• Are you useful?

  • 2024/01/07
  • 再生時間: 29 分
  • ポッドキャスト

  • サマリー

  • The final installment of the metadata trilogy. Yes, a secret trilogy of system monitoring! We share why engineers need to work closely with their colleagues in product, data science, design, marketing, support, and finance, because today... every company is a tech company.



    Recommended Resources

    Data Engineering

    • Apache Spark for MapReduce operations on Big Data https://spark.apache.org/
    • Apache Kafka for data streaming https://kafka.apache.org/
    • Amazon Kinesis for data streaming in AWS https://aws.amazon.com/kinesis/


    Product Management Concepts

    • AARRR aka Dave McClure's Pirate Metrics https://fourweekmba.com/pirate-metrics/
    • Diffusion of Innovations https://en.m.wikipedia.org/wiki/Diffusion_of_innovations


    Analytics

    • Adobe Analytics https://business.adobe.com/products/analytics/adobe-analytics.html
    • Google Marketing Platform (formerly Google Analytics) https://marketingplatform.google.com/about/
    • Mixpanel https://mixpanel.com/
    • Amplitude https://amplitude.com/


    BI (Business Intelligence)

    • Microsoft Power BI https://www.microsoft.com/en-gb/power-platform/products/power-bi
    • Tableau https://www.tableau.com/
    • Sisense https://www.sisense.com/
    • Snowflake https://www.snowflake.com/


    Columnar data stores (OLAP, not OLTP)

    • BigQuery https://cloud.google.com/bigquery
    • Redshift https://aws.amazon.com/redshift/


    A/B Testing

    • LaunchDarkly https://launchdarkly.com/
    • Optimizely https://www.optimizely.com/


    Recommended Books
    • Inspired by Marty Cagan | Best practices and strategies for creating successful tech products. Similar in goal and approach to this podcast, but from the product point of view, rather than engineering
    • The Lean Startup by Eric Ries | Creating a new tech product under conditions of extreme uncertainty, by "validated learning"


    Links to items mentioned in the episode
    • Data Science at the Command Line by Janssens | A good overview of some of the janitorial side of Data Science
    • An example of a paid marketing strategy, which could be measured using pirate metrics (AARRR). A family video clip, purchased by a company, for social media brand viral marketing https://twitter.com/Skyscanner/status/852889390661726208


    Final aside
    • If like me, you hate the idea of every single web interaction you perform being monitored for the purposes of creating better marketing profiles, to maximise cash extraction... you can take small steps to push back. Run Pi-hole as your local DNS proxy https://pi-hole.net/



    Thanks for listening to the Engineering Culture podcast. We have no date in place for when more episodes will be available, but if you'd like to get in touch, please drop us an email to ec@lifebeyondfife.com


    Hosted on Acast. See acast.com/privacy for more information.

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あらすじ・解説

The final installment of the metadata trilogy. Yes, a secret trilogy of system monitoring! We share why engineers need to work closely with their colleagues in product, data science, design, marketing, support, and finance, because today... every company is a tech company.



Recommended Resources

Data Engineering

  • Apache Spark for MapReduce operations on Big Data https://spark.apache.org/
  • Apache Kafka for data streaming https://kafka.apache.org/
  • Amazon Kinesis for data streaming in AWS https://aws.amazon.com/kinesis/


Product Management Concepts

  • AARRR aka Dave McClure's Pirate Metrics https://fourweekmba.com/pirate-metrics/
  • Diffusion of Innovations https://en.m.wikipedia.org/wiki/Diffusion_of_innovations


Analytics

  • Adobe Analytics https://business.adobe.com/products/analytics/adobe-analytics.html
  • Google Marketing Platform (formerly Google Analytics) https://marketingplatform.google.com/about/
  • Mixpanel https://mixpanel.com/
  • Amplitude https://amplitude.com/


BI (Business Intelligence)

  • Microsoft Power BI https://www.microsoft.com/en-gb/power-platform/products/power-bi
  • Tableau https://www.tableau.com/
  • Sisense https://www.sisense.com/
  • Snowflake https://www.snowflake.com/


Columnar data stores (OLAP, not OLTP)

  • BigQuery https://cloud.google.com/bigquery
  • Redshift https://aws.amazon.com/redshift/


A/B Testing

  • LaunchDarkly https://launchdarkly.com/
  • Optimizely https://www.optimizely.com/


Recommended Books
  • Inspired by Marty Cagan | Best practices and strategies for creating successful tech products. Similar in goal and approach to this podcast, but from the product point of view, rather than engineering
  • The Lean Startup by Eric Ries | Creating a new tech product under conditions of extreme uncertainty, by "validated learning"


Links to items mentioned in the episode
  • Data Science at the Command Line by Janssens | A good overview of some of the janitorial side of Data Science
  • An example of a paid marketing strategy, which could be measured using pirate metrics (AARRR). A family video clip, purchased by a company, for social media brand viral marketing https://twitter.com/Skyscanner/status/852889390661726208


Final aside
  • If like me, you hate the idea of every single web interaction you perform being monitored for the purposes of creating better marketing profiles, to maximise cash extraction... you can take small steps to push back. Run Pi-hole as your local DNS proxy https://pi-hole.net/



Thanks for listening to the Engineering Culture podcast. We have no date in place for when more episodes will be available, but if you'd like to get in touch, please drop us an email to ec@lifebeyondfife.com


Hosted on Acast. See acast.com/privacy for more information.

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