As AI continues to transform industries, the applications of generative AI and Large Language Models (LLMs) are becoming increasingly significant. This session delves into the utility of these models across various sectors. Gain an understanding of how to use LLMs, embeddings, vector datastores, and their indexing techniques to create search solutions for enhanced user experiences and improved outcomes on AWS using Amazon Bedrock, Aurora, and LangChain. By the end of this session, participants will be equipped with the knowledge to harness the power of LLMs and vector databases, paving the way for the development of innovative search solutions on AWS.

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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.

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In "Data Engineering with Databricks Cookbook," you'll learn how to efficiently build and manage data pipelines using Apache Spark, Delta Lake, and Databricks. This recipe-based guide offers techniques to transform, optimize, and orchestrate your data workflows. What this Book will help me do Master Apache Spark for data ingestion, transformation, and analysis. Learn to optimize data processing and improve query performance with Delta Lake. Manage streaming data processing with Spark Structured Streaming capabilities. Implement DataOps and DevOps workflows tailored for Databricks. Enforce data governance policies using Unity Catalog for scalable solutions. Author(s) Pulkit Chadha, the author of this book, is a Senior Solutions Architect at Databricks. With extensive experience in data engineering and big data applications, he brings practical insights into implementing modern data solutions. His educational writings focus on empowering data professionals with actionable knowledge. Who is it for? This book is ideal for data engineers, data scientists, and analysts who want to deepen their knowledge in managing and transforming large datasets. Readers should have an intermediate understanding of SQL, Python programming, and basic data architecture concepts. It is especially well-suited for professionals working with Databricks or similar cloud-based data platforms.

In today's fast-paced digital world, managing IT operations is more complex than ever. With the rise of cloud services, microservices, and constant software deployments, the pressure on IT teams to keep everything running smoothly is immense. But how do you keep up with the ever-growing flood of data and ensure your systems are always available? AIOps is the use of artificial intelligence to automate and scale IT operations. But what exactly is AIOps, and how can it transform your IT operations? Assaf Resnick is the CEO and Co-Founder of BigPanda. Before founding BigPanda, Assaf was an investor at Sequoia Capital, where he focused on early and growth-stage investing in software, internet, and mobile sectors. Assaf’s time at Sequoia gave him a front-row seat to the challenges of IT scale, complexity, and velocity faced by Operations teams in rapidly scaling and accelerating organizations. This is the problem that Assaf founded BigPanda to solve. In the episode, Richie and Assaf explore AIOps, how AIOps helps manage increasingly complex IT operations, how AIOps differs from DevOps and MLOps, examples of AIOps projects, a real world application of AIOps, the key benefits of AIOps, how to implement AIOps, excitement in the space, how GenAI is improving AIOps and much more.  Links Mentioned in the Show: BigPandaGartner: Market Guide for AIOps Platforms[Course] Implementing AI Solutions in BusinessRelated Episode: Adding AI to the Data Warehouse with Sridhar Ramaswamy, CEO at SnowflakeSign up to RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business

The Ultimate Guide to Snowpark serves as a comprehensive resource to help you master the Snowflake Snowpark framework using Python. You'll learn how to manage data engineering, data science, and data applications in Snowpark, coupled with practical implementations and examples. By following this guide, you'll gain the skills needed to efficiently process and analyze data in the Snowflake Data Cloud. What this Book will help me do Master Snowpark with Python for data engineering, data science, and data application workloads. Develop and deploy robust data pipelines using Snowpark in Python. Design, implement, and produce machine learning models using Snowpark. Learn to monetize and operationalize Snowflake-native applications. Effectively adopt Snowpark in production for scalable, efficient data solutions. Author(s) Shankar Narayanan SGS and Vivekanandan SS are experienced professionals in data engineering and Snowflake technologies. Shankar has extensive experience in utilizing Snowflake Snowpark to manage and enhance data solutions. Vivekanandan brings expertise in the intersection of Python programming and cloud-based data processing. Together, their combined knowledge and approachable writing style make this book an invaluable resource to readers. Who is it for? This book is designed for data engineers, data scientists, developers, and seasoned data practitioners. Ideal candidates are those looking to expand their skills in implementing Snowpark solutions using Python. A prior understanding of SQL, Python programming, and familiarity with Snowflake is beneficial for readers to fully leverage the techniques presented.

