Remove Data Management Remove Hadoop Remove Unstructured Data
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Data Integrity for AI: What’s Old is New Again

Precisely

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. In the beginning, there was a data warehouse The data warehouse (DW) was an approach to data architecture and structured data management that really hit its stride in the early 1990s.

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

Data Engineering Weekly

But is it truly revolutionary, or is it destined to repeat the pitfalls of past solutions like Hadoop? In a recent episode of the Data Engineering Weekly podcast, we delved into this question with Daniel Palma, Head of Marketing at Estuary and a seasoned data engineer with over a decade of experience.

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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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What is an AI Data Engineer? 4 Important Skills, Responsibilities, & Tools

Monte Carlo

Key Differences Between AI Data Engineers and Traditional Data Engineers While traditional data engineers and AI data engineers have similar responsibilities, they ultimately differ in where they focus their efforts. Challenges Faced by AI Data Engineers Just because “AI” involved doesn’t mean all the challenges go away!

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Unstructured Data: Examples, Tools, Techniques, and Best Practices

AltexSoft

In today’s data-driven world, organizations amass vast amounts of information that can unlock significant insights and inform decision-making. A staggering 80 percent of this digital treasure trove is unstructured data, which lacks a pre-defined format or organization. What is unstructured data?

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Why Open Table Format Architecture is Essential for Modern Data Systems

phData: Data Engineering

Track data files within the table along with their column statistics. Open table formats enable efficient data management and retrieval by storing these files chronologically, with a history of DDL and DML actions and an index of data file locations. Log all Inserts, Updates, and Deletes (DML) applied to the table.

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5 Reasons Why ETL Professionals Should Learn Hadoop

ProjectPro

Hadoop’s significance in data warehousing is progressing rapidly as a transitory platform for extract, transform, and load (ETL) processing. Mention about ETL and eyes glaze over Hadoop as a logical platform for data preparation and transformation as it allows them to manage huge volume, variety, and velocity of data flawlessly.

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