Christen Marshall
Senior Solutions Engineer, Healthcare at Snowflake
Kansas City, Missouri, United States
Actions
Christen is an experienced data management specialist with over 10 years of expertise as a data analyst, engineer, and architect, and Snowflake is the only thing she has not broken (yet). Passionate about innovation and process improvement, Christen has successfully modernized data platforms, implemented new technologies, and identified major cost savings at companies in Healthcare and Oil and Gas. She loves sharing knowledge with others and enabling teams to choose optimal solutions. Currently a Senior Solutions Engineer, Christen helps customers build innovative, cost effective solutions to business challenges in Healthcare using the Snowflake Data Cloud.
Links
Area of Expertise
Topics
Building Data Applications in Snowflake
In this presentation, we will discuss the process of building data applications on Snowflake's data cloud. The presentation will go through how to build, deploy, and host a python application in Snowflake.
Ps & Qs: Privacy Preserving Quandries
Have you been tasked with protecting your data but don't know where to start? This session will review privacy preserving technologies and methods such as masking, tokenization, row and column access policies, and differential privacy. Guidance will also be given on when to use each method and their pros and cons.
DevOps for DEs
Regardless of if you use a no/low code ELT tool like DBT, Fivetran, or Informatica, or develop in Spark , DevOps practices are a necessary skill for data engineers to operate with the same scale and effectiveness as their software engineer peers. This session reviews the 8 continuous delivery capabilities as suggested by Accelerate (Forsgren, Humble, and Kim) and suggests ways data engineers can incorporate them into their work.
What is a Modern Data Architecture?
In order to move forward, we need to stop thinking about data in terms of existing types of systems, such as legacy data warehouses, data marts, and data lakes. Doing that is not helpful and it introduces an unnatural and artificial boundary in an enterprise data landscape.
This presentation goes through the history of these architectures and suggestions for how to think about data differently.
Goodbye, ChatGPT: Build better chatbots with retrieval augmented generation
Models like GPT are excellent at answering general questions from public data sources but aren't perfect. Accuracy takes a nosedive when you need to access domain expertise, recent data, or proprietary data sources.
This is where retrieval augmented generation (RAG) comes in – by enhancing your LLM with custom data sources, you can build a chatbot with only Python that combines the smarts of GPT (or your model of choice) with the specificity of your own data.
SQL Synthesis: Query and Visualize Your Data Using Python, Streamlit, and Language Models
Leveraging Python and LLMs to generate and execute SQL has significant practical benefits — suddenly, data becomes accessible to people who aren’t SQL experts. Language models offer Python developers the opportunity to interact with their data without dealing with the headache of learning SQL.
This talk walks through the process of using Python to generate SQL via language models and to execute that generated SQL. Streamlit is used to collect user input, display the results of the executed queries, and visualize those results in a live demo.
Christen Marshall
Senior Solutions Engineer, Healthcare at Snowflake
Kansas City, Missouri, United States
Links
Actions
Please note that Sessionize is not responsible for the accuracy or validity of the data provided by speakers. If you suspect this profile to be fake or spam, please let us know.
Jump to top