This project-oriented book presents a hands-on approach to identifying migration and performance issues with experience drawn from real-world examples. As you work through the book, you will develop skills, knowledge, and deep understanding of Snowflake tuning options and capabilities while preparing for later incorporation of additional Snowflake features as they become available. Your Snowflake platform will cost less to run and will improve your customer experience. Written by a seasoned Snowflake practitioner, this book is full of practical, hands-on guidance and advice specifically designed to further accelerate your Snowflake journey. Tuning the Snowflake Data Cloud provides you a pathway to success by equipping you with the skills, knowledge, and expertise needed to elevate your Snowflake experience. The book shows you how to leverage what you already know, adds what you don’t, and helps you apply it toward delivering for your Snowflake accounts. Read this book to embark on a voyage of advancement and equip your organization to deliver consistent Snowflake performance. What You Will Learn Recognize and understand the root cause of performance bottlenecks Know how to resolve performance issues Develop a deep understanding of Snowflake performance tuning options Reduce expensive mistakes, remediate poorly performing code Manage Snowflake costs

Summary

Any software system that survives long enough will require some form of migration or evolution. When that system is responsible for the data layer the process becomes more challenging. Sriram Panyam has been involved in several projects that required migration of large volumes of data in high traffic environments. In this episode he shares some of the valuable lessons that he learned about how to make those projects successful.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst is an end-to-end data lakehouse platform built on Trino, the query engine Apache Iceberg was designed for, with complete support for all table formats including Apache Iceberg, Hive, and Delta Lake. Trusted by teams of all sizes, including Comcast and Doordash. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. This episode is supported by Code Comments, an original podcast from Red Hat. As someone who listens to the Data Engineering Podcast, you know that the road from tool selection to production readiness is anything but smooth or straight. In Code Comments, host Jamie Parker, Red Hatter and experienced engineer, shares the journey of technologists from across the industry and their hard-won lessons in implementing new technologies. I listened to the recent episode "Transforming Your Database" and appreciated the valuable advice on how to approach the selection and integration of new databases in applications and the impact on team dynamics. There are 3 seasons of great episodes and new ones landing everywhere you listen to podcasts. Search for "Code Commentst" in your podcast player or go to dataengineeringpodcast.com/codecomments today to subscribe. My thanks to the team at Code Comments for their support. Your host is Tobias Macey and today I'm interviewing Sriram Panyam about his experiences conducting large scale data migrations and the useful strategies that he learned in the process

Interview

Introduction How did you get involved in the area of data management? Can you start by sharing some of your experiences with data migration projects?

As you have gone through successive migration projects, how has that influenced the ways that you think about architecting data systems?

How would you categorize the different types and motivations of migrations?

How does the motivation for a migration influence the ways that you plan for and execute that work?

Can you talk us through one or two specific projects that you have taken part in? Part 1: The Triggers

Section 1: Technical Limitations triggering Data Migration

Scaling bottlenecks: Performance issues with databases, storage, or network infrastructure Legacy compatibility: Difficulties integrating with modern tools and cloud platforms System upgrades: The need to migrate data during major software changes (e.g., SQL Server version upgrade)

Section 2: Types of Migrations for Infrastructure Focus

Storage migration: Moving data between systems (HDD to SSD, SAN to NAS, etc.) Data center migration: Physical relocation or consolidation of data centers Virtualization migration: Moving from physical servers to virtual machines (or vice versa)

Section 3: Technical Decisions Driving Data Migrations

End-of-life support: Forced migration when older software or hardware is sunsetted Security and compliance: Adopting new platforms with better security postures Cost Optimization: Potential savings of cloud vs. on-premise data centers

Part 2: Challenges (and Anxieties)

Section 1: Technical Challenges

Data transformation challenges: Schema changes, complex data mappings Network bandwidth and latency: Transferring large datasets efficiently Performance tes

