{ "sessions": [ { "id": "518281", "title": "Data Contracts: Data Quality for AI", "description": "In this session, we'll dive into the world of data contracts - API-based agreements between data producers and consumers that capture the schema, semantics, distribution, and enforcement policies of the data. Learn how data contracts provide a single surface for collaboration on data in a shared language, allow the data model to evolve in an agile, iterative way, and apply data governance incrementally where it's needed for AI and ML systems. We'll explore how organizations can leverage data contracts to ensure Artificial Intelligence systems are trained on trustworthy and well-governed datasets.", "startsAt": "2023-08-29T09:10:00", "endsAt": "2023-08-29T09:50:00", "isServiceSession": false, "isPlenumSession": false, "speakers": [ "37e3ce13-b8a4-4927-b151-757f5d7de7c6" ], "categoryItems": [], "questionAnswers": [], "roomId": 34373, "liveUrl": null, "recordingUrl": null, "status": "Accepted", "isInformed": true, "isConfirmed": false }, { "id": "518126", "title": "How The Full-Stack Data Scientist Is STILL The Sexiest Job", "description": "When MLOps first began to emerge, the promise was to increase innovation by minimizing or automating the toilsome work of data scientists.\r\n\r\nAnd yet, the MLOps tooling ecosystem remains fragmented. \r\n\r\nCompanies just starting on their journeys to becoming ML-native or ML-fluent plan their roadmaps based on ill-fitting MLOps maturity models that don't account for their particular organizational goals or trajectory, while leaving data scientists out of the loop. \r\n\r\nToss in the the hype and (\"seemingly\" widespread) adoption of LLM’s and GenAI applications as well as debates about the existence of \"prompt engineers\" and it would seem like the data scientist role is headed for an extinction event.\r\n\r\nMy goals for this talk are to:\r\n- Illustrate the current state of the MLOps landscape;\r\n- Share my experiences from both sides of the fence as a former data scientist and a MLOps engineer working for companies like Autodesk, Teladoc, and Mailchimp;\r\n- Illustrate the common pain-points by companies around production ML;\r\n- Show how the future will favor the builders, regardless of whether they're \"AI Engineers\", \"full-stack data scientists\", or \"ML Engineers\". ", "startsAt": "2023-08-29T09:50:00", "endsAt": "2023-08-29T10:30:00", "isServiceSession": false, "isPlenumSession": false, "speakers": [ "5b362fba-1112-4b0a-a545-45e48d00992b" ], "categoryItems": [], "questionAnswers": [], "roomId": 34373, "liveUrl": null, "recordingUrl": null, "status": "Accepted", "isInformed": true, "isConfirmed": true }, { "id": "483101", "title": "Spark Fails to Spark: Common Anti-Patterns and How to Avoid Them", "description": "Apache Spark has become a popular choice for big data processing due to its speed, scalability, and ease of use. However, despite its many powerful features, Spark can also present several challenges, particularly to developers new to distributed computing.\r\n\r\nIn this session, we will explore the most common anti-patterns and pitfalls associated with Spark, and discuss best practices for avoiding them. We will cover issues related to performance, scalability, memory management, and more. Through real-world examples and practical tips, attendees will learn how to anticipate and overcome common challenges, and achieve optimal performance and reliability in their Spark applications.\r\n\r\nWhether you're a seasoned Spark user or just getting started, this session will provide valuable insights into the best practices for avoiding anti-patterns and common pitfalls in Spark. Attendees will leave with a better understanding of how to optimize their Spark applications and achieve more efficient and reliable data processing.", "startsAt": "2023-08-29T11:10:00", "endsAt": "2023-08-29T11:40:00", "isServiceSession": false, "isPlenumSession": false, "speakers": [ "f41aac40-7113-4d2b-991b-9b05b4285b79" ], "categoryItems": [], "questionAnswers": [], "roomId": 34373, "liveUrl": null, "recordingUrl": null, "status": "Accepted", "isInformed": true, "isConfirmed": true }, { "id": "513248", "title": "First Look at Microsoft Fabric, the