Modern Data Stack

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šŸŽ Getting Up To Speed In The Data Ecosystem
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šŸŽ Getting Up To Speed In The Data Ecosystem

A journey in becoming an expert in the Modern Data Stack

Andrew Ermogenous
Dec 6, 2020
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Share this post
šŸŽ Getting Up To Speed In The Data Ecosystem
moderndatastack.substack.com

I recently joined Atlan, a DataOps platform, and have been trying to get up to speed in the Data & Analytics stack over the past 30 days. While I’ve spent my entire career in Data & AI businesses, there is still so much to learn. I’ve read through countless blogs, market maps, analyst reports, product sheets, and ā€œFor Dummiesā€ Guides.

What was most obvious throughout this process, though?

There’s no go-to newsletter on the Modern Data Stack.

I thought I’d share some of the best writing I found along the way. Here’s my attempt!


Fundamentals

This post by A16z provides perhaps the best explanation I’ve read on the emerging Modern Data architecture.

Matt Turk from FirstMark also has a fantastic annual Data & AI market map that he puts out.

And this blog from Census crystallizes the concept of the Modern Data Stack.


Nuggets from the ecosystem:

A topical Twitter debate on ā€œthe best analytics stackā€ Favorite comment from Huy Nguyen, the Founder of Holistics.io, a data modeling platform:

"Attributes of a good analytics stack:
* ELT over ETL
* Cloud DW over on-premise DW/Hadoop systems.
* Data modeling is essential
* SQL based over non-SQL based analytics.
* Analytics workflow is more important than singular focus on visualizations"

The Data Crunch Podcast episode with Ben Jones, the CEO of Data Literacy, on common data pitfalls, learning strategies, and unique stories about both epic failures and great successes using data in the real world.

Cloud Irregular Newsletter: AWS plays the multi-cloud game?

A Must Subscribe: Ed’ Sim’s ā€œWhat’s šŸ”„ in Enterprise IT/VCā€. Here’s a link to this week’s edition.

A great Forbes post by Jim Walker of Cockroach Labs on How to Beat Latency as the Cloud Grows


I’ll leave you all with something from Scientific American on How the Best Forecasters Predict

"Rather than doing a 180, those who excel at making accurate predictionsĀ tend to change their beliefs gradually. They revise their predictions to reflect new information, but they do so slowly, comparing it with the information they had before."


Thanks for reading! Shoot me a note with any feedback: andrew@atlan.com

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šŸŽ Getting Up To Speed In The Data Ecosystem
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