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Thought Leadership

Thought Leadership

4 New Year Resolutions for Data Engineering Leaders and How You Can Achieve Them

Michal Wosk
December 11, 2022

Introduction As 2022 is about to end, many engineering leaders use this time to reflect on the year that has passed and start planning ahead on the year upcoming. Data engineering teams are usually swamped with tasks and requirements, which are many times also accompanied by failures and issues that require immediate attention. Therefore using …

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Data Engineering Thought Leadership

4 Ways to Reduce Cloud Data Storage Costs

Oz Katz
November 7, 2022

In the past year, words like recession, business slowdown and monetary cuttings are being heard more and more often. Not just in the economic press and in the media, these discussions are very much heard also in almost all companies – within boardrooms, in management meetings and when engaging with potential investors and customers. As …

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Data Engineering Thought Leadership

Top Data Engineering Conferences & Events You Don’t Want to Miss in the Fall of 2022!

The lakeFS team
November 7, 2022

As the Covid-19 pandemic loosens its grip on the world, we’re all eager to start travelling and meeting in person again. The great news is that in persone conferences are back, so it’s time to make up for lost time! Attending conferences is a great way to learn, network, and engage with like-minded people.  Not …

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Data Engineering Thought Leadership

Data Manageability: The revolution that is turning Data Trust into the New North Star

Einat Orr, PhD.
November 21, 2022

A few weeks ago, I was looking at a dashboard in our internal BI system. It’s a simple system. Redash over PostgreSQL that has just a few hundreds of thousands of rows.  I noticed a change in one of my favourite metrics that calculates the number of new installations since the beginning of the quarter. …

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Data Engineering Thought Leadership

Data Mesh: What is it and What Does it Mean for Data Engineers?

The lakeFS team
November 7, 2022

Organizations have practically always needed data analytics, and they jumped on the analytics bandwagon as soon as the first computers appeared on the scene. In the 80s, businesses built data warehouses using relational databases as their decision-support systems (DSS). However, as companies generated more diverse data at high velocity, relational databases showed their limitations.  This …

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Data Engineering Thought Leadership

Data versioning as your ‘Get out of jail’ card – DVC vs. Git-LFS vs. dolt vs. lakeFS

Einat Orr, PhD.
February 14, 2023

Data Versioning at Scale: Solutions Overview Back when I was a 23-year-old student, I worked at an Israeli networking company as a BI analyst in the Operations department. My job revolved around modeling the company’s inventory which was quite costly and needed optimization.  At some point, I attended a meeting of the company’s management. When …

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