price of convenience", "description": "Microsoft Fabric is basically a Full data stack offering, one platform to rule them all. As an early adopter, I will try to share my experience where it works well and less well and why you will pay a premium for convenience.", "startsAt": "2023-08-29T11:40:00", "endsAt": "2023-08-29T12:10:00", "isServiceSession": false, "isPlenumSession": false, "speakers": [ "657780d8-ad47-4a1a-b48f-16569b4a1537" ], "categoryItems": [], "questionAnswers": [], "roomId": 34373, "liveUrl": null, "recordingUrl": null, "status": "Accepted", "isInformed": true, "isConfirmed": true }, { "id": "498448", "title": "Practical accessibility testing for data visualisations", "description": "Since the pandemic we've seen an uptick of charts and data visualisation shared by news, government, and health organisations. Most COVID-19 data was presented as images on social media without alternative text-descriptions, or as interactive charts that were often too complicated to understand, or didn't work with increased font size, keyboards, screen readers, and other assistive devices and settings. \r\n\r\nBecause of this, many people with disabilities or varying levels of education and data literacy were excluded from receiving important health information—discrimination with fatal consequences.\r\n\r\nWhen we think about data visualisation accessibility, we often think \"colour blindness\", but what about total blindness? What about using the chart without using a mouse? What about different zoom-levels, font-size and screen widths?\r\n\r\nIn software, our applications are becoming increasingly data-driven, with lots of great JavaScript libraries that produce beautiful charts—currently (May 2023), however, almost all of them have critical accessibility issues. \r\n\r\nIn this session we'll test some data visualisations with keyboard, screen reader and other accessibility testing tools. We'll also briefly cover alt-text for static images and data visualisations, JS chart library recommendations, and where to learn more.", "startsAt": "2023-08-29T12:10:00", "endsAt": "2023-08-29T12:40:00", "isServiceSession": false, "isPlenumSession": false, "speakers": [ "b53ab5be-de5c-40ff-8fc7-ea254f8718ec" ], "categoryItems": [], "questionAnswers": [], "roomId": 34373, "liveUrl": null, "recordingUrl": null, "status": "Accepted", "isInformed": true, "isConfirmed": false }, { "id": "486026", "title": "Lessons from reluctant data engineering", "description": "As we all know, solid data engineering is essential to the success of data science and AI applications. And yet, people often get excited about fancy machine learning models and neglect the data engineering layer. This is totally understandable: playing with data in a throwaway notebook is more relaxing than dealing with a data pipeline that keeps finding ways to break in production.\r\n\r\nIn this talk, I'll share lessons on data engineering from a data science perspective. Everywhere I've worked, from small start-ups to established companies, I've found that I had to do some data engineering if I wanted my work to ever get to production. While I've always been reluctant to do too much of it, my engineering background has placed me in a better position to do it than colleagues who started off as analysts and academics.\r\n\r\nYou could call my work full-stack data science, reluctant data engineering, or some other data & AI thing. Whatever it is, I hope that my talk will help us all play better with each other, across all layers of the data stack.", "startsAt": "2023-08-29T13:40:00", "endsAt": "2023-08-29T14:10:00", "isServiceSession": false, "isPlenumSession": false, "speakers": [ "d273b766-ecc8-4377-aca3-f9c92a6d9110" ], "categoryItems": [], "questionAnswers": [], "roomId": 34373, "liveUrl": null, "recordingUrl": null, "status": "Accepted", "isInformed": true, "isConfirmed": true }, { "id": "516894", "title": "Data Modeling is Dead! Long Live Data Modeling!", "description": "Data modeling is on life support. Some say it’s dead. The traditional practices are increasingly ignored and forgotten. The result is often a loss of structure and a shared understanding of business rules and vocabulary.