professional Data Engineer Certification Guide

A guide to pass the GCP Professional Data Engineer exam on your first attempt and upgrade your data engineering skills on GCP. Key Features Fully understand the certification exam content and exam objectives Consolidate your knowledge of all essential exam topics and key concepts Get realistic experience of answering exam-style questions Develop practical skills for everyday use Purchase of this book unlocks access to web-based exam prep resources including mock exams, flashcards, exam tips Book Description The GCP Professional Data Engineer certification validates the fundamental knowledge required to perform data engineering tasks and use GCP services to enhance data engineering processes and further your career in the data engineering/architecting field. This book is a best-in-class study guide that fully covers the GCP Professional Data Engineer exam objectives and helps you pass the exam first time. Complete with clear explanations, chapter review questions, realistic mock exams, and pragmatic solutions, this guide will help you master the core exam concepts and build the understanding you need to go into the exam with the skills and confidence to get the best result you can. With the help of relevant examples, you'll learn fundamental data engineering concepts such as data warehousing and data security. As you progress, you'll delve into the important domains of the exam, including data pipelining, data migration, and data processing. Unlike other study guides, this book contains logical reasoning behind the choice of correct answers based in scenarios and provide you with excellent tips regarding the optimal use of each service, and gives you everything you need to pass the exam and enhance your prospects in the data engineering field. What you will learn Create data solutions and pipelines in GCP Analyze and transform data into useful information Apply data engineering concepts to real scenarios Create secure, cost-effective, valuable GCP workloads Work in the GCP environment with industry best practices Who this book is for This book is for data engineers who want a reliable source for the key concepts and terms present in the most prestigious and highly-sought-after cloud-based data engineering certification. This book will help you improve your data engineering in GCP skills to give you a better chance at earning the GCP Professional Data Engineer Certification. You will already be familiar with the Google Cloud Platform, having either explored it (professionally or personally) for at least a year. You should also have some familiarity with basic data concepts (such as types of data and basic SQL knowledge).

Data Contracts in Practice

In 'Data Contracts in Practice', Ryan Collingwood provides a detailed guide to managing and formalizing data responsibilities within organizations. Through practical examples and real-world use cases, you'll learn how to systematically address data quality, governance, and integration challenges using data contracts. What this Book will help me do Learn to identify and formalize expectations in data interactions, improving clarity among teams. Master implementation techniques to ensure data consistency and quality across critical business processes. Understand how to effectively document and deploy data contracts to bolster data governance. Explore solutions for proactively addressing and managing data changes and requirements. Gain real-world skills through practical examples using technologies like Python, SQL, JSON, and YAML. Author(s) Ryan Collingwood is a seasoned expert with over 20 years of experience in product management, data analysis, and software development. His holistic techno-social approach, designed to address both technical and organizational challenges, brings a unique perspective to improving data processes. Ryan's writing is informed by his extensive hands-on experience and commitment to enabling robust data ecosystems. Who is it for? This book is ideal for data engineers, software developers, and business analysts working to enhance organizational data integration. Professionals with a familiarity of system design, JSON, and YAML will find it particularly beneficial. Enterprise architects and leadership roles looking to understand data contract implementation and their business impacts will also greatly benefit. Basic understanding of Python and SQL is recommended to maximize learning.

What if you could ensure the accuracy and integrity of your data effortlessly? Join this sessions on Data Constraints in databases, where we'll uncover the fundamental rules that keep your data reliable and consistent. Starting with the basics, we'll explore what data constraints are and why they are essential for maintaining the quality of your database. We'll dive into different types of data constraints, including primary keys, foreign keys, unique constraints, and check constraints, explaining how each one helps in enforcing data rules and relationships within your database. You'll learn the correct syntax for defining these constraints and see practical examples of how they can prevent errors and ensure data integrity. Through real-world scenarios, we'll illustrate how data constraints can be used to enforce business rules and improve the overall robustness of your database applications. We'll also discuss the performance implications of using constraints and best practices for their implementation to optimize both efficiency and accuracy. Finally, we'll cover common challenges and pitfalls associated with data constraints, providing tips on how to avoid them and ensure your database remains a reliable asset for your organization. Whether you're a novice or looking to deepen your understanding, this session will equip you with the knowledge to leverage data constraints effectively and enhance the reliability of your database systems.