Welcome back to another podcast episode of Data Unchained! This is the first episode of our in person recordings at the NAB 2024 trade show in Las Vegas. Kicking off this series is, Partner Solution Architect at HP, Stephen Moore. Stephen talks with us about how remote access solutions, working on data at the edge, and future trends he would like to see involving the cloud. Flame Demo: https://youtu.be/gskRg7IYdw8?si=ILT-CmWFf7_9_3dN

data #podcast #datascience #datasets #ai #artificialintelligence #episode #datagovernance #datastorage #dataanalytics

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This book is your gateway to mastering the skills required for achieving the Azure Data Engineer Associate certification (DP-203). Whether you're new to the field or a seasoned professional, it comprehensively prepares you for the challenges of the exam. Learn to design and implement advanced data solutions, secure sensitive information, and optimize data processes effectively. What this Book will help me do Understand and utilize Azure's data services such as Azure Synapse and Azure Databricks for data processing. Master advanced data storage and management solutions, including designing partitions and lake architectures. Learn to secure data with state-of-the-art tools like RBAC, encryption, and Azure Purview. Develop and manage data pipelines and workflows using tools like Azure Data Factory (ADF) and Spark. Prepare for and confidently pass the DP-203 certification exam with the included practical resources and guidance. Author(s) The authors, None Palmieri, Surendra Mettapalli, and None Alex, bring a wealth of expertise in cloud and data engineering. With extensive industry experience, they've designed this guide to be both educational and practical, enabling learners to not only understand but also apply concepts in real-world scenarios. Their goal is to make complex topics approachable, supporting your journey to certification success. Who is it for? This guide is perfect for aspiring and current data engineers aiming to achieve the Azure Data Engineer Associate certification (DP-203). It's particularly useful for professionals familiar with cloud services and basic data engineering concepts who want to delve deeper into Azure's offerings. Additionally, managers and learners preparing for roles involving Azure cloud data solutions will find the content invaluable for career advancement.

Any time a Netflix member sits down, reclines in their chair and turns on their TV to Netflix, there's a moment of truth. It's an opportunity to deliver a spectacular service with amazing quality of experience. Misses, errors, or high latency that prevent individuals from streaming, as a result of ISP configuration changes, code deployment, or catastrophic fallback, result in an impact on how our service is perceived. This talk will go over how we measure the quality of experience for our members and how we work to develop new metrics when we have additional offerings like live streaming and cloud gaming.

Kirk continues the conversation with Daniel Crosby, founder and CEO of Legend Energy Advisors, as they highlight the challenges and opportunities in infrastructure projects, power generation, and data centers, and stress the need for a deep understanding of the energy ecosystem. Their discussion delves into the role of data centers in driving change in power consumption, emphasizing the importance of educating consumers on energy usage and transparent pricing signals. With the importance of understanding gas dynamics, leveraging real-time data, and upcoming industry shifts due to AI and cloud adoption, they underscore the interconnected nature of industries.

For more about us: https://linktr.ee/overwatchmissioncritical

This IBM® Redbooks® publication describes the new member of the IBM Z® family, IBM z14™. IBM z14 is the trusted enterprise platform for pervasive encryption, integrating data, transactions, and insights into the data. A data-centric infrastructure must always be available with a 99.999% or better availability, have flawless data integrity, and be secured from misuse. It also must be an integrated infrastructure that can support new applications. Finally, it must have integrated capabilities that can provide new mobile capabilities with real-time analytics that are delivered by a secure cloud infrastructure. IBM z14 servers are designed with improved scalability, performance, security, resiliency, availability, and virtualization. The superscalar design allows z14 servers to deliver a record level of capacity over the prior IBM Z platforms. In its maximum configuration, z14 is powered by up to 170 client characterizable microprocessors (cores) running at 5.2 GHz. This configuration can run more than 146,000 million instructions per second (MIPS) and up to 32 TB of client memory. The IBM z14 Model M05 is estimated to provide up to 35% more total system capacity than the IBM z13® Model NE1. This Redbooks publication provides information about IBM z14 and its functions, features, and associated software support. More information is offered in areas that are relevant to technical planning. It is intended for systems engineers, consultants, planners, and anyone who wants to understand the IBM Z servers functions and plan for their usage. It is intended as an introduction to mainframes. Readers are expected to be generally familiar with existing IBM Z technology and terminology.

