□ The fellow speakers from Google and Meta shed light on the importance of networking and personal branding in today's fast-paced tech world. The audience's response was overwhelmingly positive, and it was fantastic to see how open-minded and forward-thinking the students and faculty at CSULB are. □ My presentation focused on using ChatGPT as a game-changing tool for learning code faster, acing tech interviews, and even creating unique teaching experiences, like having Master Chief instruct through text-to-speech AI □. What an incredible day it was, speaking at the California State University, Long Beach's MSIS Big Tech Panel! I am still buzzing with excitement, and I want to share my experience with all of you! □įirst off, a big THANK YOU Mohamed Abdelhamid, PhD for inviting me and to the organizers of the event, who brought together such a diverse group of speakers, including other analysts from #Google ( Manu Mehra) and #Meta ( Vertika Srivastava)! It was a privilege to share the stage with these industry veterans. □ So Grateful for the Amazing Experience at CSULB's Big Tech Panel! □ #dataengineering #dataanalytics #datascience #dbt We shared our moments on how we both came from non-computer science backgrounds and making it happen in tech! Plus she’s now part of a $4.2b company, is employee #2, and is just crushing it and inspiring my mentees that also made it to event Jason & Peter □□Īfter this event I am sure I want to start hosting local LA events, so be on the lookout in June’ish for my data meetup □ Talking with Erica aka Ric was like a breath of fresh air. Not only did some really deep in the industry people like Nova Wang at StubHub or Tao Yang at HBO Max, but I also got to meet to woman behind the data team at dbt Labs Erica Louie! Excel really is the enemy and explaining this engineering stuff is a common hurdle among data pros. It’s wierd through all these stories I finally felt seen. I met analytics team of 1’s and other teams of 10+ and the various challenges they faced. Stoked to have been part of dbt Labs first community event here is Los Angeles! □□□Įven though I work in #AnalyticsEngineering I have never been in a room of this many people passionate about just this one topic. I thought the challenge would keep them busy for two or three years. (I picked AP Bio because the test is more than a simple regurgitation of scientific facts–it asks you to think critically about biology.) If you can do that, I said, then you’ll have made a true breakthrough. Make it capable of answering questions that it hasn’t been specifically trained for. In mid-2022, I was so excited about their work that I gave them a challenge: train an artificial intelligence to pass an Advanced Placement biology exam. I’d been meeting with the team from OpenAI since 2016 and was impressed by their steady progress. The second big surprise came just last year. The first time was in 1980, when I was introduced to a graphical user interface-the forerunner of every modern operating system, including Windows. In my lifetime, I’ve seen two demonstrations of technology that struck me as revolutionary. LLMs will become an amazing developer tool that radically improves productivity and data modeling speed but nothing that kills your job. A data engineer will ALWAYS be required in order to understand what the data means, how it should be structured, and mapping meaning to an expected outcome - and THAT is the most important part of the job. By describing a set of keys and their intended relationship Chat GPT will be able to propose aggregations and optimize the query to provide an intended result. What ChatGPT can and will do, is (eventually) make the work of data modeling far simpler. That's not machine learning anymore - it's general intelligence. To do that, the algorithm must have some cognition of the real world, grok how the business works, and dynamically tie the data model to that understanding. All three IDs and their properties are slightly (or significantly) different and must be integrated into a single table in the Data Warehouse.Īs smart as ChatGPT might be, it would need to understand the SEMANTICS of how these IDs coalesce into something meaningful in order to automate any step of modeling or ETL. Another could be imported from Mixpanel as nested JSON, and a third might be collected from a CDP. One customerID could be stored in a MySQL DB. Yes, it can write SQL, but the hard part of data development is understanding how code translates to the real world.Įvery business has a unique way of storing data.
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