Oracle 23AI & ADBS in Action: Exploring New Features with Hands-On Case Studies

Unlock the power of Oracle Database 23AI and Autonomous Database Serverless (ADB-S) with this comprehensive guide to the latest innovations in performance, security, automation, and AI-driven optimization. As enterprises embrace intelligent and autonomous data platforms, understanding these capabilities is essential for data architects, developers, and DBAs. Explore cutting-edge features such as vector data types and AI-powered vector search, revolutionizing data retrieval in modern AI applications. Learn how schema privileges and the DB_DEVELOPER_ROLE simplify access control in multi-tenant environments. Dive into advanced auditing, SQL Firewall, and data integrity constraints to strengthen security and compliance. Discover AI-driven advancements like machine learning-based query execution, customer retention prediction, and AI-powered query tuning. Additional chapters cover innovations in JSON, XML, JSON-Relational Duality Views, new indexing techniques, SQL property graphs, materialized views, partitioning, lock-free transactions, JavaScript stored procedures, blockchain tables, and automated bigfile tablespace shrinking. What sets this book apart is its practical focus—each chapter includes real-world case studies and executable scripts, enabling professionals to implement these features effectively in enterprise environments. Whether you're optimizing performance or aligning IT with business goals, this guide is your key to building scalable, secure, and AI-powered solutions with Oracle 23AI and ADB-S. What You Will Learn Explore Oracle 23AI's latest features through real-world use cases Implement AI/ML-driven optimizations for smarter, autonomous database performance Gain hands-on experience with executable scripts and practical coding examples Strengthen security and compliance using advanced auditing, SQL Firewall, and blockchain tables Master high-performance techniques for query tuning, in-memory processing, and scalability Revolutionize data access with AI-powered vector search in modern AI workloads Simplify user access in multi-tenant environments using schema privileges and DB_DEVELOPER_ROLE Model and query complex data using JSON-Relational Duality Views and SQL property graphs Who this Book is For Database architects, data engineers, Oracle developers, and IT professionals seeking to leverage Oracle 23AI’s latest features for real-world applications

SAP ABAP 7.5 Optimization for HANA: AMDP, CDS and Native SQL for Peak Performance

