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

Streamline your workflow with ESQL enhance data analysis with real-time insights, and speed up aggregations and visualizations Key Features Apply ESQL efficiently in analytics, observability, and cybersecurity Optimize performance and scalability for high-demand environments Discover how to visualize and debug ESQL queries Purchase of the print or Kindle book includes a free PDF eBook Book Description Built to simplify high-scale data analytics in Elasticsearch, this practical guide will take you from foundational concepts to advanced applications across search, observability, and security. It will help you overcome common challenges such as efficiently querying large datasets, applying advanced analytics without deep prior knowledge, and resolving for a unique and consolidated query language. Written by senior experts at Elastic with extensive field experience, this book delivers actionable guidance rooted in solving today’s data challenges at scale. After introducing ESQL and its architecture, the chapters explore real-world applications across various domains, including analytics, raw log analysis, observability, and cybersecurity. Advanced topics such as scaling, optimization, and future developments are also covered to help you maximize your ESQL capabilities. By the end of this book, you’ll be able to leverage ESQL for comprehensive data management and analysis, optimizing your workflows and enhancing your productivity with Elasticsearch. What you will learn Gain a solid understanding of ESQL and its architecture Use ESQL for data analysis and performance monitoring Apply ESQL in cybersecurity for threat detection and incident response Find out how to perform advanced searches using ESQL Prepare for future ESQL developments Showcase ESQL in action through real-world, persona-driven use cases Who this book is for If you’re an Elasticsearch user, this book is essential for your growth. Whether you’re a data analyst looking to build analytics on top of Elasticsearch, an SRE monitoring the health of your IT system, or a cybersecurity analyst, this book will give you a complete understanding of how ESQL is built and used. Additionally, database administrators, business intelligence professionals, and operational intelligence professionals will find this book invaluable. Even with a beginner-level knowledge of Elasticsearch, you’ll be able to get started and make the most of this comprehensive guide.

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.

We are at the start of a massive, AI-driven feedback loop. A loop between a universal language, Python, a universal engine, Spark, and universal storage, Open Table Formats, that will accelerate us from simple automation to fully agentic, automated data management. This session helps D&A leaders assess their strategy for navigating this disruptive transition and its opportunities and risks.

D&A leaders have a key strategic decision to make over the next few years. What does their strategic and long-term data management platform looks like and where to source it from? There are four options that this session will discuss: utilizing the all encompassing data and AI platform from their cloud service providers, extending their ISV solution providers to enable their data platform, engaging their enterprise SaaS application providers to support D&A use cases, or taking a blended approach.

In this roundtable, D&A leaders will discuss how they are balancing the centralization and decentralization of data management, empowering LOBs to achieve self-sufficiency while leveraging the benefits of a centralized data management function. They will also address how to maintain control over local initiatives and prevent the spread of risky patterns that are misaligned with governance policies.

In this session, two experts will have a dynamic dialogue presenting how federation and self-service are reshaping data management. Together, they’ll debate the risks and rewards of centralization and decentralization, exploring how, through federation and self-service, organizations can balance control with empowerment, and offer an actionable strategy for navigating the evolving landscape of data management.

D&A leaders must develop DataOps as an essential practice to redefine their data management operations. This involves establishing business value before pursuing significant data engineering initiatives, and preventing duplicated efforts undertaken by different teams in managing the common metadata, security and observability of information assets within the data platforms.

In this roundtable, D&A leaders will discuss how they are balancing the centralization and decentralization of data management, empowering LOBs to achieve self-sufficiency while leveraging the benefits of a centralized data management function. They will also address how to maintain control over local initiatives and prevent the spread of risky patterns that are misaligned with governance policies.

D&A leaders struggle to prioritize and justify data management spend, especially amid cloud-driven cost unpredictability. Value stream analysis links data production to direct and indirect business outcomes, driving quantifiable benefits. This session will link core research on cost management and FinOps with a means of using active metadata to measure value resulting in holistic cost optimization.