This IBM® Redbooks® publication describes the new member of the IBM Z® family, IBM z14™ Model ZR1 (Machine Type 3907). It includes information about the Z environment and how it helps integrate data and transactions more securely, and can infuse insight for faster and more accurate business decisions. The z14 ZR1 is a state-of-the-art data and transaction system that delivers advanced capabilities, which are vital to any digital transformation. The z14 ZR1 is designed for enhanced modularity, in an industry standard footprint. A data-centric infrastructure must always be available with a 99.999% or better availability, have flawless data integrity, and be secured from misuse. It also must be an integrated infrastructure that can support new applications. Finally, it must have integrated capabilities that can provide new mobile capabilities with real-time analytics that are delivered by a secure cloud infrastructure. IBM z14 ZR1 servers are designed with improved scalability, performance, security, resiliency, availability, and virtualization. The superscalar design allows z14 ZR1 servers to deliver a record level of capacity over the previous IBM Z platforms. In its maximum configuration, z14 ZR1 is powered by up to 30 client characterizable microprocessors (cores) running at 4.5 GHz. This configuration can run more than 29,000 million instructions per second and up to 8 TB of client memory. The IBM z14 Model ZR1 is estimated to provide up to 54% more total system capacity than the IBM z13s® Model N20. This Redbooks publication provides information about IBM z14 ZR1 and its functions, features, and associated software support. More information is offered in areas that are relevant to technical planning. It is intended for systems engineers, consultants, planners, and anyone who wants to understand the IBM Z servers functions and plan for their usage. It is intended as an introduction to mainframes. Readers are expected to be generally familiar with IBM Z technology and terminology.

This IBM® Redbooks® publication describes the features and functions the latest member of the IBM Z® platform, the IBM z15™ (machine type 8561). It includes information about the IBM z15 processor design, I/O innovations, security features, and supported operating systems. The z15 is a state-of-the-art data and transaction system that delivers advanced capabilities, which are vital to any digital transformation. The z15 is designed for enhanced modularity, which is in an industry standard footprint. This system excels at the following tasks: Making use of multicloud integration services Securing data with pervasive encryption Accelerating digital transformation with agile service delivery Transforming a transactional platform into a data powerhouse Getting more out of the platform with IT Operational Analytics Accelerating digital transformation with agile service delivery Revolutionizing business processes Blending open source and Z technologies This book explains how this system uses new innovations and traditional Z strengths to satisfy growing demand for cloud, analytics, and open source technologies. With the z15 as the base, applications can run in a trusted, reliable, and secure environment that improves operations and lessens business risk.

Today, we’re joined by Mike Palmer, Chief Executive Officer at Sigma, the only cloud analytics solution with a spreadsheet-like interface enabling anyone to explore data at cloud scale and speed. We talk about:  Creating a product for the average person to useIf the dashboard will be replaced by an AI promptDisruption of SaaS in the march toward cloud adoptionDo we have too many SaaS products in the market today?How technology always starts with an expert and ends democratized

This IBM® Redbooks® publication describes the features and functions the latest member of the IBM Z® platform, the IBM z15™ Model T02 (machine type 8562). It includes information about the IBM z15 processor design, I/O innovations, security features, and supported operating systems. The z15 is a state-of-the-art data and transaction system that delivers advanced capabilities, which are vital to any digital transformation. The z15 is designed for enhanced modularity, which is in an industry standard footprint. This system excels at the following tasks: Making use of multicloud integration services Securing data with pervasive encryption Accelerating digital transformation with agile service delivery Transforming a transactional platform into a data powerhouse Getting more out of the platform with IT Operational Analytics Accelerating digital transformation with agile service delivery Revolutionizing business processes Blending open source and Z technologies This book explains how this system uses new innovations and traditional Z strengths to satisfy growing demand for cloud, analytics, and open source technologies. With the z15 as the base, applications can run in a trusted, reliable, and secure environment that improves operations and lessens business risk.