In the evolving landscape of SAP development, performance is no longer just a nice-to-have—it's a necessity. With the power of SAP HANA and the enhancements introduced in ABAP 7.5, developers are now equipped to rethink how applications are built, executed, and optimized. This book is your guide to that transformation. We begin by understanding the core shift: moving data-intensive operations directly into the HANA database. When implemented correctly, this "code pushdown" philosophy dramatically reduces data transfer and processing overhead. AMDP (ABAP Managed Database Procedures), our in-database processing engine, enables us to write complex logic directly in SQLScript, harnessing HANA’s parallel processing capabilities. We focus on crafting efficient AMDP procedures by adopting set-based operations and minimizing unnecessary data movement. Next, we explore Core Data Services (CDS) Views, our go-to data modeling tool. CDS Views are not just simple database views; they act as semantic layers that define how our applications interact with data. We learn to create optimized CDS Views by leveraging associations, annotations, and table functions, enabling us to build reusable, high-performance data models. These views simplify complex queries, improve data consistency, and enhance application flexibility. We then turn to Native SQL, our direct line to the HANA database. While AMDP and CDS Views provide powerful abstractions, Native SQL offers ultimate control for specialized tasks. We embed Native SQL within AMDP procedures to access database-specific features and fine-tune performance for critical operations. Along the way, we apply best practices for writing efficient queries, with a strong focus on indexing, join strategies, and precise data filtering. Throughout this journey, we emphasize the importance of rigorous testing and proactive monitoring. Just like a race car undergoes extensive testing before hitting the track, our ABAP applications require careful validation to ensure accuracy and optimal performance. We explore techniques for unit testing AMDP procedures, validating CDS Views, and monitoring query performance. We also look at strategies for detecting and addressing potential bottlenecks before they affect end users. SAP ABAP 7.5 Optimization for HANA is not just about writing faster code—it’s about fundamentally rethinking how we develop applications. By embracing code pushdown, leveraging AMDP, CDS Views, and Native SQL, and implementing robust testing and monitoring strategies, we build ABAP applications that are not only faster, but also more scalable, maintainable, and adaptable to the ever-evolving demands of modern business. You Will: Learn how to implement the "code pushdown" philosophy, moving data-intensive operations directly into the HANA database to reduce data transfer and processing overhead Understand to create optimized CDS Views, leveraging associations, annotations, and table functions to build reusable, high-performance data models that simplify complex queries and improve data consistency. Explore how to write complex logic directly in SQLScript using AMDP, harnessing HANA's parallel processing capabilities, and using Native SQL for specialized tasks, accessing database-specific features to optimize performance. This Book is For: ABAP Developers, SAP Consultants and Architects and IT Managers and Technical Leads

The Data Flow Map: A Practical Guide to Clear and Creative Analytics in Any Data Environment

Unlock the secrets of practical data analysis with the Data Flow Map framework—a game-changing approach that transcends tools and platforms. This book isn’t just another programming manual; it’s a guide to thinking and communicating about data at a higher level. Whether you're working with spreadsheets, databases, or AI-driven models, you'll learn how to express your analytics in clear, common language that anyone can understand. In today’s data-rich world, clarity is the real challenge. Technical details often obscure insights that could drive real impact. The Data Flow Map framework simplifies complexity into three core motions: source, focus, and build. The first half of the book explores these concepts through illustrations and stories. The second half applies them to real-world datasets using tools like Excel, SQL, and Python, showing how the framework works across platforms and use cases. A vital resource for analysts at any level, this book offers a practical, tool-agnostic approach to data analysis. With hands-on examples and a universal mental model, you’ll gain the confidence to tackle any dataset, align your team, and deliver insights that matter. Whether you're a beginner or a seasoned pro, the Data Flow Map framework will transform how you approach data analytics. What You Will Learn Grasp essential elements applicable to every data analysis workflow Adapt quickly to any dataset, tool, or platform Master analytic thinking at a higher level Use analytics patterns to better understand the world Break complex analysis into manageable, repeatable steps Iterate faster to uncover deeper insights and better solutions Communicate findings clearly for better decision-making Who This Book Is For Aspiring data professionals and experienced analysts, from beginners to seasoned data engineers, focused on data collection, analysis, and decision making

For the past decade, SQL has reigned king of the data transformation world, and tools like dbt have formed a cornerstone of the modern data stack. Until recently, Python-first alternatives couldn't compete with the scale and performance of modern SQL. Now Ibis can provide the same benefits of SQL execution with a flexible Python dataframe API.

In this talk, you will learn how Ibis supercharges open-source libraries like Kedro, Pandera, and the Boring Semantic Layer and how you can combine these technologies (and a few more) to build and orchestrate scalable data engineering pipelines without sacrificing the comfort (and other advantages) of Python.

We all mix pictures, emojis and text freely in our communications. So, why not in our code? This session takes a whimsical look at what mixing emoji with Python and SQL might look like (spoiler alert: a lot like those "rebus" stories in Highlights Magazine for Kids!). We'll discuss the benefits of doing so, challenges that emoji present, and demo a rudimentary Python preprocessor that intercepts Python and SQL code containing emojis submitted from Jupyter notebooks and translates it back into text-only code using an emoji-to-text dictionary before passing it on to Python for execution. This session is intended for all levels of programmers.

