Tue.Jun 07, 2022

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An In-Depth Data Mesh Discussion with Zhamak Dehghani

Jesse Anderson

In 2021 I had the pleasure to first get to know and speak with Zhamak Dheghani, Director of Emerging Technologies at ThoughtWorks, in season one of the Data Dream Team series. Zhamak is a software engineer and architect who is (in)famously known as the founder of the data mesh concept, a paradigm shift in how we manage data-driven value at scale. I interviewed Zhamak last season as more of an introduction to Data Mesh.

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Top Posts May 30 – June 5: 21 Cheat Sheets for Data Science Interviews

KDnuggets

Also: Decision Tree Algorithm, Explained; How to Become a Machine Learning Engineer; The Complete Collection of Data Science Books – Part 2; 15 Python Coding Interview Questions You Must Know For Data Science.

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The Future Is Hybrid Data, Embrace It

Cloudera

We live in a hybrid data world. In the past decade, the amount of structured data created, captured, copied, and consumed globally has grown from less than 1 ZB in 2011 to nearly 14 ZB in 2020. Impressive, but dwarfed by the amount of unstructured data, cloud data, and machine data – another 50 ZB. In fact, the total amount of data is expected to nearly triple by 2025.

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3 Ways Understanding Bayes Theorem Will Improve Your Data Science

KDnuggets

Mastery of this intuitive statistical concept will advance your credibility as a decision-maker.

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Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know

Speaker: Timothy Chan, PhD., Head of Data Science

Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.

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How to Elastically Scale Apache Kafka Clusters on Confluent Cloud

Confluent

How to elastically scale Kafka clusters from 0 to 100 MB/s and back with automatic cluster resizing, data rebalancing, real-time consumption optimization, and monitoring in seconds.

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Data Science is Overrated, Here’s Why

KDnuggets

Think twice before jumping on the data science bandwagon.

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Building An External Data Product Is Different. Trust Me. (but read this anyway)

Monte Carlo

The data world moves unapologetically fast. It seems like just last year we started talking about how data teams were transitioning from providing a service, to treating data like a product or even building internal products across a decentralized data mesh architecture. Wait, that was *checks notes* January of this year?? Wow. Who knows, maybe Ferris Bueller became a data engineer.

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MongoDB vs DynamoDB Head-to-Head: Which Should You Choose?

Rockset

Note: We have updated this post to reflect comments and corrections we received from readers. We thank those who sent in comments for helping us make this post more accurate and useful. — Editor Databases are a key architectural component of many applications and services. Traditionally, organizations have chosen relational databases like SQL Server, Oracle , MySQL and Postgres.

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