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Data Integrity for AI: What’s Old is New Again

Precisely

Disclaimer: Throughout this post, I discuss a variety of complex technologies but avoid trying to explain how these technologies work. The goal of this post is to understand how data integrity best practices have been embraced time and time again, no matter the technology underpinning. Then came Big Data and Hadoop!

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Is Apache Iceberg the New Hadoop? Navigating the Complexities of Modern Data Lakehouses

Data Engineering Weekly

The modern data stack constantly evolves, with new technologies promising to solve age-old problems like scalability, cost, and data silos. But is it truly revolutionary, or is it destined to repeat the pitfalls of past solutions like Hadoop? Speed: Accelerating data insights.

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Big Data Technologies that Everyone Should Know in 2024

Knowledge Hut

Big data in information technology is used to improve operations, provide better customer service, develop customized marketing campaigns, and take other actions to increase revenue and profits. In the world of technology, things are always changing. It is especially true in the world of big data.

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Hadoop vs Spark: Main Big Data Tools Explained

AltexSoft

Hadoop and Spark are the two most popular platforms for Big Data processing. They both enable you to deal with huge collections of data no matter its format — from Excel tables to user feedback on websites to images and video files. What are its limitations and how do the Hadoop ecosystem address them? What is Hadoop.

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Stitching Together Enterprise Analytics With Microsoft Fabric

Data Engineering Podcast

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. Data lakes in various forms have been gaining significant popularity as a unified interface to an organization's analytics. Closing Announcements Thank you for listening!

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Reflecting On The Past 6 Years Of Data Engineering

Data Engineering Podcast

Summary This podcast started almost exactly six years ago, and the technology landscape was much different than it is now. In that time there have been a number of generational shifts in how data engineering is done. Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?

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Ship Smarter Not Harder With Declarative And Collaborative Data Orchestration On Dagster+

Data Engineering Podcast

In this episode Pete Hunt, CEO of Dagster labs, outlines these new capabilities, how they reduce the burden on data teams, and the increased collaboration that they enable across teams and business units. Can you describe what the focus of Dagster+ is and the story behind it? What problems are you trying to solve with Dagster+?

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