The surging predictive analytics market is expected to grow from $10.5 billion today to $28 billion by 2026. With the rise in automation across industries, the increase in data-driven decision-making, and the proliferation of IoT devices, predictive analytics has become an operational necessity in today's forward-thinking companies. If you're a data professional, you need to be aligned with your company's business activities more than ever before. This practical book provides the background, tools, and best practices necessary to help you design, implement, and operationalize predictive analytics on-premises or in the cloud. Explore ways that predictive analytics can provide direct input back to your business Understand mathematical tools commonly used in predictive analytics Learn the development frameworks used in predictive analytics applications Appreciate the role of predictive analytics in the machine learning process Examine industry implementations of predictive analytics Build, train, and retrain predictive models using Python and TensorFlow

Welcome to another episode of the Data Unchained podcast! On this episode, Collin Graves, Founder and CEO of North Labs, joins us to talk about his journey in the data industry, how he and his company are using AI to unlock valuable data that was previously stored away in massive amounts, and his perspective on the cloud. https://www.northlabs.io/

data #datascience #datasets #podcast #archives #ai #artificialintelligence #datastorage #episode

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Safiyy Momen and I chat about the good and bad of the Modern Data Stack, controlling cloud costs, boring engineering, and much more.

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Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that flow like your morning coffee, where industry insights meet laid-back banter. Whether you're a data aficionado or just curious about the digital age, pull up a chair and let's explore the heart of data, unplugged style!

Stack Overflow and OpenAI Deal Controversy: Discussing the partnership controversy, with users protesting the lack of an opt-out option and how this could reshape the platform. Look into Phind here.Apple and OpenAI Rumors - could ChatGPT be the new Siri? Examining rumors of ChatGPT potentially replacing Siri, and Apple's AI strategy compared to Microsoft’s MAI-1. Check out more community opinions here.Hello GPT-4o: Exploring the new era with OpenAI's GPT-4o that blends video, text, and audio for more dynamic human-AI interactions. Discussing AI's challenges under the European AI Act and chatgpt’s use in daily life and dating apps like Bumble.Claude Takes Europe: Claude 3 now available in the EU. How does it compare to ChatGPT in coding and conversation?ElevenLabs' Music Generation AI: A look at ElevenLabs' AI for generating music and the broader AI music landscape. How are these algorithms transforming music creation? Check out the AI Song Contest here.Google Cloud’s Big Oops with UniSuper: Unpack the shocking story of how Google Cloud accidentally wiped out UniSuper’s account. What does this mean for data security and redundancy strategies?The Great CLI Debate: Is Python really the right choice for CLI tools? We spark the debate over Python vs. Go and Rust in building efficient CLI tools.

Rapid change seems to be the new norm within the data and AI space, and due to the ecosystem constantly changing, it can be tricky to keep up. Fortunately, any self-respecting venture capitalist looking into data and AI will stay on top of what’s changing and where the next big breakthroughs are likely to come from. We all want to know which important trends are emerging and how we can take advantage of them, so why not learn from a leading VC.  Tomasz Tunguz is a General Partner at Theory Ventures, a $235m early-stage venture capital firm. He blogs sat tomtunguz.com & co-authored Winning with Data. He has worked or works with Looker, Kustomer, Monte Carlo, Dremio, Omni, Hex, Spot, Arbitrum, Sui & many others. He was previously the product manager for Google's social media monetization team, including the Google-MySpace partnership, and managed the launches of AdSense into six new markets in Europe and Asia. Before Google, Tunguz developed systems for the Department of Homeland Security at Appian Corporation.  In the episode, Richie and Tom explore trends in generative AI, the impact of AI on professional fields, cloud+local hybrid workflows, data security, and changes in data warehousing through the use of integrated AI tools, the future of business intelligence and data analytics, the challenges and opportunities surrounding AI in the corporate sector. You'll also get to discover Tom's picks for the hottest new data startups. Links Mentioned in the Show: Tom’s BlogTheory VenturesArticle: What Air Canada Lost In ‘Remarkable’ Lying AI Chatbot Case[Course] Implementing AI Solutions in BusinessRelated Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision ScientistSign up to RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business