25 episodes

Discussions around Data Engineering

The Engineering Side of Data Bob Haffner

    • Technology
    • 5.0 • 15 Ratings

Discussions around Data Engineering

    2023 re:Invent Data Recap with Alex DeBrie

    2023 re:Invent Data Recap with Alex DeBrie

    Alex Debrie and Bob Haffner recap their favorite announcements from 2023 re:Invent



    #data #dataengineering #aws



    Connect with Alex
    Twitter: @alexbdebrie
    Blog: alexdebrie.com
    Book: dynamodbbook.com
    Podcast: youtube.com/@SoftwareHuddle



    Connect with Bob
    Twitter - @bobhaffner
    LinkedIn - linkedin.com/in/bobhaffner



    Alex’s talk
    https://www.youtube.com/watch?v=PVUofrFiS_A

    • 39 min
    Data Quality with JP Urrutia

    Data Quality with JP Urrutia

    Juan Pablo (JP) Urrutia and Bob Haffner discuss Data Quality

    #data #dataengineering #dataquality



    Connect with JP

    Twitter - @the_datachef
    Linkedin - https://www.linkedin.com/in/jpurrutia

    Substack - https://substack.com/@thedatachef


    Connect with Bob

    Twitter - @bobhaffner

    LinkedIn - https://www.linkedin.com/in/bobhaffner



    Follow the show on Twitter @EngSideOfData



    Data quality camp slack community:https://join.slack.com/t/dataqualitycamp/shared_invite/zt-2452i21wm-iZjqABfR7Hr8gcsshYuh2A



    Data Quality Fundamentals Book

    https://www.oreilly.com/library/view/data-quality-fundamentals/9781098112035/

    • 51 min
    2023 Snowflake Summit Recap with Roy Hasson

    2023 Snowflake Summit Recap with Roy Hasson

    Roy Hasson and Bob Haffner recap the 2023 Snowflake Summit.

    #data #dataengineering #snowflake



     Connect with Roy

     Linkedin - https://www.linkedin.com/in/royhasson/



    Connect with Bob 

    Twitter - @bobhaffner 

    LinkedIn - https://www.linkedin.com/in/bobhaffner 



    Follow the show on Twitter 

    @EngSideOfData

    • 50 min
    Starting a Career in Data Engineering with Arjun Bansil

    Starting a Career in Data Engineering with Arjun Bansil

    Arjun Bansil and Bob Haffner chat about Ajun's start in Data Engineering

    #data #dataengineering

    Connect with Arjun
    Linkedin - https://www.linkedin.com/in/arjun-bansil/

    Connect with Bob
    Twitter - @bobhaffner
    LinkedIn - https://www.linkedin.com/in/bobhaffner



    Follow the show on Twitter

    @EngSideOfData

    • 28 min
    Developer Ergonomics with Joseph Machado

    Developer Ergonomics with Joseph Machado

    Joseph Machado and Bob Haffner discuss the many aspects of developer ergonomics and how they relate to data engineering.  



    #data #dataengineering 



    Connect with Joseph 

    Blog - https://www.startdataengineering.com/

    Linkedin - https://www.linkedin.com/in/josephmachado1991/ 



    Connect with Bob

    Twitter - @bobhaffner

    LinkedIn - https://www.linkedin.com/in/bobhaffner

    • 31 min
    2022 re:Invent Data Recap with Alex DeBrie

    2022 re:Invent Data Recap with Alex DeBrie

    Alex Debrie and Bob Haffner recap their favorite announcements from 2022 re:Invent  



    #data #dataengineering #aws   



    Connect with Alex

    Twitter: @alexbdebrie 

    Blog: alexdebrie.com 

    Book: dynamodbbook.com  



    Connect with Bob 

    Twitter - @bobhaffner 

    LinkedIn - linkedin.com/in/bobhaffner   



    Top Announcements 

    https://aws.amazon.com/blogs/aws/top-announcements-of-aws-reinvent-2022/  



    Yan Cui’s Choreography vs Orchestration blog post 

    https://theburningmonk.com/2020/08/choreography-vs-orchestration-in-the-land-of-serverless/

    • 45 min

Customer Reviews

5.0 out of 5
15 Ratings

15 Ratings

uber_gadgetz ,

Great Podcast On the Critical Field of Data Engineering

Personally, I’m a machine learning enthusiast. I get great satisfaction out of building models that can make good predictions in a particular problem space. However, models are just mappings of inputs to outputs that are derived from data…they’re mathematical representations of the data. So, I’m the first to acknowledge the fundamentally critical role that good data engineering plays in the success or failure of my machine learning endeavors. No amount of model architecture tweaking can ever make up for training data that inadequately represents the problem space. Trying to use data that’s inappropriate for the problem to be modeled, is analogous to pouring old stale gasoline into a rocket engine…you’re not going to get very far.

So, I’m very happy to have found this podcast that casts a proper spotlight on the absolutely critical role of data engineering - the rocket fuel that powers all successful ML/AI endeavors!

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