\r\n\r\nAt the same time, data modeling is more critical than ever. With AI's rising popularity, many organizations rush to incorporate it into their infrastructure. Without consideration of the underlying data framework, the result will be unpleasant for many organizations.\r\n\r\nIn this talk, I argue that data modeling is a key enabler for success with AI. We must return to basics and revamp data modeling to work with modern business workflows and technologies. Long live data modeling!", "startsAt": "2023-08-29T14:10:00", "endsAt": "2023-08-29T14:40:00", "isServiceSession": false, "isPlenumSession": false, "speakers": [ "4f52399d-37c0-4b2b-8507-4d9fe56f185d" ], "categoryItems": [], "questionAnswers": [], "roomId": 34373, "liveUrl": null, "recordingUrl": null, "status": "Accepted", "isInformed": true, "isConfirmed": false }, { "id": "518283", "title": "Duck, duck, Go! Hybrid SQL Execution with DuckDB and MotherDuck", "description": "It's time to re-think the mainframe model of centralized cloud warehouses which require complicated horizontal scaling on shared infrastructure to achieve performance.\r\n\r\nDuckDB is a lightweight SQL OLAP engine that can run in a browser on your laptop via WASM, in a local python app, and in the cloud.\r\n\r\nThis talk will describe the origin and properties of DuckDB which caused it to take the internet by storm and then dive into the ways these can be used to create a powerful hybrid local+cloud data development environment.\r\n\r\nThis hybrid development model often has your data warehouse sitting in the cloud, but enables it to be joined against local sources in parquet, CSV or pandas DataFrames. Results can be cached and further filtered inside the web browser, using the same SQL dialect and engine. After exploratory analysis, you can create a new cloud branch of your data shared with your colleagues, allowing them to reproduce and expand upon your work.", "startsAt": "2023-08-29T14:40:00", "endsAt": "2023-08-29T15:10:00", "isServiceSession": false, "isPlenumSession": false, "speakers": [ "1546327a-cefa-45a1-add1-be54fd18e47d" ], "categoryItems": [], "questionAnswers": [], "roomId": 34373, "liveUrl": null, "recordingUrl": null, "status": "Accepted", "isInformed": true, "isConfirmed": false }, { "id": "524459", "title": "Brisbane Panel Discussion", "description": "Hear from some of the brightest data engineering minds both locally and internationally for this thrilling panel discussion!", "startsAt": "2023-08-29T15:50:00", "endsAt": "2023-08-29T16:30:00", "isServiceSession": false, "isPlenumSession": false, "speakers": [ "37e3ce13-b8a4-4927-b151-757f5d7de7c6", "4f52399d-37c0-4b2b-8507-4d9fe56f185d", "5b362fba-1112-4b0a-a545-45e48d00992b" ], "categoryItems": [], "questionAnswers": [], "roomId": 34373, "liveUrl": null, "recordingUrl": null, "status": "Accepted", "isInformed": true, "isConfirmed": false }, { "id": "519415", "title": "Getting serious about transformations in the modern data stack", "description": "Data transformations, processing, and modelling serve as the fundamental elements in constructing a data platform for analytics, machine learning, and AI purposes. In this session, learn how Coalesce helps Snowflake customers to implement data engineering best practices and data modelling best practices with the speed of an intuitive graphical user interface (GUI), the flexibility of code, and the efficiency of automation for data transformations.", "startsAt": "2023-08-29T16:30:00", "endsAt": "2023-08-29T17:00:00", "isServiceSession": false, "isPlenumSession": false, "speakers": [ "9a96fe9d-ada6-449c-841d-e90210578384", "a07fa723-435d-47e8-bb53-3f3abc75f765" ], "categoryItems": [], "questionAnswers": [], "roomId": 34373, "liveUrl": null, "recordingUrl": null, "status": "Accepted", "isInformed": true, "isConfirmed": true }, { "id": "524458", "title": "Build your own electric vehicle charging map with PostGIS", "description": "An educational live demo of a map server showing electric vehicle charging stations so you'll never be stuck without a charge again.\r\nYour map will show the available charging stations, which ones you can reach with your current range and the most efficient route to each one.\r\n\r\nCome along and see what you can build with PostGIS and pgRouting in the context of a topical real world use case.