In this session, you’ll explore a reference architecture that serves as a blueprint for future-proofing your data and analytics environment. Through practical, step-by-step guidance, you’ll learn how to align your technology stack with business objectives — whether you're modernizing an existing architecture or building one from the ground up.

Almost every GenAI use case requires organizations to extract, qualify and govern significant volumes of unstructured data. Data management leaders must deliver workflows that orchestrate entity extraction, vector data embeddings and semantic data enrichment with structured data pipelines to deliver GenAI-ready data. Join this session to learn more.

This session details how Agentic AI will impact existing data management architecture and technologies, which new use cases it enables in data management and engineering, and which skills will be needed or become obsolete. We’ll also cover how to prepare budgets, teams, and operating models for these changes. These are now valid, frequently debated questions as Agentic AI evolves.

In this session, you’ll explore a Lakehouse architecture which is becoming the cornerstone of modern data management initiatives. Through practical, step-by-step guidance, you’ll gain clarity on the purposes of data zones and the design principles of the Lakehouse architecture.

Urgent investments in AI-ready data and operational use cases have put the spotlight on foundational data management. The Data Fabric has emerged as a long-term data management architecture that you should now pursue for sustained data, analytics, and AI success. This session will help participants understand what data fabrics are and their implications for your data architecture. It will also address how to build and where to buy data fabrics.

Data quality and data observability tools provide significant capabilities to ensure good data for your BI and AI. Data observability tools give organizations integrated visibility into the health of their data, data pipelines and data landscape. Data quality tools enable business users to manage data at its sources by setting rules and policies. Together, these tools help organizations build a strong foundation in data management for BI and AI initiatives.

Data management platforms emerge through the convergence of several individual data management capabilities. D&A leaders keen on data platform modernization should join this breakout session to learn about the dynamics of this emerging market and the benefits of reducing architectural silos to meet data demands for both current and innovative use cases.

This session will explore the challenges faced by data management leaders and offer actionable recommendations to address them. By understanding and overcoming these obstacles, leaders can optimize their data management strategies and drive more informed decision making.

Unlock the potential of your BIM workflows with artificial intelligence and data visualization tools. This book provides guided instruction on using software like Revit, Dynamo, Python, and Power BI to automate processes, derive insights, and craft tailored dashboards that empower data-driven decisions in AEC projects. What this Book will help me do Effectively preprocess and manage BIM data for analysis and visualization. Design interactive and insightful dashboards in Power BI for project stakeholders. Integrate real-time IoT data and advanced analytics into BIM projects. Automate repetitive tasks in Revit using Dynamo and Python scripting. Understand the ethical considerations and emerging trends in AI for BIM. Author(s) Bruno Martorelli, a seasoned BIM manager, specializes in integrating technology and data analytics into construction workflows. With a background in architecture and programming, he bridges the gap between traditional methods and modern innovations. Bruno is dedicated to sharing practical strategies for data automation and visualization. Who is it for? This book is tailored for architects, engineers, and construction managers interested in elevating their BIM practices. If you're familiar with Revit and possess a basic understanding of data management, you'll find this resource invaluable. Beginners in Python or Power BI will also find accessible guidance to start applying advanced techniques in their workflows.

In this Supercomputing edition of Data Unchained, host Molly Presley is joined live from the St. Louis Convention Center by Ari Berman, former Founder and CEO of Fireteam and current member of the Starfish team. The conversation explores the growing convergence of high performance computing, AI, and large scale data management, with a focus on unstructured data visibility, global file systems, and shared data stewardship across science, life sciences, and enterprise environments. Ari and Molly discuss why knowing what data you have is foundational to innovation, how organizations can reduce silos, and how platforms like Starfish and Hammerspace work together to enable discovery, collaboration, and smarter use of data at scale. Cyberpunk by jiglr | https://soundcloud.com/jiglrmusic Music promoted by https://www.free-stock-music.com Creative Commons Attribution 3.0 Unported License https://creativecommons.org/licenses/by/3.0/deed.en_US Hosted on Acast. See acast.com/privacy for more information.