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3 Must Reads On The Modern Data Stack

3 Must Reads On The Modern Data Stack

Our Pick Of The Week
Juan Martin Pampliega

Posted by Juan Martin Pampliega

on December 12, 2022 · 2 mins read

For today’s insights recommendation post, we gathered a selection of three reading materials that will help you understand the Modern Data Stack and the role machine learning plays in them.

#1: Emerging Architectures for Modern Data Infrastructure

Making sense of what goes on in the constantly evolving world of data can be challenging. The network the Andreessen Horowitz team has built makes them uniquely positioned to survey the market.

This post offers an interesting take on how backend and frontend developers are adapting to platform-like data stacks leading to a new emerging data stack. Read on to see what this could look like, and what implications it has for data apps and infrastructures. This post is authored by Matt Bornstein, Jennifer Li, and Martin Casado.

#2: The Fundamentals Of Data Engineering

Joe Reis and Matt Housley deliver a walkthrough of the data engineering lifecycle demonstrating how to use different cloud technologies together, to serve a variety of data needs. The book covers concepts that are key to the Modern Data Stack, such as generation, ingestion, orchestration, transformation, storage, and data governance.

#3: How To Build A Modern Data Stack

Julia Wrzosinska from our technological partner, Astronomer, breaks down what a modern data stack means in practice. Discussing four core components, five reasons to set it up, and how to orchestrate it.

Astronomer offers, without a doubt, the best Apache Airflow based platform in the market. Having a solid foundation to build upon ensures that the data systems we create can achieve next-level quality, scalability, and stability. This, paired with their awesome team, their focus on growing the Airflow community, and their constant quest for improvement makes them the perfect complementary partner.

Bonus Track!: Meet The Modern Data Stack Whitepaper

Why should you implement a Modern Data Stack? What pain points does it solve? What are its main benefits? How do you get started? What questions should you be asking when looking to evolve your current stack? All this and much more!

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