Extending SQL Databases with Python

What if your database could run Python code inside SQL? In this talk, we’ll explore how to extend popular databases using Python, without needing to write a line of C.

We’ll cover three systems—SQLite, DuckDB, and PostgreSQL—and show how Python can be used in each to build custom SQL functions, accelerate data workflows, and prototype analytical logic. Each database offers a unique integration path: - SQLite and DuckDB allow you to register Python functions directly into SQL via sqlite3.create_function, making it easy to inject business logic or custom transformations. - PostgreSQL offers PL/Python, a full-featured procedural language for writing SQL functions in Python. We’ll also touch on advanced use cases, including embedding the Python interpreter directly into a PostgreSQL extension for deeper integration.

By the end of this talk, you’ll understand the capabilities, limitations, and gotchas of Python-powered extensions in each system—and how to choose the right tool depending on your use case, whether you’re analyzing data, building pipelines, or hacking on your own database.

From Notebook to Pipeline: Hands-On Data Engineering with Python

In this hands-on tutorial, you'll go from a blank notebook to a fully orchestrated data pipeline built entirely in Python, all in under 90 minutes. You'll learn how to design and deploy end-to-end data pipelines using familiar notebook environments, using Python for your data loading, data transformations, and insights delivery.

We'll dive into the Ingestion-Tranformation-Delivery (ITD) framework for building data pipelines: ingest raw data from cloud object storage, transform the data using Python DataFrames, and deliver insights via a Streamlit application.

Basic familiarity with Python (and/or SQL) is helpful, but not required. By the end of the session, you'll understand practical data engineering patterns and leave with reusable code templates to help you build, orchestrate, and deploy data pipelines from notebook environments.

AWS re:Invent 2025 - Cut costs & operate efficiently on Amazon RDS for SQL Server & Oracle (DAT325)

Discover how leading enterprises are leveraging 15+ years of Amazon RDS operational excellence to power their SQL Server and Oracle databases in the cloud. In this session, explore features across Amazon RDS for SQL Server and Oracle that help you achieve substantial cost savings, enhanced scalability, and efficient operations. Through real-world cost optimization techniques and architectural best practices, learn how organizations are reducing operational overhead and costs while improving availability, scalability, and performance.

Learn more: More AWS events: https://go.aws/3kss9CP

Subscribe: More AWS videos: http://bit.ly/2O3zS75 More AWS events videos: http://bit.ly/316g9t4

ABOUT AWS: Amazon Web Services (AWS) hosts events, both online and in-person, bringing the cloud computing community together to connect, collaborate, and learn from AWS experts. AWS is the world's most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster.

AWSreInvent #AWSreInvent2025 #AWS

AWS re:Invent 2025 - Accelerating data engineering with AI Agents for AWS Analytics (ANT215)

Data engineers face critical time sinks: writing code to build analytics pipelines from scratch and upgrading Apache Spark versions. In this lightning talk, discover how AWS is addressing both challenges with AI agents that accelerate development cycles. Learn how the Amazon SageMaker Data Agent transforms natural language instructions into executable SQL and Python code within SageMaker notebooks, maintaining full context awareness of your data sources and schemas. Then explore the Apache Spark upgrade agent, which accelerates complex multi-month upgrade projects into week-long initiatives through automated code analysis and transformation. Walk away understanding how these agents work to automate manual work from your data engineering workflows, whether you're building new applications or modernizing existing ones.

Learn more: More AWS events: https://go.aws/3kss9CP

Subscribe: More AWS videos: http://bit.ly/2O3zS75 More AWS events videos: http://bit.ly/316g9t4

ABOUT AWS: Amazon Web Services (AWS) hosts events, both online and in-person, bringing the cloud computing community together to connect, collaborate, and learn from AWS experts. AWS is the world's most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster.

AWSreInvent #AWSreInvent2025 #AWS