Data Engineering

All things data engineering & science newsletter #7

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(if this newsletter was forwarded to you then you can subscribe here: https://insightextractor.com/)

The goal of this newsletter is to promote continuous learning for data science and engineering professionals. To achieve this goal, I’ll be sharing articles across various sources that I found interesting. The following 5 articles made the cut for today’s newsletter.

1. Why a data scientist is not a data engineer?

Good post on the difference between data engineer and data scientist and why you need both roles in a data team. I chuckled when one of the sections had explanations around why data engineering != spark since I completely agree that these roles should be boxed around just one or two tools! read the full post here

2. Correlation vs Causation:

1 picture = 1000 words!

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3. Best Practices from Facebook’s growth team:

Read Chamath Palihapitiya and Andy John’s response to this Quora question here

4. Simple mental model for handling for handling “big data” workloads
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5. Five things to do as a data scientist in firt 90 days that will have big impact.

Eric Weber gives 5 tips on what to do as a new data scientist to have a big impact. Read here

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Thanks for reading! Now it’s your turn: Which article did you love the most and why?

Data Engineering and Data Science Newsletter #6

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The goal of this Insight Extractor’s newsletter is to promote continuous learning for data science and engineering professionals. To achieve this goal, I’ll be sharing articles across various sources that I found interesting. The following 5 articles made the cut for today’s newsletter.

1. How do you measure Word of mouth for growth analytics?

Some really good research and methodologies on how to measure word of the growth analytics? Read here

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2. Lean data science

really good insights like “measure business performance and not model performance” with the end goal of delivering business value instead of focusing too much on the algorithm. Read here

3. Good data storytelling: Emoji use in the new normal

Read this to get inspired about to tell stories through data, really well done! Go here

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4. Why is Data engineering important?

Good post that explains important of data engineering here

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5. Five things you should know about Data engineering career

This is a good post to read along with reading about the importance of Data engineers above. Both of these articles give you a good mental model to explain the role and assess if this the right fit for you if you are considering this career track. Read here

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Thanks for reading! Now it’s your turn: Which article did you love the most and why?

Data Engineering and Data Science Newsletter #4

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The purpose of this Insight Extractor’s newsletter is to promote continuous learning for data science and engineering professionals. To achieve this goal, I’ll be sharing articles across various sources that I found interesting. The following articles made the cut for today’s newsletter.

1. What does a Business Intelligence Engineer (BIE) do in Amazon?

Have you wondered what Analytics professionals at Top tech companies work on? Are you job hunting and wondering what data roles (data engineer, data science, or Bi engineer) at Amazon are a great fit for your profile? If so, read Jamie Zhang’s (Sr Business Intelligence Engineer at Amazon) post here

2. What are the 2 Data & Analytics Maturity models that you should absolutely know about?

If you have read my blog, you know that I am a fan of mental models. So, here are 2 mental models (frameworks) shared by Greg Coquillo that are worth reading/digesting here

3. Using Machine Learning to Predict Value of Homes On Airbnb

Really good case study by Airbnb Data scientist Robert Chang here

4. How Netflix measures product succes?

Really good post on how to define metrics to prove or disprove your hypotheses and measure progress in a quick and simple manner. To do this, the author, Gibson Biddle, shares a mechanism of proxy metrics and it’s a really good approach. You can read the post here

Once you read the post above, also suggest learning about leading vs lagging indicators. It’s a similar approach and something that all data teams should strive to build for their customers.

5. Leading vs lagging indicators

Kieran Flanagan and Brian Balfour talk about why your north star metric should be a leading indicator and if it’s not then how to think about it. Read about it here

Thanks for reading! Now it’s your turn: Which article did you love the most and why?

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Data Maturity Mental Model Screenshot:

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INSIGHT EXTRACTOR’S DATA ENGINEERING AND SCIENCE NEWSLETTER #3

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The purpose of this newsletter is to promote continuous learning for data science and engineering professionals. To achieve this goal, I’ll be sharing articles across various sources that I found interesting. Following articles made the cut for today’s newsletter:

1.What I love about Scrum for Data Sciene.

I love the Scrum mechanism for all data roles: data engineering, data analytics and data science. The author (Eugene) shares his perspective based on his experiences. I love that the below quote from the blog and you can read the full post here

Better to move in the right direction, albeit slower, than fast on the wrong path.

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2. Building Analytics at 500px:

One of the best article on end to end anayltics journey at a startup by Samson Hu. Must read! Go here (Note that the analytics architectures have changed since this post which was published in 2015 but read through the mental model instead of exact tech tools that were mentioned in the post)

3. GO-FAST: The Data Behind Ramadan:

A great example of data storytelling from Go-Jek BI team lead Crysal Widjaja. Read here

4. Why Robinhood uses Airflow:

Airflow is a popular data engineering tool out there and this post provides really good context on it’s benefits and how it stacks up against other tools. Read here

5. Are dashboards dead?

Every new presentation layer format in the data field can lead to experts questioning the value of dashboards. With the rise of Jupyter notebooks, most vendors have now added the “notebooks” functionality and with that comes the follow-up question on if dashboards are dead? Here’s one such article. Read here

I am not still personally convinced that dashboards are “dead” but it should complement other presentation formats that are out there. The post does have good points against dashboards (e.g data is going portrait mode) and you should be aware about those to ensure that you are picking the right format for your customers. The author is also biased since they work for a data vendor that is betting big on notebooks and so you might want to account for that bias while reading this. Also, I had written about “Are dashboards dead?” in context of chat-bots in 2016 and that hypothesis turned out to be true; you can read that here

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Thanks for reading! Now it’s your turn: Which article did you love the most and why?