\r\nWe will also take a look at some ETL and geocoding examples using PL/Python running natively in Postgres.\r\n\r\nFinally, we will look at why PostGIS is so powerful for both performance and integration - including functions to work with GeoJSON, KML and MVT, built-in 3D and topology support, and advanced spatial indexing enhancements.\r\nThis is a beginner to intermediate session for PostGIS and assumes a rudimentary knowledge of SQL and spatial data.", "startsAt": "2023-08-29T17:00:00", "endsAt": "2023-08-29T17:30:00", "isServiceSession": false, "isPlenumSession": false, "speakers": [ "2155043b-45eb-4f85-8f5c-7a2cb99a96c1" ], "categoryItems": [], "questionAnswers": [], "roomId": 34373, "liveUrl": null, "recordingUrl": null, "status": "Accepted", "isInformed": true, "isConfirmed": false } ], "speakers": [ { "id": "37e3ce13-b8a4-4927-b151-757f5d7de7c6", "firstName": "Chad", "lastName": "Sanderson", "bio": "Chad Sanderson is passionate about data quality, and fixing the muddy relationship between data producers and consumers. 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He’s a business-minded data nerd who’s worked in the data industry for 20 years, with responsibilities ranging from statistical modeling, forecasting, machine learning, data engineering, data architecture, and almost everything else.\r\n\r\nJoe hosts the popular data podcasts The Monday Morning Data Chat and The Joe Reis Show, keynotes major data conferences worldwide and runs several meetups, including The Utah Data Engineering Meetup and SLC Python. In his spare time, he can be found writing new books and articles and teaching at the University of Utah.\r\n\r\nWhen he’s not busy running a company, teaching, or creating content, Joe often finds himself rock climbing or trail running in the mountains around Salt Lake City, Utah.", "tagLine": "Best-selling author, \"recovering data scientist\", data engineer and architect.", "profilePicture": "https://cdn.sessionize.com/image/c493-400o400o1-nyM7xoHWSkEcHJQRShXezx.png", "isTopSpeaker": true, "links": [], "sessions": [ 516894, 524459 ], "fullName": "Joe Reis", "categoryItems": [], "questionAnswers": [] }, { "id": "a07fa723-435d-47e8-bb53-3f3abc75f765", "firstName": "John", "lastName": "Cosgrove", "bio": "I believe that there is only one way to truly change the planet with data: put it in the hands of the people.\r\n\r\nI've built beautiful predictive models and complex data structures that have advised CEOs and Cabinet Ministers. I've modelled entire markets and forecasted entire sectors. But do you know what achieved the greatest genuine change of all my projects in the last decade?\r\n\r\nMaking graphs for tradespeople. They didn't decide the fate of nations... but they did change their behaviour today because of what the data could tell them about tomorrow. \r\n\r\nIvory towers are beautiful. But grass roots are powerful.\r\n\r\nMy experience spans a range of topics and sectors, including higher education and training, public policy, construction, property, mining and energy, information technology, retail, economics and social media.\r\n\r\nI enjoy managing collaborative, high performance teams, where the continuous development of my staff is the highest priority.\r\n\r\nI am an experienced public speaker, presenter and group facilitator. 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She has experience in machine learning and did her university thesis in computer vision and image classification.\r\n\r\nLarene is part of the [DatavizA11y group](https://github.com/dataviza11y/Why-We-Exist), formed in 2020. 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With a proven capability to work independently and in teams, lead and mentor co-workers, and communicate with both technical and non-technical stakeholders, I consistently deliver value to a variety of clients and projects.I'm an experienced data scientist and software engineer with a deep background in computer science, programming, machine learning, and statistics. My work spans the full spectrum from solving isolated data problems to building production systems that serve millions of users. 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